Clinical trial NCT04656691
Conditions: Covid19
Keywords: COVID19 bamlanivimab UnitedHealth
Sponsor: Daniel Griffin
Start date: 2021-01-04; Completion date: 2021-04-18
Enrollment: 139 participants; Allocation: NA; Intervention model: SINGLE_GROUP

Brief summary:
This is an open-label, pragmatic, single-dose study using matched controls in participants with mild to moderate COVID-19. Participants will track for developing symptoms while at home and upon reporting of symptoms will test for COVID-19. If positive for COVID-19, a one-time at-home infusion of Bamlanivimab (LY3819253) will be provided by Optum Infusion. Participants will then track for 28 days to assess for any additional medical care needed or if hospitalization was required.

Detailed description:

Arms: arm 1: Participants testing positive for COVID-19 may be eligible to receive a one-time dose of bamlanivimab 700 mg, delivered via infusion through the vein, lasting around 60 minutes. This infusion will be done in-home and administered by a registered nurse.
Interventions: intervention 1: bamlanivimab
Intervention descriptions: intervention 1: Participants that test positive for COVID-19 may be eligible to receive an in-home infusion of bamlanivimab by a registered nurse. This infusion will last approximately 1 hour followed by 1 hour of observation.

Protocol documents:
Prot_002.pdf: 
 
 
 
1 
 
 
For clinicaltrials.gov submission: 
 
Full Title:  
At-Home Infusion using Bamlanivimab in Participants with Mild to Moderate COVID-19 symptoms    
Project UNITED   
 
NCT: NCT04656691 
 
Date of Document: 2/3/2021 (uploaded on 3/11/2022)  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 


 
 
 
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PROTOCOL FACESHEET  
Protocol Number  
2020-0081  
Principal 
Investigator  
Daniel Griffin, MD, PHD  
Title  
(Please include Initiative# 
as prefix)  
At-Home Infusion using Bamlanivimab in Participants with Mild to 
Moderate COVID-19 symptoms    
Project UNITED   
 
Type of Research 
Description  
Open-Label, Pragmatic, Observational Trial    
IND 
153935 
Funding Sponsor  
Eli Lilly and Company  
UHG R&D  
Device Vendor   
(if applicable)  
Let’s Get Checked  
Everly Well  
UHG Research Sites  Select Optum Infusion Sites – Sites dictate at-home locations for 
infusion of Bamlanivimab (LY3819253)  
External Sites  
(if applicable)  
None  
Sub-Investigators  
Ken Cohen, MD  
Ethan Berke, MD  
Deneen Vojta, MD  
Protocol Document 
Version & Date  
Version 10, Dated 2/3/2021  
  
1 
RESEARCH OVERVIEW / ABSTRACT   
  
This is an open-label, pragmatic, single-arm observational study using matched, real-world, 
external controls in participants with mild to moderate COVID-19.  Potential participants will 
track for symptom development while at home and upon reporting of symptoms will be tested for 
COVID-19.  If positive for COVID-19, a one-time at-home infusion of Bamlanivimab 


 
 
 
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(LY3819253) will be provided by Optum Infusion.  Participants will then be tracked for 28 days 
to assess for any additional medical care needed or if hospitalization was required.   
  
2. 
STUDY PURPOSE & OBJECTIVES  
The purpose of this study is to: Assess the safety of at-home infusion of Bamlanivimab and to 
determine whether symptomatic high risk COVID-19 participants will have a reduction in 
hospitalizations compared to a real-world external control population when provided with a 
combination of symptom tracking and single dose administration of Bamlanivimab (LY3819253) 
via an at-home infusion visit.     
  
The primary objectives are:  
Safety   
• 
To describe the incidence of infusion reactions during receipt of infusion and during the 
defined infusion follow-up period  
• 
To describe the incidence of patient reported adverse event outcomes through Day 28  
Efficacy  
• 
To determine the incidence of COVID-related hospitalization at Day 28 among 
Bamlanivimab-treated participants (long-term – hospitalization rate via medical 
records/claims data and short-term via participant self-report during active study 
participation) relative to external controls. 
The exploratory objective is: 
• 
To determine the incidence of COVID-related mortality at Day 28 among Bamlanivimab-
treated patients relative to external controls. 
 
3. 
JUSTIFICATION & BACKGROUND  
  
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in the  
pandemic spread of coronavirus disease 2019 (COVID-19), which in critical cases results in  
progressive pulmonary failure, complications with acute respiratory distress syndrome (ARDS),  
and in some cases death. There is an urgent need for effective therapeutics to modify disease 
outcomes. Bamlanivimab (LY3819253) is a neutralizing IgG1 monoclonal antibody (mAb) to the 
spike protein of SARS-CoV-2.  It is designed to block viral attachment and entry into human 
cells, thus neutralizing the virus, potentially preventing and treating COVID-19.    
  
4. 
STUDY DESIGN  
  
4.1 
Design  
Open-label, single-arm, pragmatic, observational study using matched, real-world external 
controls (all enrolled participants receive 700 mg Bamlanivimab– there is no placebo arm)  
  
4.2 
Duration  


 
 
 
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For enrolled participants: 
 
Overall duration → 6 months  
Individual participant duration (symptom tracking only) → up to 6 months  
Active participation after reporting symptoms → up to 38 days (28 days of active participation 
after receipt of infusion on Day 1 and up to 10 days from report of symptoms to receive 
infusion)   
 
 
For matched controls: 
 
Active insurance coverage after date of symptoms onset → up to 38 days. We require matched 
controls to remain enrolled under their UHC insurance plan to assure data availability. Similar to 
enrolled participants, matched controls may die after the synthetic date of treatment.  
  
4.3 
Data Description   
This section describes the main sources of data to be utilized for study participants and 
matched controls in Table 1. Specific data elements to be utilized are listed in the next section 
4.4. 
 
Table 1. Main data sources of study participants and matched controls 
Study 
population 
Data name 
Data description 
Study 
participants 
Optum infusion nurse data 
Day 1 at-home infusion will collect vitals and other 
essential data pre, during and post-infusion using 
an established case report form (CRF) to collect 
any events experienced during and post-infusion 
related to Bamlanivimab (LY3819253)   
ProtectWell data 
during symptom tracking – status of what was 
selected per daily check-in, first trigger point of 
noting experiencing symptoms, if COVID-19 
positive and received infusion – Day 1-28 wellness 
tracking (in numeric 1-10 ratings)  
Patient reported data 
from point of first report of symptoms through 
completion of participation on Day 28 (if COVID-19 
positive after initial report of symptoms)   
UHC claims data 
data including demographic characteristics 
(e.g. age, gender, race) and existing comorbidities 
(e.g. congestive heart failure, diabetes, chronic 
kidney disease)  
Blended Census Reporting 
Tool (BCRT) data 
real-time inpatient admission data including ICD-10 
diagnosis codes, discharge status, COVID test 
status reviewed by UHC’s clinical team  
Clinical Data Services 
Management (CDSM) data 
SARS-CoV-2 lab testing data from reporting health 
systems, clinics and third-party labs  
Matched 
controls 
OptumCare EMR data 
primary source to confirm for presence of 
symptoms during COVID+ test of a matched 
individual (analogous to the symptoms collected as 
part of ProtectWell data for study participants) 
 UHC claims data 
data including demographic characteristics 
(e.g. age, gender, race) and existing comorbidities 


 
 
 
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(e.g. congestive heart failure, diabetes, chronic 
kidney disease)  
 Blended Census Reporting 
Tool (BCRT) data 
real-time inpatient admission data including ICD-10 
diagnosis codes, discharge status, COVID test 
status reviewed by UHC’s clinical team  
 Clinical Data Services 
Management (CDSM) data 
SARS-CoV-2 lab testing data from reporting health 
systems, clinics and third-party labs  
  
 
 
  
  
 
  
4.4 
Metrics and Variables   
The primary outcome of interest is COVID-related hospitalization rate in study participants and 
matched controls. We list all potential covariates in two phases. Phase I covers historical data 
and Phase II includes post COVID-19 diagnosis information. Phase I historical data will be 
collected for both study participants and matched controls. Phase II post-COVID 19 diagnosis 
data has two parts: data elements to be collected for both study participants and matched 
controls (Phase II.A), and data elements to be collected for only study participants (Phase II.B). 
 
Phase I - Historical data: Study participants and matched controls:  
1. COVID-19-related information: 
• 
Symptomatic or asymptomatic during COVID-19 test 
• 
Number of COVID-19 cases per 10,000 people in the county of residence during 
COVID-19 test (if available) 
• 
Number of COVID-19 deaths per 10,000 people in the county of residence during 
COVID-19 test (if available) 
2. Demographic:  
• 
Age as of 2020 
• 
ZIP code of residence (may or may not be used for identifying matched controls) 
• 
Urbanization of residence in a zip code level (urban, suburban, rural) 
• 
Region/State of residence   
• 
Race   
• 
Gender  
• 
Flag determining whether a patient resides in a nursing facility (or has been staying in a 
nursing facility currently) – determining patient does not live at home 
3. Insurance:  
• 
Line of business (should be mostly Medicare)  
• 
Dual-eligibility status  
• 
Continuous member enrollment in 2019  
• 
Continuous member enrollment in 2020  
3.   Comorbidities in 2019:  
• 
History of chronic kidney disease  
• 
History of diabetes  
• 
History of immunosuppressive disease  
• 
History of cardiovascular disease  
• 
History of hypertension  
• 
History of chronic obstructive pulmonary disease/other chronic respiratory disease  


 
 
 
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• 
Elixhauser score for readmission  
• 
Historical flag for all other associated comorbidities used in computing Elixhauser score 
(i.e., 29 categories like kidney disease, cancer etc.)  
 
4. Immunosuppressive treatment 
• 
History of receiving immunosuppressive treatment (6 months prior to date of COVID 
diagnosis)  
Phase II.A: Post COVID-19 diagnosis data: Study participants and Matched controls 
 
• 
Date of COVID-19 diagnosis test  
• 
Type of COVID-19 diagnosis test (e.g., PCR, Antigen)   
• 
Hospital admission date (if patient is hospitalized)  
• 
Type of hospital admission (measuring level of trauma) (if available) 
o 
Through ER (if any) 
o 
With ICU transfer (if any) 
o 
With ventilation (if any) 
• 
Diagnosis and procedures associated with hospital admission  
• 
Death date (if applicable) 
• 
Discharge date (if applicable)  
 
Phase II.B: Post COVID-19 diagnosis data: Study participants only 
 
• 
 Infusion date (i.e., date at which drug is administered)  
• 
History of adverse effects (if any)   
• 
Details of adverse effects (if any)   
• 
Types of adverse effects (Based on Optum Infusion nurse data/ProtectWell assessment 
score of 1-10)   
• 
Date of experiencing adverse effect(s) (if applicable)   
• 
Drop-out date (If available) 
• 
Drop-out reason (if applicable) 
 
4.5 
Materials/Devices/Technology  
Primary materials that will be used in this study include:  
  
• 
COVID-19 at-home test kits (Let’s Get Checked, Everly Well)  
• 
ProtectWell App - initial tracking for symptoms and for tracking wellness from Day 1-28 
after receipt of Bamlanivimab (LY3819253)  
• 
Optum Infusion Kits  
• 
United in Research – Citizen Scientist Project Profile – to provide interim notifications 
and other check-in points as needed during the participation experience   
 
5. 
STUDY POPULATIONS  
  
5.1  
Target (Source) Population  
There are two study populations – study participants and matched controls. For study 
participants, we plan to enroll those who are 65 and older, and who are deemed high risk if 
contracting COVID-19. For matched controls, we primarily plan to utilize UHC members who 
have OptumCare EMR data to confirm presence of symptoms during COVID test as well as 


 
 
 
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having access to their claims and prospective hospitalization and death data.  If applicable, non-
UHC members may be used as members of the control population based on EMR review and 
matching to enrolled participants.  
  
5.2   
Number of Subjects  
Number targeted for tracking for symptoms: 500,000  
Number targeted for evaluable data (COVID-19 positive who receive Bamlanivimab 
(LY3819253)): 7500 (with an interim safety report to the FDA during the infusion of 
approximately 50 to 100 participants for safety concurrence and study progression)  
Number of matched controls: 7500, same as number targeted for evaluable data 
  
5.3   
Eligibility  
Both study participants and matched controls will meet the inclusion and exclusion criteria 
below.  
Inclusion Criteria:  
• 
Age 65+, confirmed SARS-CoV-2 positive, located in an area where 
Bamlanivimab (LY3819253) is available for infusion,  
• 
Have mild or moderate COVID-19 symptoms (after stage 1 of symptom tracking) 
• 
Control only: seek care at an OptumCare facility for confirmed symptomatic 
COVID-19 
Exclusion Criteria:  
• 
Current (from first symptom report) hospitalization for COVID-19  
• 
Prior administration of Bamlanivimab  
 
  
5.4 
Potentially Vulnerable Populations (Please select all that apply to this research)   
☒ UnitedHealth Group Employees  
☒ Intellectually/ Cognitively / Developmentally Disabled Persons  
☒ Economically Disadvantaged Persons  
☐ Children  
☐ Prisoners  
☐ Pregnant Women   
  
5.5 
Subject Identification & Accrual Plan   
Those who meet initial qualifying criteria (65+, located in an area with Bamlanivimab 
(LY3819253) is currently available or may become available) will be invited to participate.   
  
5.6 
Recruitment Plan and Materials  
Email/mail invitations to consider participation will be sent to those who qualify to consider 
joining to track for symptoms (located in an area where Bamlanivimab (LY3819253) is currently 
available).  
  
5.7 
Enrollment / Consent Plan and Materials  
Enrollment will occur in the online platform United in Research.   


 
 
 
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• 
https://www.unitedinresearch.com/volunteers-for-covid-19-experimental-treatment-
studies  
Enrollment plan:  
• 
If registered through United in Research as a Citizen Scientist – in an area where 
Bamlanivimab (LY3819253) at-home infusion is currently available – enrollment 
experience is agreeing to: daily tracking for symptoms via ProtectWell, to being tested 
for COVID-19 upon reporting symptoms, to receiving the one-time at-home infusion of 
Bamlanivimab (LY3819253) if positive for COVID-19 and to provide wellness checks 
from Day 1-28 after receipt of Bamlanivimab (LY3819253).  
Note: No participants will be able to begin tracking for symptoms via ProtectWell if they have not 
reviewed and signed the complete consent experience through receipt of Bamlanivimab 
(LY3819253) and ongoing wellness checks post-infusion.  This is will keep participants informed 
about what the overall experience will entail should they contract COVID-19 and avoid making 
any less informed decisions during the period of reporting symptoms and awaiting COVID-19 
results.  Participants are free to withdraw at any time and will be reminded of this throughout 
their experience. This balances the request for consent up-front for a product they may never 
require if they do not contact COVID-19.    
  
5.7  
Compensation / Remuneration / Reimbursement   
No compensation will be provided for participation.  There are no potential costs to reimburse 
since all study related activity costs are covered.  If additional COVID-19 care is required, this 
will be covered via usual insurance.  
 
6. 
STUDY PROCEDURES    
  
6.1 
Procedures  
Optum Infusion:  
Optum Infusion Pharmacy is a home infusion provider that will manage the shipment and 
administration of the study drug to the participants per the study protocol.  Upon testing positive 
for COVID-19, Optum Infusion Pharmacy will be notified by the sponsor.  The study drug will be 
shipped from a licensed pharmacy in validated cold chain containers and administered as soon 
as possible by registered nurses at the participant’s home   within 10 days of symptom onset.  
Receipt of Bamlanivimab:  
One-time dose of Bamlanivimab, delivered via infusion through the vein, lasting around 30 
minutes   
Active Participation Period (up to Day 28):  
After the Optum Infusion Nurse visit has concluded (any adverse event information during and 
immediately post-infusion will be collected by the Nurse and will be filed for safety assessment), 
participant will begin ProtectWell tracking on a daily basis from Day 1 (how do you feel starting 
on Day 1 – this is the baseline) through Day 28 with the following approach: Day 1 rating (1-10 
with 1 being the worst they can feel and 10 being the best they can feel) and then rating on a 
daily basis Day 2-28 (if rating remains in a 1-3 range, this will trigger clinical follow-up as the 
participant is not getting better).    


 
 
 
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Note: if traditional ProtectWell tracking cannot be utilized, the participant will be provided with 
contact information to report any issues that they are experiencing or to report that they are not 
feeling well.  
End of Study:  
Given the virtual nature of participation after the initial in-person infusion visit, participants will 
receive a notification on Day 28 thanking them for their participation and asking if they are 
willing to complete a brief survey on their study experience.  
  
6.2 
Subject Participation   
Enrolled in COVID-19 Readiness Cohort   
• If located in an area where at-home infusion of Bamlanivimab is available, screened for 
detailed eligibility and begin symptom tracking upon determining eligible   
• If symptoms begin to develop, receive COVID-19 test at home  
• If COVID-19 positive, home infusion visit is scheduled/confirmed for enrolled participant   
Please see Appendix 1 & 2 for additional detail.   
  
6.3 
Participant Engagement & Results   
All those who participate will have the opportunity to review findings/results and what actions 
were taken in response to those results either via clinicaltrials.gov or on United in Research 
(findings of United in Research projects are shared on the site for all to review – regardless of 
participation – and those who participated would also receive a specific notification that 
findings/results have been posted).    
  
6.4 
Data Collection  
Tracked data collection begins from first report of symptoms via ProtectWell (those who track 
but never experience symptoms will be accounted for in the final analysis but do not have a 
robust data collection process since they never proceed to the step of requiring a COVID-19 
test).    
Data collection timepoints:   
1. First report of symptoms  
2. COVID-19 test result (positive – moves forward; negative – resets the experience)   
3. Date of receipt of Bamlanivimab (LY3819253) via infusion (Day 1)  
4. Day 1 infusion (pre, during, post) collection of CRF data (participant vitals/health 
information and any events experienced)   
5. Day 1 post-infusion baseline wellness check through Day 28 final wellness check   
6. Any point during Day 1 post-infusion baseline through Day 28 where wellness check 
indicates participant is feeling worse   
7. Point from participant marking feeling worse through action taken (live daily check-in via 
clinical support, recommendation to seek medical care and/or requiring hospitalization)   
  
6.5 
Data Analysis   
There are four research objectives: one primary efficacy objective, one exploratory efficacy 
objective, and two secondary safety objectives. A control group will be needed to address the 
study objectives on efficacy of treatment. The following sections describe the creation of a  real-
world external control group, methods for matching study participants to controls, and analysis 
for the study objectives. 
 


 
 
 
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Figure 1 illustrates the timeline for study participants and controls to ensure alignment on an 
index date for symptoms onset and the follow-up period. Since the control group will not have 
an infusion date associated with them, we will randomly select a number from  the distribution of 
number of days after date of symptoms onset in study participants to identify an index date with 
which to define 28 days post infusion where we will monitor claims and EMR data for 
hospitalizations and mortality. 
 
Figure 1. Illustration of the timeline for study participants and controls 
 
 
6.5.1 Methodology for creating a matched cohort for the study participants  
Research Questions:  
Data Variables:  
Data Source:  
Can we build a control 
group for the study 
participants?  
Do we observe any 
differences in 
demographics and 
comorbidities between 
study participants and 
potential controls before 
matching?  
• Flag of infusion treatment  
• Demographics  
• Elixhauser conditions 
• Symptoms  
  
 
• Optum infusion nurse data  
• UHC claims data  
• CDSM   
• BCRT   
• OptumCare EMR data 
 
We consider three types of matching methods to identify a control cohort: propensity score 
matching (PSM) as the primary method and prognostic score matching (PrSM) as the 
alternative method. Each matching method has its strengths and weaknesses. Propensity score 
matching is the most used method for matching, but it is usually challenging to justify all 
confounders are considered and available.  In general, inference based on propensity method 
works well if the PSM model consists of statistically significant covariates that vary considerably 
between treatment and control. Alternative methods are proposed and to be used to ensure 
robustness of inference.    
 
6.5.1.1 Pre-matching identification of risk factors as matching covariates 
 
Before matching, we will identify the risk factors that are associated with COVID 
hospitalization. The result of this will be a parsimonious list of variables on which treatment and 
control group will be matched.   
Consider a binary response taking a value of 1 if a patient is hospitalized due to COVID-19, and 
0 if a patient is tested positive for SARS-CoV2 but does not have a record of subsequent 
COVID-related hospitalization (hospitalization within 38 days of a positive test).  We include risk 
factors such as demographic (e.g., gender, age, race, geographical area such as state or 


 
 
 
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county), comorbidities (Elixhauser scores related comorbidities such as diabetes, metastatic 
cancer, heart diseases, obesity, coagulopathy, and so on), and other potential clinical attributes 
as available in the identification of the appropriate matched-cohort. These risk factors will also 
include all clinically identified variables by Eli Lilly to define moderate and high-risk groups. We 
will run this step three times with three datasets; specifically, we consider: 
 
a. Iteration 1: This is based on retrospective data available in-house consisting of 
Medicare Advantage (MA) members, aged greater than or equal to 65, and COVID 
diagnosed. We will exploit BCRT and CDSM, UHC Claims. The study cohort here 
is a broader population than the study participants.   
b. Iteration 2: This is solely based on data associated with study participants. We will 
exploit risk factors from UHC Claims. and study enrollment information from Optum 
infusion nurse data.   
c. Iteration 3: This is based on  information of both the general MA population and 
study participants –retrospective and prospective data in Iteration 1 and Iteration 2, 
respectively. We will exploit COVID labs and hospitalizations from BCRT and 
CDSM, risk factors from UHC Claims data, and study enrollment information 
from Optum infusion nurse data.  
We will identify the covariates that are consistent across all three iterations and select the final 
set based on data, literature, and clinician decision. The main objective of this initial iteration 
step is to reduce dimension, exclude noise variables, include clinically meaningful variables, and 
lay out the foundation for the subsequent analyses. We will develop a generalized linear (mixed) 
model (Nelder, 1972) (McCulloch, 2014) with appropriately chosen link functions to quantify the 
association and make inferences about the parameter estimates based on the retrospective 
data. The appropriateness of using random effects will be evaluated by the likelihood ratio test 
(LRT) and summary information criteria (e.g., conditional or marginal Akaike information criteria 
(AIC)). We will select influential risk factors in the light of both clinical relevance and statistical 
significance. In this pursuit, we will use GL(M)M with a Least Absolute Shrinkage Penalty 
(LASSO) (Tibshirani, 1996) to develop a parsimonious model; here, the optimum parameters 
will be selected by k-fold cross-validation with the 1-standard-error rule. We will also check the 
statistical significance of any association based on Wald test statistic between each covariate 
and response based on univariate and multivariate GL(M)M; a pre-determined threshold of, say 
0.10 (level of significance), will be used to select covariates. This sequential inferential approach 
(i.e., Wald) is supplementary to the regularization step (i.e., GLMM-LASSO), and the final set of 
covariates will be determined by exploiting both/either approaches and will be combined with 
clinical relevance (i.e., factors used in determining high, moderate, low risk groups by Lilly).  
 
6.5.1.2 Primary method – propensity score matching   
The main objective of propensity score matching is to ensure balance between the treatment 
and control group on the selected covariates. Propensity score matching will be conducted after 
the completion of treatment enrollment (28-day study period) and upon the availability of 
information of study participants. Data sources of information for calculating the propensity 
scores for study participants will be Optum infusion nurse data, UHC claims data, and CDSM 
data. Note for the matched controls we will select them from OptumCare EMR+claims dataset, 
we will include only members who are COVID diagnosed, have documented symptoms of 
COVID-19, meet the inclusion/exclusion criteria for the study, and have historical information 
about selected risk factors (either through claims or EMR).    


 
 
 
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In this pursuit, we will apply propensity score matching techniques described in (Guo, 2014). We 
will fit a GLM assuming a known functional form (e.g., main effects) with response being the log 
odds of receiving treatment, and estimate balancing (i.e., propensity) scores which are next to 
be used in finding the matched pair between treatment and control group. We will use nearest 
neighbor matching with a caliper which its size will be determined empirically. Note this 
matching model is different from 6.5.1.1.1 in terms of response (hospitalization flag vs receiving 
treatment). We will evaluate balance in covariates between study participants and matched 
controls by absolute standardized difference in mean (Austin, 2009) and variance ratio of 
propensity score and covariates since it is more robust in terms of sample size and covariate 
distribution requirements in comparison to other balance diagnostics (Ali, 2016). We will also 
conduct a hypothesis test to examine if a significant difference in matching covariates exists 
between treatment and control groups, such as Wilcoxon rank-sum (Mann-Whitney) test, t-test, 
or other bivariate analysis. We will use these tests to evaluate covariate balances before and 
after matching and repeat the processes if covariate imbalances remain.  
We will consider multiple greedy matching algorithms (Smith, 2005) based on nearest neighbor 
matching, caliper matching, and nearest neighbor matching within a caliper, and obtain 1-to-1 
match after the baseline matching and before accessing any outcome data. As an alternative 
approach, optimal matching technique may also be adopted, in the event that the covariate 
imbalances persist using the prior methods. In addition, greedy matching methods make 
decisions about inclusion a pair of treated-control participants as a matched set sequentially; 
here decisions are made one at a time without reconsidering early decisions as later ones are 
made. From this point of view, such mechanism is not optimal and therefore optimal matching 
based sensitivity analyses will be performed to assess the numerical performances. In addition, 
propensity score subclassification (Rosenbaum, 1984) in conjunction with trimming 
strategy (Crump, 2009) will be adopted. Such greedy matching methods require a sizable (e.g., 
70%) common support region for logit between treated and control, known as “overlap 
assumption”. It is possible that greedy matching excludes participants because treated cases 
fall outside the lower end of the common support region (those who have low logit) and 
nontreated cases fall outside the upper end of the common support region (those who have high 
logit). This can be investigated once we fit PSM model, estimate the logits, and plot them side-
by-side for treated and control. If we see significant nonoverlap then we may need to try 
alternative such as trimming method. Regarding unmeasured confounding, we will also consider 
sensitivity analyses such as the e-value (VanderWeele, 2017) or the method of Zhang et. al 
(2018). 
 
 
 
6.5.2 Primary efficacy outcome analysis: COVID-related hospitalization rate at Day 28 
Research Questions:  
Data Variables:  
Data Source:  
Do we find a significant 
difference in rate of 
COVID-related 
hospitalization at Day 28 
between study participants 
and matched controls? 
  
• Flag of infusion treatment  
• Demographics  
• Elixhauser conditions  
  
• Patient self-reported data  
• Optum infusion nurse 
data  
• UHC claims data  
• CDSM COVID labs data  
• BCRT COVID 
hospitalizations data 
• OptumCare EMR data  


 
 
 
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What characteristics do 
those hospitalized study 
participants and matched 
controls have?  
 
 
Understanding the COVID-related hospitalization rate at Day 28 between study participants and 
matched controls is the key primary objective. We consider the analysis in combinations 
of with/without time-related outcome and with/without covariate adjustment. A single model will 
be selected and documented prior to accessing outcome data with the focus of including  
covariates expected to be strongly related to outcome based on other studies. 
First, consider the simplest scenario without covariates adjustment and time-related outcome; 
we will use nonparametric McNemar’s test and Wilcoxon signed-rank test to compare the 
hospitalization rate at Day 28 between the treatment and control groups at the level 0.05. On 
the other hand, for those who are not admitted due to COVID at Day 28, we will treat their non-
hospitalization as a censored response after Day 28, instead of assuming no COVID 
hospitalizations after Day 28. Without covariates adjustment, we will draw Kaplan-Meier curves 
and perform log-rank test to compare the hospitalization rate between the treatment and control 
groups.   
Next, consider covariates adjustment, this will allow us to compare hospitalization rate between 
treatment and control groups given a subset of matching covariates or covariates not included in 
matching. We will construct a generalized linear model with fixed or mixed effects to examine if 
the hospitalization rate differs between study participants and matched controls after adjusting 
demographics and comorbidities. Considering the time component in the response, we will use 
Cox proportional hazard model, accelerated failure time model and other survival models. 
Method selection will depend on if model assumptions hold in our data.  
In the short term, we will use patient-reported data and BCRT data to identify hospitalization 
among the treatment group; in the long term, we will use UHC claims data for inpatient records 
to examine if we obtain consistent results and receive additional information such as procedures 
and allowed amounts and for identification of hospitalizations for the control group. 
 
6.5.3 Exploratory efficacy outcome analysis: COVID-related mortality rate at Day 28 
 
Research Questions:  
Data Variables:  
Data Source:  
Do we observe COVID-
related mortality at Day 28 
between study participants 
and matched controls? 
  
What characteristics do 
those deceased study 
participants and matched 
controls have if any?  
• Flag of infusion treatment  
• Demographics  
• Elixhauser conditions  
• Symptoms 
  
• Patient self-reported data  
• Optum infusion nurse 
data  
• UHC claims data  
• CDSM COVID labs data  
• BCRT COVID 
hospitalizations data 
• OptumCare EMR data  
 
 
We will perform descriptive analysis and report summary statistics if there will be COVID deaths 
in study participants and matched controls. 
 
To address the secondary (safety) objectives we will perform the following analyses.  


 
 
 
14 
 
6.5.4 Secondary safety outcome analysis: Incidence of infusion reactions 
Research Questions:  
Data Variables:  
Data Source:  
Do we find any infusion 
reactions during receipt of 
infusion and during the 
infusion follow-up period? 
 
Do infusion reactions vary 
from demographic 
characteristics or 
comorbidities?   
• Date of infusion treatment  
• Date of infusion reactions 
of interest  
• Demographics  
• Elixhauser conditions  
 
• Patient self-reported data  
• Optum infusion nurse data  
• UHC claims data  
 
 
 
Clinicians will start with qualitative analysis on a case by case basis. We will report descriptive 
statistics according to the data we will obtain. If we have a sufficient size of study participants 
with particular infusion reactions, we can calculate descriptive statistics, run Chi-square 
independence test and Kruskal-Wallis test to examine if infusion reactions vary from 
demographics or comorbidities.  
 
6.5.5 Secondary safety outcome analysis: Adverse events 
Research Questions:  
Data Variables:  
Data Source:  
Do we find any self-
reported adverse events 
between receipt of infusion 
and day 28 from study 
participants?  
Do patient-reported 
adverse events vary from 
demographic 
characteristics or 
comorbidities?   
• Date of infusion treatment  
• Date of infusion reactions 
of interest  
• Demographics  
• Elixhauser conditions  
 
• Patient self-reported data  
• Optum infusion nurse data  
• UHC claims data  
 
 
Once adverse events are defined or classified and adjudicated, we can perform the same 
methodology described in 6.5.4.  
6.5.6 Subgroup Analysis 
Research Questions:  
Data Variables:  
Data Source:  
Is the time gap between a 
COVID positive test result 
and an infusion associated 
with COVID 
hospitalization?  
Do we find any patterns in 
a subgroup with certain 
demographic 
characteristics or 
comorbidities of interest?  
• Flag of infusion treatment  
• Demographics  
• Elixhauser conditions  
• Symptoms 
 
• Patient self-reported data  
• Optum infusion nurse data  
• UHC claims data  
• CDSM COVID labs data  
• ProtectWell data 
 
 
There are two pre-specified subgroup analyses we will pursue aside from the primary and 
secondary objectives (efficacy and safety) above.  


 
 
 
15 
 
First, we want to understand if the timing of the infusion relative to a positive COVID test, 
impacts hospitalizations. We will compute the Cramer’s V and phi coefficient as one-number 
summary of correlation between two categorical variables when we group time gap between 
date of COVID positive and date of home infusion. If considering this time gap as a continuous 
variable, we will run a simple logistic regression model to examine if time gap is associated with 
the hospitalization rate at Day 28. Then, we will apply the same methodology described above 
for categorical and continuous covariates and binary outcomes.  
  
Second, we want to understand if the timing of the infusion relative to a symptom onset, impacts 
hospitalizations. We will compute the Cramer’s V and phi coefficient as one-number summary of 
correlation between two categorical variables when we group time gap between date of COVID 
positive and date of home infusion. If considering this time gap as a continuous variable, we will 
run a simple logistic regression model to examine if time gap is associated with the 
hospitalization rate at Day 28. Then, we will apply the same methodology described above for 
categorical and continuous covariates and binary outcomes. 
 
7. 
RISKS AND BENEFITS  
  
7.1 
Risks and Risk Mitigation  
Bamlanivimab (LY3819253) is an investigational product for those who are at high-risk of 
experiencing more severe symptoms if they contract COVID-19.  The receipt 
of Bamlanivimab (LY3819253) occurs under an Optum Infusion Nurse monitored procedure, 
with appropriate protections in place to address any expected infusion reactions (either 
from Bamlanivimab (LY3819253) or the process of infusion) as well as to address any 
unexpected events that occur either pre, during or post-infusion 
of Bamlanivimab (LY3819253).  Nurses will remain for a period of up to two hours while 
preparing for, during and follow-up after infusion to confirm that the participant is stable and 
does not require any further medical oversight.    
Participants will self-report a baseline on Day 1 after receipt of the infusion and this baseline will 
create the landscape for their wellness reports Day 2 through 28 after receipt of infusion.  We 
will have additional contacts for participants external to the daily ProtectWell wellness check via 
the app, including using post-infusion Optum Infusion clinical support features as needed for 
anyone who is experiencing anything that requires more immediate attention.   
The risk of loss of confidentiality given the number of partners engaging to execute the research 
is always a possibility.  The risk is mitigated by using appropriate protected electronic files and 
to avoid exchanging information that contains protected health information (PHI) and no PHI will 
be released to the external partners, but personal identifying information (i.e. name, address) 
will be provided as needed.    
  
7.2 
Benefits  
There is no guarantee of direct benefit from participating. The study will test the hypothesis that 
treatment with Bamlanivimab (LY3819253) will reduce the incidence of COVID-19 related 
hospitalization and mortality in mild or moderate COVID-19 patients who are at high risk of 
progressing to severe COVID-19 and/or hospitalization, relative to matched external controls. 
The study will further describe the incidence of infusion related reactions and patient reported 
adverse events in patients treated with Bamlanivimab (LY3819253). 


 
 
 
16 
 
Given the available data and the recent emergency use authorization of Bamlanivimab 
(LY3819253) in this patient population, the benefit-risk assessment for this study is favorable. 
 
8. 
DATA HANDLING  
  
8.1 
Data Protection and Storage Plan   
  
The Data Operations Team and study staff within UHG R&D are responsible for adhering to the 
following Best Practices for Data Management:  
Data Operations follows the standards of HITRUST for data management and data governance  
1. Data always remains encrypted in transit and at rest.   
2. Managed access with expiration dates.   
3. Minimum use regarding dataset usage.   
Access to protected data is limited to the Data Operations Team within UHG R&D  
1. Data Operations does not participate in research efforts.   
2. Data Operations uses identifiable data to match across all data sources and issue 
surrogate keys.   
3. Data Operations de-identifies and lands the matched data in the Data Factory for use by 
the organization in research efforts.   
Restricted research project data can be isolated from other data sources  
1. Data Operations uses the Master Data Management (MDM) asset which contains logic 
to identify sources and matched people that have restrictions for use.   
2. The MDM de-identified surrogate key crosswalks are maintained in separate restricted 
datasets using the restricted flag as referenced in #1 above.   
3. Data operations user groups allow access to be limited to only those users with 
permission to work with restricted data. The restricted data is managed by the Data 
Operations to ensure isolation from other data sources.   
4. Data for use in operations businesses is also isolated at the project level in the Person 
ID Management (PIM) asset. Only users with project level access may view the 
identifiable data associated with these restricted records.  
  
8.2 
Power Analysis  
  
In this section, we detail the methodology and results for power analysis. To evaluate treatment 
effects based on the discrete clinical endpoint (hospitalization rate at Day 28 post infusion), we 
compared the proportion of events (i.e., prevalence rate or COVID hospitalization rate) between 
treatment and matched-control group which is our primary objective of interest. Specifically, we 
are interested in testing the efficacy (superiority) of treatment effects, which can be written 
formally in one- or two-sided hypotheses. Assume the null hypothesis is two-sided. We 
performed the power analyses based on a 5% level of significance (Type-I error) for a set 
of values of power (i.e., 0.75, 0.80, 0.85, 0.90, 0.95). We obtained the minimally required 
sample size for multiple scenarios - (a) hospitalization rate (0.20, 0.25, 0.30, 0.35) based on 
BCRT and CDSM data (b) different possible efficacy rates (reduction rates in hospitalization 
after using treatment). We used multiple methods - (i) a two-sample proportion test in detecting 
a difference between two binomial probabilities (Casagrande, 1978) (Fleiss, 1980) and 
(ii) McNemar’s test (Agresti, 2003).  
 
The former test statistic follows Gaussian distribution asymptotically exploiting large sample 
theory and the latter is designed to test categorical shift (or association) in responses 
(hospitalization rate in our example) between treatment and control group where the idea is to 
cast the data into a 2 x 2 contingency table. While both these methods provide competitive and 


 
 
 
17 
 
comparable results, reported below are the results based on the first method, a two-sample 
proportion test with continuity correction. The one-sided hypothesis requires a smaller sample 
size than that of a two-sided test.  
  
Figure 2 illustrates the sample size required with a case and control ratio being 1:1 for different 
hypothetical scenarios. As expected, when the true prevalence rates between treatment and 
control groups are small, it requires a larger sample size to detect a statistically significant 
difference. As reported in the Lilly SARS-CoV-2 Neutralizing Antibody Program Update, the 
hospitalization rate for the treatment (i.e., LY-CoV555 Mono) group drops by 75% compared to 
that of the placebo group. Based on this relative change, we will need a total sample size less 
than 1,000; follow the power curves (i.e., corresponding to 0.10 Type-II error) in Figure 2 top-
right panel with 75% (approximately) reduction rate. For example, using the COVID 
hospitalization rate of 25%, reported below in Table 2, are the minimum COVID diagnosed 
patients required to detect varying differences between the proportions at a 5% level of 
significance with 90% power of rejecting a two-sided null hypothesis.  
Table 2: A case study for baseline hospitalization rate of 25%. 
Absolute differences (assumed) between 
prevalence rates of treatment and control 
group (% reduction from baseline rate)  
Total sample size 
(treatment and control 
as 1:1)  
0.24 (95%)  
97  
0.21 (85%)  
129  
0.19 (75%)  
175  
0.16 (65%)  
244  
0.14 (55%)  
355  
0.11 (45%)  
550  
0.09 (35%)  
940  
0.06 (25%)  
1,899  
0.04 (15%)  
5,416  
                           0.01 (5%)  
49,900  
 
 


 
 
 
18 
 
 
 
Figure 2: Power analyses at 5% level of significance for six scenarios with baseline COVID hospitalization 
rates of 15% (top-left), 20% (top-right), 25% (middle-left), 30% (middle-right), 35% (bottom-left), and 40% 
(bottom-right) based on CDSM and BCRT databases. Varying prevalence rates along with reduction (in 
parenthesis) in hospitalization from baseline (i.e., control group) for the treatment group are considered (X-
axis) as 95% to 5%. Reference lines corresponding to 5,000 and 2,000 sample size are drawn for 
convenience of comparison. 
 
 
  
8.3 
Future Utilization  
There are no samples being collected from the participants in this study and therefore, there will 
be no residual biospecimens.  The data collected from this study, combined with appropriate 
health record and claims data, will be retained for additional analysis/utilization if determined 
appropriate (this will require a separate protocol submission or a modification to this existing 
study to outline the additional analysis occurring and the associated plan to retain confidentiality 
for the data being utilized).   
  
8.4 
Publication Plan  
There is intention to publish the findings of the study in partnership with Eli Lilly and 
Company.  Any data used for publication purposes will be presented in aggregate and will not 
identify individual participants.   


 
 
 
19 
 
 
1. 
QUALITY CONTROL, MONITORING AND REPORTING  
  
9.1   Issues Management Plan  
There are two potential sources of issues that may be experienced during participation in the 
study.  We will limit monitoring and reporting to those participants who 
receive Bamlanivimab (LY3819253).    
  
Participant Reported Issues:  
  
Participants will track daily how they are feeling from Day 1 post-infusion (their baseline of how 
they are currently feeling) through Day 28 (end of active study).  This tracking will be conducted 
via the ProtectWell application and will have two potential outcomes: 1) Participant indicates 
they are feeling the same or better (no further follow-up needed) or 2) Participant marks they 
are feeling worse and there will be a reminder notification via ProtectWell if they are 
experiencing severe/life-threatening symptoms to seek help and it will trigger a clinical follow-up 
with that participant (clinical follow-up will occur via phone outreach to result in a discussion with 
the participant).  Based on the discussion, one of the following may occur:1) participant is OK to 
remain at-home and on study or 2) participant should seek medical care and are marked as 
meeting an end point (and we will track outcome of what they needed and if they required 
hospitalization).  
  
In addition, when speaking to the participant, the clinical team will attempt to ascertain if the 
issue is: 1) related to worsening COVID-19 symptoms, 2) a potential  side effect 
of Bamlanivimab (LY3819253) or 3) any other unanticipated event that might occur during active 
study participation.  If the issue is potentially related to a side effect of 
Bamlanivimab (LY3819253), the clinical team will engage the UHG R&D clinical support team 
(internal R&D team with nurse management oversight for connection for clinical consult when 
needed) and that project support team will report the issue to Eli Lilly and Company if 
determined by the PI or delegate to be an unexpected and likely related to the study event 
(related to receiving Bamlanivimab (LY3819253) – not related to COVID-19 infection which is 
not due to participating in the study).    
  
Optum Infusion Reported Issues:   
  
Optum Infusion has three potential touch points with the participants in the study:  
  
Pre-Infusion:  
  
• 
Any issues that arise during the contact call to set up shipping the drug and scheduling 
the at-home infusion visit  
  
• 
Any issues that arise during the period from scheduling the at-home visit through to 
infusion visit  
  
Infusion Visit:  


 
 
 
20 
 
  
• 
Collection of pre, during and post-infusion information and any adverse reactions or 
other reported symptoms (this is formally documented on the case report form (CRF) 
and is compiled and reported to the UHG R&D project support team (and the project 
report team may determine that Eli Lilly and Company needs to be informed of 
information on the CRF and will follow-up on that accordingly – events that are likely to 
be related and/or unexpected in severity/frequency will be reported to Lilly). 
  
Post-Infusion:  
  
Optum Infusion will provide contact information for the participants at the infusion visit and the 
participant may opt to use this for outreach (they will also have ProtectWell and the outreach 
number provided on the consent document) and the Optum Infusion representative will discuss 
the issue with the participant and make a determination if they can give descriptive advice or if 
they need to loop in the UHG R&D project support team to engage either the UHG clinical team 
(if deemed appropriate) or   
to inform Eli Lilly and Company of the issue and expected plan of action to address the issue.   
  
o 
Any issues reported via either mechanism will be documented, assessed 
whether the event was related to study drug.  
o 
For Serious Adverse Events (SAEs) – events resulting in hospitalization, 
substantial action being required (without hospitalization) or death – events must 
be reported/assessed and submitted to the IRB for consideration within 72 hours  
o 
For any serious adverse events that meet the following criteria and result 
in any of the outcomes listed – these will be reported to Lilly upon 
discovery within 24 hours: 
o 
Suspected adverse reaction 
o 
Serious 
o 
Unexpected 
And result in the following outcomes (i.e. serious adverse events): 
o 
Death  
o 
Life-threatening adverse drug experience (meaning determined 
related to bamlanivimab)  
o 
Inpatient hospitalization or prolongation of existing hospitalization 
(for > 24 hours)  
o 
Persistent or significant incapacity or substantial disruption of the 
ability to conduct normal life functions  
o 
Congenital anomaly/birth defect (not likely to apply to this 
protocol) 
o 
Important Medical Event (IME) that may not result in death, be life 
threatening, or require hospitalization may be considered a 
serious adverse drug experience when, based upon medical 
judgment, it may jeopardize the patient or subject and may require 
medical or surgical intervention to prevent one of the outcomes 
listed in this definition  
o 
For Adverse Events (AEs) – events must be reported/assessed and submitted to 
the IRB for consideration within 10 business days   
o 
For any events – if event is likely related to Bamlanivimab – event will be 
assessed by the PI and medical review team and findings will be reported to Lilly 
in alignment with the FDA EUA requirements  


 
 
 
21 
 
Interim Safety Analysis and FDA Reporting: 
 
During the period of active at-home infusions (including post-infusion observation period), data 
will be compiled and assessed real time for any unanticipated problems or other serious 
adverse events that may require reporting to either Lilly and/or the FDA.  In addition, after 
infusion of approximately 50 to 100 participants (up to 100 participants), an interim safety report 
will be compiled for Lilly and the FDA, per the FDA’s request in the IND correspondence letter 
received on December 23, 2020.  Given the design of the study and the fact that there is a 10-
day from symptom onset window to infuse participants after confirmed COVID-19 diagnosis with 
a constant rolling list of eligible participants to have an at-home infusion of Bamlanivimab 
scheduled – the number of participants infused will be at least 50 and no more than 100 when 
this interim report is filed with Lilly and the FDA.  Enrollment of new participants to enter the first 
stage of the trial (symptom screening) will pause while FDA review of the interim safety data 
occurs and enrollment will not recommence until FDA concurrence of enrollment expansion is 
received.   
 
APPENDIX  
  
1. Subject Participation Flow 
Prospective/Potential Participant is informed about joining the COVID-19 Readiness Cohort and to visit 
United in Research to create a Citizen Scientist profile (or if they are already registered – they can log 
into their existing profile and review and choose to join the COVID-19 Readiness Cohort  
 
 
For those who are willing/interested, a Citizen Scientist profile is created (the profile collects the 
following information: name, age, best contact information including cell phone number, and whether 
the person has UHC insurance) 
 
 
After creating a profile, the Citizen Scientist is now eligible to pre-enroll into the COVID-19 Readiness 
Cohort to be placed on the waiting list for notification that their area was selected due to location to 
product supplier for the Lilly product and Optum Infusion Nurses 
 
 
During pre-enrollment, additional information is collected from the Citizen Scientist – specific to 
information needed to determine eligibility for the Lilly product study (this includes: confirming home 
address, height/weight, race/ethnicity, any potential exclusions such as pregnancy, cancer, transplants, 
transfusions, or COVID vaccines, confirmation that they are willing to use a smartphone app for 
symptom tracking (Protect Well), willingness to do COVID testing – either at home and mail it back, at a 
clinic or that either is fine and willingness to consent to the Lilly product trial)  


 
 
 
22 
 
 
 
After reviewing what they are agreeing to (the COVID-19 Readiness Cohort Consent) – if they choose to 
join, they are on the waiting list for location selection eligibility determination (along with individual 
eligibility determination from that point forward – but the first step is picking a location where they can 
reasonably receive the product and have it infused)  
 
 
 
If their area is selected as a Lilly product eligible site – the pre-enrolled Citizen Scientists in the COVID-19 
Readiness Cohort in that area will receive an automated email notification (to whatever preference 
email they have in United in Research or a text message if that was their preferred method of contact) 
that they are now eligible to enroll into symptom tracking via Protect Well and will provide instructions 
for how they should do that and begin tracking 
 
 
If symptomology in the ProtectWell app indicates likely COVID-19 infection, a kit would be expedited (if 
they are not in Well At Home) to the house address for the participant to complete/return   
 
If symptomology in the ProtectWell app indicates likely COVID-19 infection, the registered participant 
would receive notification of how to proceed with completing the test kit and sending in/processing for 
results  
 
 
In either scenario, once the kits are completed/returned and COVID-19 infection is confirmed via the 
test, the participant becomes eligible to move forward into actively participating in the Lilly product trial 
and UHG R&D will flag them as ready to schedule the at-home infusion  
 
 
 
At-home infusion appointment is scheduled – aligning with product preparation and considerations for 
site specific travel times and product shelf-life after preparation – and confirmed with those in the 
household receiving the infusion  


 
 
 
23 
 
 
 
At-home infusion occurs, and proper protocol is followed for infusion of the product and monitoring 
those who received it for any unexpected effects after receipt of the product  
 
 
After at-home infusion, the household will be tracked via the ProtectWell app and any specific 
symptom/side effect tracking mechanisms in the United in Research platform will also occur to monitor 
how the participants are doing  
 
 
If any issues develop during the active follow-up period, the issues will be presented to Lilly (if they are 
medical/clinical in nature – not logistical or operational issues) for consideration of: 1) how to address 
for the particular participant (or whether any action is required) and 2) if the issues reported warrant 
informing the FDA 
 
 
At completion of the active follow-up period data will be compiled and prepared and shared with Lily for 
review/analysis in order to produce interim findings (and any unexpected findings) during active 
conduct of the study  
 
 
Once a participant has completed their active follow-up phase, we will either thank them for their 
participation and consider them as completed (informing them that we will circle back with results of 
the project) or if there is any advantage to keeping them tracking for long-term response or effects, we 
could develop a long-term follow-up mechanism (i.e. please do inform us via United in Research if you 
start developing symptoms again or if a member of the household gets sick)  
 
 
 
 
 
 


 
 
 
24 
 
 
 
 
 
 
 
 
 
2. Subject Participation Flow Visual 
 
 
 
3. Wellness check plan and clinical connection 
Instructing Patients for Using ProtectWell Wellness Check via the App Post-Infusion from Day 1 – Day 
28  
Rating system: 
• 
Using a 1 – 10 system (1 = the worst you can feel and 10 = feeling great)  
• 
Grouping:  


 
 
 
25 
 
o 1-3 rating (potential flag for clinical outreach) 
o 4-7 rating (feeling OK – no action needed) 
o 8-10 (feeling good/great – no action needed)  
• 
Day 1 rating dictates baseline for Days 2-28  
o Goal is for patients to either stay steady at Day 1 rating or to continue to see an increase 
as they progressively feel better  
• 
Rating drops between Day 2 and Day 28 
o If rating drops back to a 4 or above from where the patient was (for example – 
registered a 6 and the next day registered a 4) – no clinical follow-up at that time but 
will get a note recognizing that they took a step back in their ratings  
o If rating drops back to 3 or below from where the patient was (for example – registered 
a 5 and the next day registered a 3) – this would trigger clinical outreach by the UHG 
Clinical Team   
• 
Post-Infusion Day 1 – Baseline 
o It is fine to start at a 1-3 (and likely expected as these folks have COVID-19 and even 
though symptoms are mild to moderate – they are probably not feeling too well)  
o It is fine to stay in the 1-3 range for a few days if they are either: 
▪ 
Staying the same as baseline 
▪ 
Seeing minor increases (for example – going from a 1 to a 2 or a 2 to a 3)  
o If the patient continues to check the same rating but does not get above a 3 – this would 
be flagged for clinical follow-up – the clinical lead will assess what day of the experience 
the participant is in and either flag them for clinical follow-up connection or document 
permitting another wellness day check-in to see if the level of wellness improves  
• 
Clinical Follow-up/Outreach During 28-day active period 
o UHG R&D internal clinical team (leadership role is a RN) will review the twice daily 
reports that have extracted any participants who have a 1-3 rating  
o This report will dictate who requires clinical follow-up/outreach or it will be 
documented as to the reason why clinical connection was not (see example above – 
closer to day 1 post-infusion)  
o Clinical follow-up will occur with consistency by using the “Symptoms List for Clinical 
Outreach and Action Plan”  
o Information collected during clinical follow-up will be consistently documented in the 
participant file and reports for symptoms and other issues being reported will be 
generated for assessment as needed (i.e. aggregate reports for assessing 
severity/frequency of events as needed)  
o Clinical action taken in response to reported symptoms/issues will also be documented  
• 
Overall goal  
o Patients will progress from the lower numbers to the higher range numbers during the 
28-day period (with hopefully a good jump from initial receipt of infusion)  
 
4. Symptom checker and clinical action 
Participant ID:  
Assessment Date: (DD/MMM/YYYY) 
 


 
 
 
26 
 
High priority  
Cough – requires immediate medical attention if severe symptoms reported  
○ Yes 
○ Mild 
○ Moderate 
○ Severe 
○ No (Absent) 
 
High priority  
Shortness of breath – requires immediate medical attention if moderate to severe symptoms reported 
○ Yes 
○ Mild 
○ Moderate 
○ Severe 
○ No (Absent) 
 
High priority  
Feeling feverish – requires immediate medical attention if high fever is reported. For moderate fever, 
clinical call required 
○ Yes 
○ Low grade (<100 degrees F) 
○ Moderate – (100-102 degrees F) 
○ High (anything above 102 degrees F) 
○ No (Absent) 
 
High priority  
Chills/Shaking – requires immediate medical attention if severe symptoms reported (severe 
symptoms being “shaking chills” – i.e. teeth chattering, body shakes) 
○ Yes 
○ Mild 
○ Moderate 
○ Severe 
○ No (Absent)  
Body aches and pain – requires immediate 
medical attention if severe symptoms reported 
○ Yes 
○ Mild 
○ Moderate 
○ Severe 
○ No (Absent) 
Headache  
○ Yes 
○ Mild 
○ Moderate 
○ Severe 
○ No (Absent) 
 
Fatigue  
Loss of taste  


 
 
 
27 
 
○ Yes 
○ Mild 
○ Moderate 
○ Severe 
○ No (Absent) 
○ Yes 
○ No 
Sore throat  
○ Yes 
○ Mild 
○ Moderate 
○ Severe 
○ No (Absent) 
Loss of smell  
○ Yes 
○ No 
 
Loss of appetite  
○ Yes 
○ Mild 
○ Moderate 
○ Severe 
○ No (Absent) 
Nausea 
○ Yes 
○ Mild 
○ Moderate 
○ Severe – how often ___________ 
○ No (Absent) 
 
Nausea 
○ Yes 
○ Mild 
○ Moderate 
○ Severe – how often ___________ 
○ No (Absent) 
 
Diarrhea 
○ Yes 
○ Mild 
○ Moderate 


 
 
 
28 
 
 
 
For any issues reported or discovered that may meet the following criteria, please contact Tracy Ziolek 
immediately at 215-868-3114 or tracy_ziolek@uhg.com to report: 
○ Severe – how often ___________ 
○ No (Absent) 
 
 
 
 
 
 
 
Are you experiencing any additional symptoms/health issues beyond what was just asked above? 
○ Yes 
○ No 
If yes – please explain:  
 
 
 
 
Overall, how bad are your symptoms TODAY?  
○ No symptoms 
○ Mild 
○ Moderate 
○ Severe 
○ Very severe 
 
 
 
Overall, how is your general physical health 
TODAY? 
○ Poor 
○ Fair 
○ Good 
○ Very good 
○ Excellent 


 
 
 
29 
 
Event is likely related to bamlanivimab and is both unexpected and serious (see outcomes below for 
serious adverse events) → 
Event outcomes resulted in: 
1) Death  
2) Life-threatening adverse drug experience (meaning determined related to bamlanivimab)  
3) Inpatient hospitalization or prolongation of existing hospitalization (for > 24 hours) *already being 
checked for due to primary efficacy endpoint 
4) Persistent or significant incapacity or substantial disruption of the ability to conduct normal life 
functions  
5) Congenital anomaly/birth defect  
6) Important Medical Event (IME) that may not result in death, be life threatening, or require 
hospitalization may be considered a serious adverse drug experience when, based upon medical 
judgment, it may jeopardize the patient or subject and may require medical or surgical intervention to 
prevent one of the outcomes listed in this definition 
 

SAP_003.pdf: 
CONFIDENTIAL 
[Protocol number: UHG (J2X-NS-I002)] SAP [v.3] 
 
 
LY3819253 
For clinicaltrials.gov submission: 
 
Full Title:  
At-Home Infusion using Bamlanivimab in Participants with Mild to Moderate COVID-19 symptoms    
Project UNITED   
 
NCT: NCT04656691 
 
Date of Document: 3/9/2021 (uploaded on 3/11/2022)  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 


CONFIDENTIAL 
[Protocol number: UHG (J2X-NS-I002)] SAP [v.3] 
 
 
LY3819253 
 
 
 
 
 
 
 
Statistical Analysis Plan: 
 
Protocol UHG (J2X-NS-I002): At-Home Infusion using 
Bamlanivimab in Participants with Mild to Moderate COVID-19 
symptoms 
 
Confidential Information 
The information contained in this document is confidential and is intended for the use of clinical investigators.  It is the property of 
Eli Lilly and Company or its subsidiaries and should not be copied by or distributed to persons not involved in the clinical 
investigation of LY3819253 and LY3832479, unless such persons are bound by a confidentiality agreement with Eli Lilly and 
Company or its subsidiaries.  
 
Note to Regulatory Authorities:  This document may contain protected personal data and/or commercially confidential 
information exempt from public disclosure.  Eli Lilly and Company requests consultation regarding release/redaction prior to any 
public release.  In the United States, this document is subject to Freedom of Information Act (FOIA) Exemption 4 and may not be 
reproduced or otherwise disseminated without the written approval of Eli Lilly and Company or its subsidiaries. 
 
LY3819253 (bamlanivimab) 
 
 


CONFIDENTIAL 
[Protocol number: UHG (J2X-NS-I002)] SAP [v.3] 
 
 
LY3819253 
Eli Lilly and Company, UHG R&D 
Indianapolis, Indiana USA 46285 
UHG (J2X-NS-I002) 
Phase 2 
 
 
 
 


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[Protocol number: UHG (J2X-NS-I002)] SAP [v.3] 
 
 
LY3819253 
Table of Contents 
Table of Contents ...........................................................................................................................4 
1. 
Revision History ..............................................................................................................6 
2. 
Background and Rationale ............................................................................................7 
3. 
Objectives ........................................................................................................................8 
3.1 
Primary Objective .............................................................................................................8 
3.2 
Exploratory Objectives .....................................................................................................8 
3.3 
Safety Objectives ..............................................................................................................8 
4. 
Research Design ..............................................................................................................9 
4.1 
Summary of Research Design ...........................................................................................9 
4.2 
Data Collection .................................................................................................................9 
4.3 
Sample Size Determination ............................................................................................11 
4.4 
Study Population .............................................................................................................13 
4.4.1 
Selection Criteria ............................................................................................................13 
4.4.2 
Patient Groups .................................................................................................................14 
4.5 
Endpoints ........................................................................................................................16 
4.6 
Study Therapies ..............................................................................................................16 
4.7 
Variables/Measures .........................................................................................................16 
5. 
Statistical Analyses .......................................................................................................20 
5.1 
General Considerations ...................................................................................................20 
5.1.1 
Populations for Analyses ................................................................................................20 
5.2 
Adjustment for Covariates ..............................................................................................21 
5.3 
Multiple Comparisons/Multiplicity ................................................................................21 
5.4 
Participant Dispositions – Study Participants Group Only .............................................21 
5.5 
Patient Characteristics.....................................................................................................22 
5.6 
Treatment Compliance ....................................................................................................22 
5.7 
Prior Medication and Concomitant Therapy ..................................................................22 
5.8 
Primary Analysis ............................................................................................................22 
5.8.1 
Main analytical approach ................................................................................................22 
5.8.2 
Sensitivity Analyses ........................................................................................................26 
5.8.3 
Handling of missing data or dropouts .............................................................................27 
5.9 
Exploratory Analyses ......................................................................................................28 
5.10 
Safety Analyses...............................................................................................................28 
5.10.1.1 Extent of Exposure ..........................................................................................................29 
5.10.1.2 Adverse Events ...............................................................................................................29 
5.11 
Protocol Violations .........................................................................................................30 
5.12 
Other Analyses ................................................................................................................30 
5.13 
Interim Analyses .............................................................................................................31 
5.14 
Changes to Protocol-Planned Analyses ..........................................................................31 
6. 
Supporting Documentation ..........................................................................................32 
6.1 
Appendix 1: Definitions and Medical Codes ..................................................................32 
6.2 
Appendix 2: CRF forms or questionnaires .....................................................................35 
6.3 
Appendix 3: Mock TFLs.................................................................................................39 


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7. 
References ......................................................................................................................50 
 
 
Table of Abbreviations 
 


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1. Revision History 
This is the thirdversion of statistical analysis plan (SAP) for the UNITED protocol. We (UHG) 
update the second version according to the feedback from Eli Lilly.  
 
 


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2. Background and Rationale 
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in 
the pandemic spread of coronavirus disease 2019 (COVID-19), which in critical cases results 
in progressive pulmonary failure, complications with acute respiratory distress syndrome 
(ARDS), and in some cases death. There is an urgent need for effective therapeutics to modify 
disease outcomes. Bamlanivimab (LY3819253) is a neutralizing IgG1 monoclonal antibody 
(mAb) to the spike protein of SARS-CoV-2. It is designed to block viral attachment and entry 
into human cells, thus neutralizing the virus, potentially preventing and treating COVID-19.    
This is an open-label, pragmatic, single-arm observational study of Bamlanivimab (LY3819253) 
infusion efficacy and safety using matched, real-world, external controls in participants with 
mild to moderate COVID-19.   
 


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3. Objectives 
The purpose of this study is to: Determine whether symptomatic high risk COVID-19 
participants will have a reduction in hospitalizations compared to a real-world external control 
population when provided with a combination of symptom tracking and single dose 
administration of Bamlanivimab (LY3819253) via an at-home infusion visit.     
3.1 Primary Objective 
The primary objective is related to efficacy and is:  
• 
To determine the incidence of COVID-related hospitalization at Day 28 among 
Bamlanivimab-treated relative to external controls. 
3.2 Exploratory Objectives 
The exploratory objective is related to efficacy and is: 
• 
To determine the incidence of COVID-related mortality at Day 28 among Bamlanivimab-
treated patients relative to external controls. 
3.3 Safety Objectives 
The safety objectives are considered as secondary objectives. They are: 
• 
To describe the incidence of infusion reactions during receipt of infusion and during the 
defined infusion follow-up period (follow-up period to be consistent with EUA fact sheet 
for healthcare providers), 
• 
To describe the incidence of patient reported adverse event (AE) outcomes through Day 
28.  
 
 


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4. Research Design 
4.1 Summary of Research Design 
This is an open-label, pragmatic, single-arm observational study using matched, real-world, 
external controls in participants with mild to moderate COVID-19. Potential participants will 
track for symptom development while at home and upon reporting of symptoms will be 
tested for COVID-19.  If positive for COVID-19, a one-time at-home infusion of 700mg 
Bamlanivimab (LY3819253) will be provided by Optum Infusion. Study participants who 
received infusion will then be tracked for 28 days to assess for any additional medical care 
needed or if hospitalization was required.   
There is no placebo arm in the study, hence, we will identify external matched controls using 
propensity score matching and assess the robustness of findings via sensitivity analysis. We 
elaborate how we collect data from the target population and what data elements will be captured 
in the following subsections. Figure 2 (in Section 4.4) describes how we identify study 
participants and Figure 3 (in Section 4.5) illustrates the timeline of study participants and 
controls. 
4.2 Data Collection 
The data sources for study participants and the pool of potential controls are not all the same 
because we don’t enroll matched controls in this study. To make sure both groups are 
symptomatic, we will use Optum electronic medical record (EMR) data to select symptomatic 
COVID-19 cases as candidates of control, while this information for study participants comes 
from ProtectWell symptoms data. We summarize the data sources for study participants and 
matched controls in Table 1.  
For enrolled participants, tracked data collection begins from first report of symptoms 
via ProtectWell. The data collection timepoints are: 
1. First report of symptoms  
2. COVID-19 test result (positive – moves forward; negative – resets the experience)   
3. Date of receipt of Bamlanivimab (LY3819253) via infusion (Day 1)  
4. Day 1 infusion (pre, during, post) collection of CRF data (participant vitals/health 
information and any events experienced)   
5. Day 1 post-infusion baseline wellness check through Day 28 final wellness check   
6. Any point during Day 1 post-infusion baseline through Day 28 where wellness check 
indicates participant is feeling worse   
7. Point from participant marking feeling worse through action taken (live daily check-in via 
clinical support, recommendation to seek medical care and/or requiring hospitalization)   
Regarding the timeline, the overall duration for enrolled participants is up to 6 months with 
symptom tracking only. Once an enrolled participant becomes a study participant after reporting 
symptoms, it is up to 38 days (28 days of active participation after receipt of infusion on Day 1 
and up to 10 days from report of symptoms to receive infusion). For matched controls, the active 
insurance coverage after date of symptoms onset should be up to 38 days. We require matched 
controls to remain enrolled in utilized data sources to assure data availability. Similar to enrolled 
participants, matched controls may die after the synthetic date of treatment. 
Commented [WWY1]: Would suggest to add 
 A figure to illustrate study design, including screening and 
treatment time point and assessment period 
Commented [JAT2R1]: Thanks for the suggestion. I added a 
sentence directing the audience to Figures 2 and 3 on the screening 
process and timeline. 


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Table 1. Main data sources of study participants and the potential pool of controls. 
Study 
population 
Data name 
Data description 
Study 
participants 
Optum infusion pharmacy 
data 
Day 1 at-home infusion will collect vitals and other 
essential data pre, during and post-infusion using an 
established case report form (CRF) to collect any 
events experienced during and post-infusion related 
to Bamlanivimab (LY3819253)   
ProtectWell data 
during symptom tracking – status of what was 
selected per daily check-in, first trigger point of 
noting experiencing symptoms, if COVID-19 
positive and received infusion – Day 1-28 wellness 
tracking (in numeric 1-10 ratings)  
UHC claims data 
(UHC members only) 
data including demographic characteristics 
(e.g., age, gender, race) and existing comorbidities 
(e.g., congestive heart failure, diabetes, chronic 
kidney disease)  
Blended Census Reporting 
Tool (BCRT) data 
(UHC members only) 
real-time inpatient admission data including ICD-
10 diagnosis codes, discharge status, COVID test 
status reviewed by UHC’s clinical team; will be 
used as complementary to Optum infusion 
pharmacy data for COVID hospitalizations  
Clinical Data Services 
Management (CDSM) data 
(UHC members only) 
SARS-CoV-2 lab testing data from reporting health 
systems, clinics, and third-party labs; will be used 
as complementary to Optum infusion pharmacy 
data for COVID tests   
Potential pool 
of controls 
Optum EMR data 
primary source to confirm for presence of 
symptoms during COVID+ test of a matched 
individual (analogous to the symptoms collected as 
part of ProtectWell data for study participants); will 
also be used for COVID test status and COVID 
hospitalization of all potential controls, and for 
demographic characteristics, existing 
comorbidities, COVID test status and COVID 
hospitalization of potential non-UHC controls. 
UHC claims data 
(UHC members only) 
data including demographic characteristics 
(e.g., age, gender, race) and existing comorbidities 
(e.g. congestive heart failure, diabetes, chronic 
kidney disease)  
Blended Census Reporting 
Tool (BCRT) data 
real-time inpatient admission data including ICD-
10 diagnosis codes, discharge status, COVID test 
Commented [JAT3]: This doesn’t change any data elements but 
the data name. 


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(UHC members only) 
status reviewed by UHC’s clinical team; this 
dataset does not include symptom information 
during COVID test, hence it will be only used as a 
potential sensitivity analysis data source 
Clinical Data Services 
Management (CDSM) data 
(UHC members only) 
SARS-CoV-2 lab testing data from reporting health 
systems, clinics, and third-party labs; this dataset 
does not include symptom information during 
COVID test, hence it will be only used as a 
potential sensitivity analysis data source  
 
4.3 Sample Size Determination 
To evaluate treatment effects based on the discrete clinical endpoint (hospitalization rate at Day 
28 post infusion), we compared the proportion of events (i.e., prevalence rate or COVID 
hospitalization rate) between treatment and matched-control group which is our primary 
objective of interest. Specifically, we are interested in testing the efficacy (superiority) of 
treatment effects, which can be written formally in one- or two-sided hypotheses. Assume the 
null hypothesis is two-sided. We performed the power analyses based on a 5% level of 
significance (Type-I error) for a set of values of power (i.e., 0.75, 0.80, 0.85, 0.90, 0.95). We 
obtained the minimally required sample size for multiple scenarios - (a) hospitalization rate 
(0.20, 0.25, 0.30, 0.35) based on BCRT and CDSM data (b) different possible efficacy rates 
(reduction rates in hospitalization after using treatment). We used multiple methods - (i) a two-
sample proportion test in detecting a difference between two binomial probabilities (Casagrande, 
1978) (Fleiss, 1980) and (ii) McNemar’s test (Agresti, 2003).  
The former test statistic follows Gaussian distribution asymptotically exploiting large sample 
theory and the latter is designed to test categorical shift (or association) in responses 
(hospitalization rate in our example) between treatment and control group where the idea is to 
cast the data into a 2 x 2 contingency table. While both these methods provide competitive and 
comparable results, reported below are the results based on the first method, a two-sample 
proportion test with continuity correction. The one-sided hypothesis requires a smaller sample 
size than that of a two-sided test.  
Figure 1 illustrates the sample size required with a case and control ratio being 1:1 for different 
hypothetical scenarios. As expected, when the true prevalence rates between treatment and 
control groups are small, it requires a larger sample size to detect a statistically significant 
difference. As reported in the Lilly SARS-CoV-2 Neutralizing Antibody Program Update, the 
hospitalization rate for the treatment (i.e., LY-CoV555 Mono) group drops by 75% compared to 
that of the placebo group. Based on this relative change, we will need a total sample size less 
than 1,000; follow the power curves (i.e., corresponding to 0.10 Type-II error) in Figure 1 top-
right panel with 75% (approximately) reduction rate. For example, using the COVID 
hospitalization rate of 25%, reported below in Table 2, are the minimum COVID diagnosed 
patients required to detect varying differences between the proportions at a 5% level of 
significance with 90% power of rejecting a two-sided null hypothesis. 
 


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Table 2. A case study for baseline hospitalization rate of 25%. 
Absolute differences (assumed) between 
prevalence rates of treatment and control 
group (% reduction from baseline rate)  
Total sample size 
(treatment and control 
as 1:1)  
0.24 (95%)  
97  
0.21 (85%)  
129  
0.19 (75%)  
175  
0.16 (65%)  
244  
0.14 (55%)  
355  
0.11 (45%)  
550  
0.09 (35%)  
940  
0.06 (25%)  
1,899  
0.04 (15%)  
5,416  
                             0.01 (5%)  
49,900  
 
 
 


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LY3819253 
 
 
Figure 1. Power analyses at 5% level of significance for six scenarios with baseline 
COVID hospitalization rates of 15% (top-left), 20% (top-right), 25% (middle-left), 30% (middle-
right), 35% (bottom-left), and 40% (bottom-right) based on CDSM and BCRT 
databases. Varying prevalence rates along with reduction (in parenthesis) in hospitalization from 
baseline (i.e., control group) for the treatment group are considered (X-axis) as 95% to 5%. 
Reference lines corresponding to 5,000 and 2,000 sample size are drawn for convenience of 
comparison. 
 
4.4 Study Population 
4.4.1 Selection Criteria 
Both study participants and matched controls will meet the inclusion and exclusion criteria 
below.  
Inclusion Criteria:  
• Age 65+, confirmed  SARS-CoV-2 positive, located in an area where 
Bamlanivimab (LY3819253) is available for infusion 
• Have mild or moderate COVID-19 symptoms (after stage 1 of symptom tracking)  
Commented [JAT4]: Update to keep the same criteria in the 
latest protocol (version 10 on Feb 3, 2021). 
Commented [JAT5]: Remove it as it is no necessary for active 
participants and controls. 


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LY3819253 
• Control only: Confirmed symptomatic COVID-19 through Optum EMR data. 
Exclusion Criteria:  
• Current (from first symptom report) hospitalization for COVID-19  
• Prior administration of Bamlanivimab  
4.4.2 Patient Groups 
Before analysis there are two study populations – study participants and the pool of potential 
controls. For study participants, we plan to enroll aged 65 and older, who are deemed high risk 
per the emergency use authorization, if contracting COVID-19. For the pool of potential 
controls, we plan to utilize UHC members who have Optum EMR data to confirm presence of 
symptoms during COVID test as well as having continuous enrollment to access to their claims 
and prospective hospitalization and death data. If applicable, non-UHC members or UHC 
members who did not seek care at OptumCare facilities may be used as members of the control 
population based on EMR review and matching to enrolled participants.  
During analysis (i.e., after matching), there will be two study populations – study participants 
after matching and matched controls. Note that the size of study participants after matching may 
be smaller than the size of original study participants if a matched control can’t be identified. 
The targeted number of enrolled participants for tracking for symptoms is 500,000, and the 
targeted number of study participants who are COVID-19 positive and consent to receive 
Bamlanivimab (LY3819253) is 7,500. Figure 2 illustrates the process to identify study 
participants. Regarding controls, we set the case:control ratio at 1:1 and plan to identify 7,500 
matched controls which is the same as number targeted for study participants. 
Given the current enrollment speed (as of March 9, 2021) and the decreasing COVID-19 
hospitalization rate across the US, we may not achieve the target number of study participants, 
7,500. However, according to the power analysis in Section 4.3, it is sufficient to have 1,000 
study participants with another 1,000 matched controls to detect a 33% decrease in the 
hospitalization rate (i.e., from 15% to 10%) with power 0.8 and level 0.05. 
Commented [JAT6]: Remove as not necessary per the EUA. 
Commented [JAT7]: Remove as treatment is allowed in this 
situation per the EUA. 
Commented [JAT8]: Remove prior vaccination from exclusion 
criteria as this is not required per the EUA. 
Commented [WWY9]: Should we replace continuous member 
enrollment? 
 
Commented [WWY10]: Given current enrollment speed, it will 
be helpful to show what is the minimal sample size for Bam treated 
group required for the primary objective analysis.   
Commented [WWY11]: Suggest to provide power estimate and 
assumptions (e.g. distribution, COVID-19 hospitalization rate for 
both treatment groups, and match rate…) 
 
How many controls are targeted before the matching? Would 
suggest to have sample size with the raio of 1:3 between treated vs 
controls.  
 
Commented [JAT12R11]: Added the power analysis from the 
protocol as a new section above (sec 4.3 sample size determination). 
Commented [WWY13]: Suggest to have a separate section of 
sample size.  


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Figure 2. Flow chart of the process of identifying study participants. 
 
 
 
 
 
 
Individuals who meet study 
inclusion/exclusion criteria (UHC members, 
65 and older, who are deemed high risk if 
contracting COVID-19) and created a user 
profile in United in Research platform
Subset of individuals with Bamlanivimab 
drug available in their area, and who 
consented to symptom tracking and getting 
infusion if tested positive
Subset of individuals reporting at least one 
symptom will receive COVID19 test
Subset of individuals with positive test result 
will receive antibody treatment and will be 
tracked for 28 days post-infusion
(Treatment group 7500 individuals)


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4.5 Endpoints 
The following definitions of endpoints hold for all analyses described in this statistical analysis 
plan. For study participants, we define the study endpoint as 28 days after date of the 
Bamlanivimab infusion. This holds for both outcomes of COVID-related hospitalization with or 
without time consideration (i.e., no censoring vs censoring at Day 28). For example, if a study 
participant hasn’t been hospitalized due to COVID-19 within 28 days after COVID-19 diagnosis, 
we’ll treat the binary response of COVID-19 hospitalization as 0 without time consideration. 
However, with time consideration, it becomes right-censored since this participant may be 
hospitalized 29 or more days after COVID-19 diagnosis and our observation period is 28 days 
after COVID-19 diagnosis. More details are described in Section 5.8.1.3. For the pool of 
potential controls, we define the study endpoint as 28 days after a synthetic date of treatment. We 
will randomly select a number from the distribution of number of days after date of symptoms 
onset in study participants to identify an index date with which to define 28 days post infusion 
where we will monitor claims and EMR data for hospitalizations and mortality. 
Figure 3 illustrates the timeline for study participants and controls to ensure alignment on an 
index date for symptoms onset and the follow-up period. Since the control group will not have an 
infusion date associated with them, we will randomly select a number from the distribution of 
number of days after date of symptoms onset in study participants to identify an index date with 
which to define 28 days post infusion where we will monitor claims and EMR data for 
hospitalizations and mortality. 
 
Figure 3. Illustration of the timeline for study participants and controls. 
For patients who do not experience a COVID-19 related hospitalization by Day 28 following the 
date of infusion or synthetic treatment, a censoring date is defined as the earliest of 28 days 
following the date of treatment, or for study participants, the early discontinuation date due to 
lost to follow-up. 
4.6 Study Therapies 
Bamlanivimab (LY3819253) is a neutralizing IgG1 monoclonal antibody (mAb) to the 
spike protein of SARS-CoV-2. It is designed to block viral attachment and entry into human 
cells, thus neutralizing the virus, potentially preventing and treating COVID-19.    
4.7 Variables/Measures 
The primary outcome of interest is COVID-related hospitalization rate in study participants and 
matched controls. We list all potential covariates in two phases. Phase I covers historical data 
and Phase II includes post COVID-19 diagnosis information. Phase I historical data will be 
Commented [WWY14]: Should be the pool of controls which 
was before PS matching. 


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collected for both study participants and matched controls. Phase II post-COVID 19 diagnosis 
data has two parts: data elements to be collected for both study participants and matched controls 
(Phase II.A), and data elements to be collected for only study participants (Phase II.B). 
Phase I - Historical data: Study participants and matched controls:  
1. COVID-19-related information: 
 
Variable  
Data source for study 
participants 
Data source for matched 
controls 
Date of COVID-19 diagnosis 
test 
ProtectWell 
Optum EMR or CDSM 
Type of COVID-19 diagnosis 
test* 
Symptomatic flag during 
COVID-19 test 
ProtectWell (all study 
participants are symptomatic) 
Optum EMR 
Previous COVID diagnosis flag 
CDSM  
Optum EMR or CDSM 
Previous receipt of 
Bamlanivimab 
UHC claims 
Optum EMR or UHC 
Claims 
*: Please see Table A1 in Appendix 1 for LOINC codes of the PCR and antigen COVID-
19 tests. 
 
 
2. Demographic:  
 
Variable  
Data source for study 
participants 
Data source for matched 
controls 
Age as of 2021* 
UHC claims  
 
Optum EMR or UHC 
claims 
 
ZIP code of residence (may or 
may not be used for identifying 
matched controls) 
Urbanization of residence in a 
zip code level (urban, suburban, 
rural) 
Region/State of residence 
Race 
Gender 
Flag determining whether a 
patient resides in a nursing 
facility – determining a patient 
does not live at home 
*: Age = 2021 – birth year. 
 
 
Commented [WWY15]: This variable won’t be used for 
analysis.  
Commented [JAT16R15]: Not necessarily. We’ll use it to 
approximate date of symptoms onset and identify matched controls 
who got COVID-19 around the same time. Please see the comment 
below for more details. 
Commented [WWY17]: Would also include COVID-19 
symptoms, and a timing variable:  duration since symptom onset to 
index date 
 
Commented [JAT18R17]: We will include these data fields if 
available. Regarding date of symptoms onset, it’s approximated to 
be 2 days prior to date of COVID-19 diagnosis (1 day for overnight 
shipping of COVID-19 test kit and the other day for PCR test). 
Commented [WWY19]: As obesity is a high risk factor, suggest 
to include BMI as a covariate in PS matchig 
 
Commented [JAT20R19]: Agreed. We include obesity as a 
comorbidity in 4. Comorbidities in 2019 part below. This 
comorbidity is extracted from our claims data. 
 
It makes perfect sense to include BMI, but we don’t have this 
numerical value from the claims data and have very limited coverage 
from other data sources. 


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3. Insurance:  
 
Variable  
Data source for study 
participants 
Data source for matched 
controls 
Line of business 
UHC claims  
 
Optum EMR or UHC 
claims 
 
Dual-eligibility status 
Continuous member enrollment 
in 2019 
Continuous member enrollment 
in 2020 
Continuous member enrollment 
in 2021 (up to available 
months) 
 
 
4. Comorbidities in 2019:  
 
Variable  
Data source for study 
participants 
Data source for matched 
controls 
Elixhauser score for 
readmission  
UHC claims  
 
Optum EMR or UHC 
claims 
 
Historical flag for all associated 
comorbidities used in 
computing Elixhauser score* 
*: The 29 categories include diabetes, obesity, chronic pulmonary disease, congestive heart 
failure, etc. The full list is in Table A2 of Appendix 1. 
 
5. Immunosuppressive treatment 
 
Variable  
Data source for study 
participants 
Data source for matched 
controls 
History of receiving 
immunosuppressive treatment 6 
months prior to date of COVID 
diagnosis*  
 
UHC claims 
 
 
Optum EMR or UHC 
claims 
 
*: Please see the full list of immunosuppressive prescriptions in Table A3 of Appendix 1. 
 
Phase II.A: Post COVID-19 diagnosis data: Study participants and Matched controls 
 
Variable  
Data source for study 
participants 
Data source for matched 
controls 
Date of COVID-19 admission 
(if hospitalized) 
 
 
 
 
Commented [WWY21]: Will comorbidities be looking only in 
2019? 
Commented [JAT22R21]: Yes, we chose the year 2019 
because the year 2020 is a special year, and people tend to use 
healthcare less than usual. Besides, we assume that comorbidities 
listed here last to the end of our study or even longer given most of 
them are chronic (e.g. T2D, renal failure, congestive heart failure) 
and our study cohort is the Medicare Advantage cohort. 


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ER flag (admitted from ER; if 
data is available) 
 
 
BCRT  
 
 
 
Optum EMR or BCRT 
 
ICU flag (related to ICU 
transfer; if data is available) 
Ventilation flag (use of 
ventilation; if data is available) 
Diagnosis code – primary  
Diagnosis code – secondary  
Diagnosis code – tertiary  
Date of discharge (if applicable) 
Date of death (if applicate) 
 
Phase II.B: Post COVID-19 diagnosis data: Study participants only 
 
Variable  
Data source for study 
participants 
Infusion date 
Optum infusion pharmacy 
 
History of adverse effects (if 
any) 
Details of adverse effect (if any) 
Types of adverse effects (if any) 
Date of experiencing adverse 
effects (if applicable) 
 


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5. Statistical Analyses 
5.1 General Considerations 
Statistical analysis of this study will be the responsibility of the sponsor or its designee. All 
tables, figures, and listings will be created using the data described in Table 1 and Section 4.7 
(unless otherwise noted), including data during study participation. While not reflected in a table, 
figure, or listing, any data collected after study participation (e.g., in the Lilly Safety System or 
collected through queries to the investigator) may be discussed in a clinical study report (CSR) 
or integrated summary document when deemed relevant. Unless otherwise noted, displays will 
include columns for study participants and controls will be displayed.  
Not all displays described in this statistical analysis plan (SAP) will necessarily be included in 
the CSR. Not all displays will necessarily be created as a “static” display. Some may be 
incorporated into interactive display tools instead of, or in addition to, a static display. Any 
display described in this SAP and not provided would be available upon request.  
Treatment differences will be evaluated based on a two-sided significance level of 0.05 for all 
efficacy analyses. Ninety-five percent confidence intervals for the estimated event rate, odds 
ratio/hazard ratio will be provided. While we describe many methods addressing different 
uncertainties, we may not need to perform all analyses mentioned in the draft. Additional 
exploratory analyses of the data will be conducted as deemed appropriate. All statistical analyses 
will be performed using R 3.6 (or a higher version). 
5.1.1 Populations for Analyses  
We define the analysis populations in Table 3 along with the analysis we will conduct. All 
patients meet the study eligibility criteria. Note that all populations for analyses are UHC 
members, and the following proposed analytical methods are for UHC members. We may see 
non-UHC members satisfying the study eligibility criteria, but we cannot obtain or have very 
limited information of their comorbidities, demographic (e.g., area of residence, socioeconomic 
index), and if having immunosuppressive treatment prior to COVID-19 diagnosis. 
Table 3. Population for analyses 
Population  Data for analysis 
Definition/Data 
Study 
participants  
Disposition analysis and 
safety 
Study participants who 
sign informed consent 
and receive 
Bamlanivimab in single 
infusion of 700 mg. 
Pool of 
potential 
controls 
Formation of the control 
group 
Pre-matching 
identification of risk 
factors 
Estimation of prognostic 
scores 
Patients who meet the 
study eligibility criteria 
but did not receive 
Bamlanivimab. 
Commented [WWY23]: Would suggest to revise to include 
general results reporting, definition of the analysis populations, 
analysis methods, definition of baseline ….. Refer to attached 
sample SAP 


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Overall 
population 
Descriptive analyses 
Propensity score matching 
Consists of study 
participants and pool of 
potential controls. 
Propensity 
score 
matched 
population  
Efficacy and descriptive 
analyses 
Subset of the overall 
population consisting of 
study participants and 
matched controls after 
propensity matching. 
Double 
score 
(propensity 
score and 
prognostic 
score) 
matched 
population 
Sensitivity analyses 
Subset of the overall 
population consisting of 
study participants and 
matched controls after 
double matching. 
 
5.2 Adjustment for Covariates 
Unless otherwise specified, for the efficacy analysis, when logistic regression model or Cox 
proportional hazards model are used, the model include treatment group, age, gender, and all risk 
factors as part of the qualification to enter the study. Any additional variables found to be risk 
factors for the COVID-19 related hospitalization, per the regression model in Section 5.8, will 
also be included. The final list of variables will be confirmed with the study team prior to 
initiating the outcome analyses. 
5.3 Multiple Comparisons/Multiplicity 
All analyses will be conducted at the two-sided 0.05 significance level without adjustments for 
multiplicity. 
5.4 Participant Dispositions – Study Participants Group Only 
The number and percentage of study participants who complete the study or discontinue early 
will be tabulated by 28 days. Here, we consider that the subset of study participants for whom 
outcome data can be obtained through UHC claims, Optum EMR, or Optum infusion pharmacy 
data sources during the 28 days post-infusion time period are completing the study, whereas 
remaining subset of study participants for whom outcome data cannot be confirmed by any of 
these data sources during the 28 days post-infusion time period are discontinuing early. All 
patients who enter the study and discontinued from the study will be listed, and if available, the 
timing (from receiving study treatment) and reason of discontinuing the study will be reported.  
Commented [XM24]: The number and percentage of study 
participants who complete the study or discontinue early will be 
tabulated by 28 days. Reasons for discontinuation will be 
summarized. 
All patients who enter the study and discontinued from the study 
will be listed, and the timing of discontinuing (from receiving study 
treatment) the study will be reported. If known, a reason for their 
discontinuation will be given. 
In addition, a graphical summary (i.e., KM plot) of time from study 
treatment to early permanent discontinuation of study due to AEs 
may be generated if there are a substantial number of such events.  
 
Commented [XM25]: Please add sections: 
•Multiple Comparisons/Multiplicity 
•Patient Characteristics  
•Prior Medication and Concomitant Therapy 
•Protocol Violations 
Commented [WWY26R25]: Refer to attached sample SAP 
Commented [JAT27R26]: Added the new section 5.7. 


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5.5 Patient Characteristics 
Patient demographic variables and baseline characteristics (listed in Section 4.7) will be 
summarized by participant status and the overall population. The continuous variables will be 
summarized using mean with standard deviations or median with interquartile range, and the 
categorical variables will be summarized using frequency count and percentage. Difference 
between study participants and controls will be tested by t-test or Wilcoxon rank sum test for 
continuous variables and chi-squared test for categorical variables. Baseline characteristics 
included in the propensity score model and prognostic score model will be summarized for the 
propensity matched population and double score matched population. 
5.6 Treatment Compliance 
Participants will receive study intervention directly from the investigator or qualified designee, 
under medical supervision. The date of infusion will be recorded in the source documents and 
recorded in the CRF. Treatment compliance will not be reported. 
5.7 Prior Medication and Concomitant Therapy 
According to the EUA, we consider immunosuppressive medications 6 months prior to date of 
COVID-19 diagnosis as a potential risk factor of COVID-19 hospitalization. Additional 
descriptive summaries on prior medications and concomitant therapies could be provided for 
study participants and matched controls if available. 
5.8 Primary Analysis 
The main objective is to determine the incidence of COVID-related hospitalization at Day 28 
among Bamlanivimab-treated participants relative to external controls. We separate the primary 
analysis into three parts – identify risk factors of COVID-related hospitalization as matching 
covariates, match controls for study participants, and compare the COVID-related hospitalization 
rate between study participants and matched controls. 
5.8.1 Main analytical approach 
5.8.1.1 
Pre-matching identification of risk factors as matching covariates 
 
Before matching, we will select covariates to be included in the propensity score estimation 
model. Covariates that are predictive of both treatment status and the outcomes and covariates 
that are associated with the outcomes will be selected to estimate propensity score. To do so, we 
will first conduct a literature review to identify the risk factors that are associated with COVID-
19 hospitalization. The findings from the literature review will help create a list of variables on 
which treatment and control groups will be matched. 
Consider a binary response taking a value of 1 if a patient is hospitalized due to COVID-19, and 
0 if a patient is tested positive for SARS-CoV2 but does not have a record of subsequent 
COVID-related hospitalization (hospitalization within 38 days of a positive test). We include risk 
factors such as demographic (e.g., gender, age, race, geographical area such as state or county), 
comorbidities (Elixhauser scores related comorbidities such as diabetes, metastatic cancer, heart 
diseases, obesity, coagulopathy, and so on), and other potential clinical attributes as available in 


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the identification of the appropriate matched cohort. These risk factors will also include all 
clinically identified variables by Eli Lilly to define moderate and high-risk groups. We will run 
this step three times with three datasets; specifically, we consider: 
a. Iteration 1: This is based on retrospective data available in-house consisting of 
Medicare Advantage (MA) members, aged greater than or equal to 65, and COVID 
diagnosed. We will exploit BCRT, CDSM, and UHC Claims data sources. The 
study cohort here is a broader population than the study participants.   
b. Iteration 2: This is solely based on data associated with study participants. We will 
exploit risk factors from UHC Claims and study enrollment information from 
Optum infusion pharmacy data.   
c. Iteration 3: This is based on information of both the general MA population and 
study participants –retrospective and prospective data in Iteration 1 and Iteration 2, 
respectively. We will exploit COVID labs and hospitalizations from BCRT and 
CDSM, risk factors from UHC Claims data, and study enrollment information 
from Optum infusion pharmacy data.  
We will identify the covariates that are consistent across all three iterations and select the final 
set based on data, literature, and clinician decision. The main objective of this initial iteration 
step is to reduce dimension, exclude noise variables, include clinically meaningful variables, and 
lay out the foundation for the subsequent analyses. We will develop a generalized linear (mixed) 
model (Nelder, 1972) (McCulloch, 2014) with a logit link function to quantify the association 
and make inferences about the parameter estimates based on the retrospective data. The 
appropriateness of using random effects (e.g., region or state of residence) will be evaluated by 
the likelihood ratio test (LRT) and summary information criteria (e.g., conditional or marginal 
Akaike information criteria (AIC)). Model performance will be evaluated by conditional 
(pseudo) R-square, Somers’ Dxy, and C-statistic. We will select influential risk factors in the 
light of both clinical relevance and statistical significance. In this pursuit, we will use GL(M)M 
with a Least Absolute Shrinkage and Selection Operator(LASSO) (Tibshirani, 1996) to develop 
a parsimonious model; here, the optimum parameters will be selected by k-fold cross-validation 
with the 1-standard-error rule. We will also check the statistical significance of any association 
based on Wald test statistic between each covariate and response based on univariate and 
multivariate GL(M)M; a pre-determined threshold of, say 0.10 (level of significance), will be 
used to select covariates. This sequential inferential approach (i.e., Wald) is supplementary to the 
regularization step (i.e., GL(M)M-LASSO), and the final set of covariates will be determined by 
exploiting both/either approaches and will be combined with clinical relevance.  
 
5.8.1.2 
Propensity score matching  
Objective:  
Data Variables:  
Data Source:  
Construct a control group 
for study participants 
• Flag of infusion treatment  
• Symptomatic flag  
• Demographic  
• Elixhauser conditions 
 
• Optum infusion pharmacy 
data  
• UHC claims data  
• CDSM   
• BCRT   
Commented [XM28]: Which variables will be considered as 
random variables? 
Commented [JAT29R28]: Added in the main texts. 
Commented [XM30]: Using R or SAS? 
Commented [JAT31R30]: R. Also added software information 
in Section 5.1, General Considerations. 


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• Optum EMR data 
 
The objective of propensity score matching is to ensure balance between the treatment and 
control group on the selected covariates. Propensity score matching will be conducted after the 
completion of treatment enrollment (28-day study period) and upon the availability of 
information of study participants. Data sources of information for calculating the propensity 
scores for study participants will be Optum infusion pharmacy data, UHC claims data, and 
CDSM data. Note for the matched controls we will select them from Optum EMR+claims 
dataset, we will include only members who are COVID diagnosed, have documented symptoms 
of COVID-19, meet the inclusion/exclusion criteria for the study, and have historical information 
about selected risk factors (either through claims or EMR).    
According to the current study eligibility criteria in Section 4.4.1, the treatment group consists of 
both UHC and non-UHC members. While UHC patients with continuous enrollments have 
pertinent historical information (i.e., comorbidity, demographic), accruing such information 
might be challenging and is likely unavailable for non-UHC members. We will first perform 
matching for the UHC-treated members with complete information. Next, we will exclude the 
selected matched-control individuals for each UHC-treated from the control set. The non-UHC 
patients will be matched sequentially case-by-base from the remaining control group for each 
outstanding treated participant using “exact” matching method based on available information. 
Note that, such exact matching for patients without relevant comorbidity variables may result in 
sub-optimal match. 
The following description of matching methods is for UHC members in the treatment group. We 
will apply propensity score matching techniques described in (Guo, 2014), and fit a GLM using 
matching covariates as selected risk factors of COVID-19 hospitalizations from Section 5.8.1.1 
and response as the log odds of receiving treatment with a logit link, and estimate balancing (i.e., 
propensity) scores which are next to be used in finding the matched pair between treatment and 
control group. Model performance will be evaluated by pseudo R-square values (e.g., 
McFadden) and Hosmer-Lemeshow test. We will use nearest neighbor matching with a caliper 
which its size will be determined empirically. Note this matching model is different from 5.8.1.1 
in terms of response (hospitalization flag vs receiving treatment). We will evaluate balance in 
covariates between study participants and matched controls by absolute standardized difference 
in mean (Austin, 2009) and variance ratio of propensity score and covariates since it is more 
robust in terms of sample size and covariate distribution requirements in comparison to other 
balance diagnostics (Ali, 2016). We will also conduct a hypothesis test to examine if a 
significant difference in matching covariates exists between treatment and control groups, such 
as Wilcoxon rank-sum (Mann-Whitney) test or t-test for continuous variables and Chi-squared 
test for categorical variables. We will use these tests to evaluate covariate balances before and 
after matching and repeat the processes if covariate imbalances remain.  
We will consider nearest neighbor matching within a caliper (Smith, 2005), and obtain 1-to-1 
match. . As an alternative approach, optimal matching technique may also be adopted, in the 
event that the covariate imbalances persist using the prior methods. In addition, greedy matching 
methods make decisions about inclusion of a pair of treated-control participants as a matched set 
sequentially; here decisions are made one at a time without reconsidering early decisions as later 
ones are made. From this point of view, such mechanism is not optimal and therefore optimal 
Commented [XM32]: Please describe in detail on how to 
estimate PS. Use the variables selected from 5.3.2.1 or redo the 
model selection? 
Commented [JAT33R32]: Variables selected from the previous 
section. Added this specification in the second sentence of this 
paragraph. 
Commented [WWY34]: Suggest to be more specific for what 
will be test for non-parametric method 


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matching based sensitivity analyses will be performed to assess the numerical performances. In 
addition, propensity score subclassification (Rosenbaum, 1984) in conjunction with trimming 
strategy (Crump, 2009) will be adopted. Such greedy matching methods require a sizable (e.g., 
70%) common support region for logit between treated and control, known as “overlap 
assumption”. It is possible that greedy matching excludes participants because treated cases fall 
outside the lower end of the common support region (those who have low logit) and nontreated 
cases fall outside the upper end of the common support region (those who have high logit). This 
can be investigated once we fit PSM model, estimate the logits, and plot them side-by-side for 
treated and control. If we see significant nonoverlap then we may need to try alternative such as 
trimming method.  
One of the key limitations is - covariates in the propensity model may not be statistically 
significant resulting in suboptimal probability estimation of treatment assignments (or propensity 
scores).   
5.8.1.3 
Primary efficacy outcome analysis: COVID-related hospitalization rate at 
Day 28 
Objective:  
Data Variables:  
Data Source:  
Compare and determine if 
there is a significant 
difference in the rate of 
COVID-19 related 
hospitalization between 
study participants and 
matched controls 
• Flag of COVID-19 related 
hospitalization through 
Days 1-28 
• Flag of infusion treatment  
• Demographics  
• Elixhauser conditions  
  
• Optum infusion pharmacy 
data  
• UHC claims data  
• CDSM COVID labs data  
• BCRT COVID 
hospitalizations data 
• Optum EMR data  
 
 
Understanding the COVID-related hospitalization rate at Day 28 (binary endpoint) between 
study participants and matched controls is the key primary objective. We consider the analysis in 
combinations of with/without time-related outcome and with/without covariate adjustment. A 
single model will be selected and documented prior to accessing outcome data with the focus of 
including covariates expected to be strongly related to outcome based on other studies. 
First, consider the simplest scenario without covariates adjustment and time-related outcome; we 
will use nonparametric McNemar’s test to compare the hospitalization rate at Day 28 between 
the treatment and control groups at the level 0.05. Separately, for those who are not admitted due 
to COVID at Day 28, we will treat their non-hospitalization as a censored response after Day 28, 
instead of assuming no COVID hospitalizations after Day 28. Without covariates adjustment, we 
will draw Kaplan-Meier curves and perform log-rank test to compare the equality of curves 
implying similar cumulative incidence of hospitalization between the treatment and control 
groups over 28-day period.   
Next, consider covariates adjustment, this will allow us to compare hospitalization rate between 
treatment and control groups given a subset of matching covariates or covariates not included in 
matching. Without any covariate adjustments, treatment effect might be subject to potential 
confounding variables. We will construct a multiple generalized linear model with fixed or 
Commented [XM35]: Please be specific which endpoint will be 
used for the primary analysis binary or time-to-event? 
Commented [JAT36R35]: Specified in Section 4.5 Endpoint. 
Commented [TEE37R35]: Added in parenthesis to further 
clarify in first sentence. 
Commented [WWY38]: If add a table of Populations for 
Analyses, this table may not need (see attached sample SAP) 
 
Commented [JAT39R38]: I added the table of populations for 
analyses in Section 5.1.1. but still want to keep the table with 
objective, data variables and data source here since they contain 
different information. 
Commented [XM40]: Please specify the link, dist 


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mixed effects  and a logit link to examine if the hospitalization rate differs between study 
participants and matched controls after adjusting demographics (e.g., age, gender, etc.) and 
comorbidities (e.g., Elixhauser conditions). The appropriateness of using mixed model will be 
determined by information criteria (AIC or Bayesian information criteria (BIC)) values. 
Considering the time component in the response, we will use Cox proportional hazard model, 
accelerated failure time model and other survival models. Proportionality assumption of the Cox 
model will be checked by Schoenfeld residuals. Method selection will depend on if model 
assumptions hold in our data.  
In the short term, we will use Optum infusion pharmacy data and BCRT data to identify 
hospitalization among the treatment group; and we will use UHC claims data and Optum EMR 
data for inpatient records for identification of COVID-related hospitalizations among the control 
group. We will evaluate treatment effects based on a two-sided test with a level of 0.05 for all 
efficacy analyses, and provide 95% confidence intervals for the odds ratio/hazard ratio.  
5.8.2 Sensitivity Analyses 
The validity of all the above analyses relies on the assumption of ‘no unmeasured confounding’. 
That is, the identification of average treatment effects in observational studies is achieved by 
assuming that the correct set of confounders has been measured and properly included in the 
relevant models. Thus, sensitivity analysis for the potential impact of unmeasured confounding is 
important as the assumption of no unmeasured confounding cannot be completely verified. In 
this study, potential unmeasured confounders include the frequency of interaction with clinicians 
(e.g., doctors or nurses), viral load (e.g., cycle threshold value from polymerase chain reaction 
tests), COVID-19 variants (e.g., B.1.1.7 and B.1.351 first identified in the United Kingdom and 
South Africa, respectively), lifestyle patterns (e.g., activity level or diet pattern), psychosocial 
factors (e.g., a patient’s psychological reaction when facing COVID-19 and its interaction with 
the disease progression, or if a patient has the support network such as family and friends), etc. 
Assuming it is not possible to obtain information on specific unmeasured confounders, 
plausibility/one-number summary methods, such as the E-value, may be performed as an initial 
assessment of the robustness of the primary analysis findings to the potential for unmeasured 
confounding. Specifically, the E-value (VanderWeele, 2017) and the proportion of unmeasured 
confounding (Bonvini, 2020) may be used to evaluate the robustness of the primary objective in 
our study (i.e., the difference in COVID-19 hospitalization during Days 1 to 28 analysis between 
matched study participants and matched controls). 
The E-value is defined as “the minimum strength of association, on the risk ratio scale, that an 
unmeasured confounder would need to have with both the treatment and the outcome to fully 
explain away a specific treatment-outcome association, conditional on the measured covariates.” 
E-values can be based on the adjusted treatment effect estimate or the confidence limit endpoint 
of interest. 
Secondly, the proportion of unmeasured confounding is a method built upon a mixture model for 
confounding. The authors conceptualize that an unknown fraction (𝜀) of the units in the sample is 
arbitrarily confounded while the rest is not. This framework yields the one-number summary of a 
study’s robustness (i.e., the proportion of unmeasured confounding): the minimum proportion of 
confounded units such that bounds on the average treatment effect contain zero. The proportion 
Commented [XM41]: What’s the possible random variable? 
Commented [JAT42R41]: Region or state of residence. 
Commented [XM43]: Please specify 
Commented [XM44]: Describe in detail 
Commented [JAT45R44]: I’m not sure what details are desired 
here. This sentence is for selection of Cox PH and AFT models, and 
the previous sentence mentions we’ll check Schoenfeld residuals for 
the proportionality assumption of the Cox model. 
Commented [XM46]: Would add a paragraph “Treatment 
effects will be evaluated based on an two-sided significance level of 
0.05 for all efficacy analyses. Ninety-five percent confidence 
intervals (CIs) for the OR/HR will be provided.” 
Commented [WWY47]: Suggest to add double score matching 
or prognostic score matching (PrSM) as sensitivity analyses 
Commented [JAT48R47]: Added, please see below. 


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of unmeasured confounding 𝜀 ranges from 0 to 1 (i.e., 0% to 100%). In general, a study is more 
robust when 𝜀 is bigger because we need more unmeasured confounding to alter the conclusion 
of treatment effect, and computation can be done in the sensitivitypuc package in R. Of course, 
there is no universal threshold to determine if a study is robust enough. This decision making 
largely depends on subject-matter knowledge to determine if the unmeasured confounding is 
serious enough to affect the conclusion under the assumption of no unmeasured confounding. 
Given the results of the initial assessment of unmeasured confounding sensitivity, further 
analyses may or may not be conducted. For instance, negative control outcomes, treatment 
comparisons, or perturbation variables are other approaches which could be utilized. See Zhang 
and colleagues (Zhang, 2018) for a further set of unmeasured confounding sensitivity analysis 
methods. 
In addition, we will use double score matching (Yang, 2020), where matched controls are 
identified using both propensity and prognostic scores, rather than the propensity score only. We 
want to emphasize that the model-based matching (i.e., propensity and dual matching) are only 
applicable for UHC members; we will not have information available for all prognostic factors to 
calculate model-driven scores for non-UHC members. We will compute the propensity score as 
described in Section 5.8.1.2. The prognostic score is defined as the predicted outcome under the 
control group. It is estimated by modeling the COVID-19 related hospitalization using only the 
pool of potential controls, and then using the model to obtain the prediction of potential 
outcomes for the treatment group. Aside from the modeling method (logistic regression) 
described in Section 5.8.1.1, we also consider ensemble-based machine learning models (e.g., 
gradient boost, random forest, bagging, and support vector machine). We randomly split the 
historical cohort data (i.e., dataset described in Section 5.8.1.1 iteration 1) data into a training 
and test set (i.e., 80%-20%) ensuring the same proportion of cases (i.e., the observed number of 
hospitalization in control group divided by the total number of COVID cases) in each cohort. 
Next, we develop the models based on the training set, and evaluate their performances on the 
test set. We repeat the process 100 times, report the average classification metrics, and select the 
model that provides best numerical performances in terms of the prediction accuracy of the test 
set (e.g., F1 score, Area under the curve (AUC) of a receiver operating characteristic (ROC), 
balanced accuracy, and positive-predictive value (PPV)). Next, we exploit the full data set 
(without splitting) to train the final model which is to be used to generate prognostic scores for 
both treated and potential control participants. We will repeat the primary analysis using the 
double-score-matched study participants and controls. An R-package “dsmatch” will be used to 
perform matching and assess treatment efficacy after calculating propensity and prognostic 
scores. 
5.8.3 Handling of missing data or dropouts 
Other potential sources of bias are missing data, selection bias, and measurement error. We 
perform the analyses under the assumption of missing (completely) at random. We exclude the 
cases with missing features if the number of missing subjects is less than 5% of total sample size. 
Otherwise, we impute values using multivariate imputation by chained equations (MICE) 
algorithm (Van Buuren, 2018). 
The effect of selection bias will be attenuated by appropriately selecting control subjects (who to 
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Systematic bias arising from socio-economic factors will be mitigated by adjusting for various 
demographic, economic, and societal variables. Measurement errors if there are any, would be 
considered as random noises, and will be negligible.  
5.9 Exploratory Analyses 
Objective:  
Data Variables:  
Data Source:  
Identify COVID-related 
mortality at Day 28 in 
study participants and 
matched controls  
• Flag of infusion treatment  
• Demographics  
• Elixhauser conditions  
• Symptoms 
  
• Optum infusion pharmacy 
data  
• UHC claims data  
• CDSM COVID labs data  
• BCRT COVID 
hospitalizations data 
• Optum EMR data  
 
 
The exploratory objective is to determine the incidence of COVID-related mortality at Day 28 
among Bamlanivimab-treated patients relative to external controls. We will perform descriptive 
analysis and report summary statistics if there will be COVID deaths in study participants and 
matched controls. 
5.10 Safety Analyses 
The two safety objectives are: 
1. To describe the incidence of infusion reactions during receipt of infusion and during the 
defined infusion follow-up period (follow-up period to be consistent with EUA fact sheet for 
healthcare providers) 
2. To describe the incidence of patient reported adverse event (AE) outcomes through Day 28  
Objective:  
Data Variables:  
Data Source:  
Identify any infusion 
reactions during receipt of 
infusion and during the 
infusion follow-up period   
• Infusion reactions 
• Date of infusion treatment  
• Date of infusion reactions 
of interest  
• Demographics  
• Elixhauser conditions  
 
• Optum infusion pharmacy 
data  
• UHC claims data  
 
 
Clinicians will start with qualitative analysis a case by case. We will report descriptive statistics 
according to the data we will obtain. If we have a sufficient size of study participants 
with particular infusion reactions, we can calculate descriptive statistics, run Chi-square 
independence test and Kruskal-Wallis test to examine if infusion reactions vary from 
demographics or comorbidities.  
Commented [WWY49]: May not need this table.  
Commented [WWY50]: This section should add such as 
Reporting by System Organ Class (SOC) and PT. Please refer to the 
sample SAP 
 
Commented [JA51R50]: We’re checking internally and will 
keep you posted. 


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Objective:  
Data Variables:  
Data Source:  
Identify any self-reported 
adverse events between 
receipt of infusion and day 
28 from study participants  
• Adverse events 
• Date of infusion treatment  
• Date of infusion reactions 
of interest  
• Demographics  
• Elixhauser conditions  
 
• Optum infusion pharmacy 
data  
• UHC claims data  
 
 
We will perform the same methodology described above given the AEs and serious AEs defined 
in Section 5.10.1.2. 
5.10.1.1 
      Extent of Exposure  
Exposure to therapy will be represented as the total number of complete and incomplete 
infusions, and will be summarized using descriptive statistics. 
5.10.1.2 
Adverse Events 
The current list of AEs comes from the Phase 2 trial of ambulatory participants and a reasonable 
anticipation of serious AEs for this study.  
The most commonly reported AEs from the Phase 2 trial include: 
• Nausea 
• Diarrhea 
• Dizziness 
• Headache 
• Pruritus 
• Vomiting 
The reasonably anticipated serious AEs include: 
• Secondary infection – pneumonia, sepsis 
• Respiratory failure requiring non-invasive or invasive mechanical ventilation – acute 
respiratory distress syndrome (ARDS) 
• Major acute cerebrocardiovascular event – cardiovascular death, myocardial infarction, 
hospitalization for unstable angina, hospitalization for heart failure, serious arrhythmia, 
resuscitated sudden death, cardiogenic shock due to myocardial infarction, coronary 
revascularization procedure, neurologic stroke, and peripheral vascular events 
• Coagulopathy 
• Acute kidney injury – renal replacement therapy 
• Multi-organ dysfunction syndrome 
Commented [ATJ52]: To Lilly: I’m not sure what requires in 
this section and if we need it. 
Commented [XM53]: Exposure to therapy will be represented 
as the total number of complete and incomplete infusions, and will 
be summarized using descriptive statistics.  
Commented [JAT54R53]: Thanks. Added the texts. 


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5.11 Protocol Violations 
Protocol deviations will be identified throughout the study. Important protocol deviations (IPDs) 
are defined as those deviations from the protocol that would potentially compromise participants’ 
safety, data integrity, or study outcome. 
The number and percentage of participants having IPDs will be summarized within category and 
subcategory of deviations. A by-patient listing of IPDs will be provided. 
5.12 Other Analyses 
 
Objective:  
Data Variables:  
Data Source:  
Evaluate the association of 
the time gap between a 
COVID positive test result 
and an infusion with 
COVID hospitalization  
Evaluate the association of 
the time gap between a 
symptom onset and an 
infusion with COVID 
hospitalization 
• Date of symptoms onset 
• Date of COVID diagnosis 
• Date of infusion treatment  
• Date of COVID 
hospitalization 
• Demographics  
• Elixhauser conditions  
 
 
• Optum infusion pharmacy 
data  
• UHC claims data  
• CDSM COVID labs data  
• ProtectWell data 
 
 
There are two pre-specified analyses we will pursue aside from the primary, exploratory and 
safety objectives in the previous sections.  
First, we want to understand if the timing of the infusion relative to a positive COVID test, 
impacts hospitalizations. We will compute the Cramer’s V and phi coefficient as one-number 
summary of correlation between two categorical variables when we group time gap between date 
of COVID positive and date of home infusion. If considering this time gap as a continuous 
variable, we will run a simple logistic regression model to examine if time gap is associated with 
the hospitalization rate at Day 28. Then, we will apply the same methodology described above 
for categorical and continuous covariates and binary outcomes.  
Second, we want to understand if the timing of the infusion relative to a symptom onset, impacts 
hospitalizations. We will compute the Cramer’s V and phi coefficient as one-number summary 
of correlation between two categorical variables when we group time gap between date of 
COVID positive and date of home infusion. If considering this time gap as a continuous variable, 
we will run a simple logistic regression model to examine if time gap is associated with the 
hospitalization rate at Day 28. Then, we will apply the same methodology described above for 
categorical and continuous covariates and binary outcomes. 


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5.13 Interim Analyses  
We plan to report descriptive statistics when we get the first 1,000 study participants and then 
conduct the primary analysis when we obtain the data through Day 28 for these study 
participants.  
5.14 Changes to Protocol-Planned Analyses 
NA. 


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6. Supporting Documentation 
6.1 Appendix 1: Definitions and Medical Codes 
1. COVID-19 tests: We list all LOINC codes of PCR or antigen COVID-19 tests we use to 
extract test result. 
Table A1. LOINC codes of PCR/antigen COVID-19 tests. 
Test type (PCR or antigen) 
LOINC code 
Antigen 
95209-3 
94558-4 
PCR 
94759-8 
94534-5 
94559-2 
94316-7 
94565-9 
94533-7 
94819-0 
94760-6 
95425-5 
95608-6 
94639-2 
94756-4 
94315-9 
94309-2 
94500-6 
94308-4 
95406-5 


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94306-8 
94531-1 
95422-2 
95423-0 
95380-2 
94532-9 
94502-2 
 
2. Elixhauser conditions: We list the 29 Elixhauser conditions in Table A2. The crosswalk of 
each condition and ICD-10 codes is on the AHRQ website. We follow the AHRQ’s 
definition to calculate the Elixhauser comorbidity score (Moore, 2017). 
Table A2. The 29 Elixhauser conditions. 
Elixhauser condition 
Acquired immune deficiency 
syndrome 
Lymphoma 
Alcohol abuse 
Fluid and electrolyte disorders 
Deficiency anemia 
Metastatic cancer 
Rheumatoid arthritis/collagen 
vascular diseases 
Other neurological disorders 
Chronic blood loss anemia 
Obesity 
Congestive heart failure 
Paralysis 
Chronic pulmonary disease 
Peripheral vascular disorders 
Coagulopathy 
Psychoses 
Depression 
Pulmonary circulation disorders 
Diabetes, uncomplicated 
Renal failure 
Diabetes with chronic 
complications 
Solid tumor without metastasis 


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Drug abuse 
Peptic ulcer disease excluding 
bleeding 
Hypertension (combine 
uncomplicated and complicated) 
Valvular disease 
Hypothyroidism 
Weight loss 
Liver disease 
 
 
3. Immunosuppressive prescriptions: We identify immunosuppressive treatment by the 
following prescriptions in our claims data. 
Table A3. The list of immunosuppressive prescriptions. 
Immunosuppressive prescriptions 
Cyclosporine 
Abatacept 
Tacrolimus 
Etanercept 
Sirolimus 
Adalimumab 
Everolimus 
Infliximab-abda 
Mycophenolate mofetil 
Infliximab-dyyb 
Mycophenolate sodium 
Infliximab 
Methotrexate 
Sulfasalazine 
Azathioprine 
Hydroxychloroquine sulfate 
Cyclophosphamide 
Certolizumab pegol 
Leflunomide 
Golimumab 
Anakinra 
Tocilizumab 
  
 
 


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6.2 Appendix 2: CRF forms or questionnaires  
1. Please see the CRFs as attached. Both are used as part of the clinical review of the 
participants. The Bamlanivimab IND RPh Admit is the pharmacy documentation; the 
Bamlanivimab IND Admission is the in-home nursing assessment in regards to monitoring 
the participant throughout the infusion and afterward; the Bamlanivimab Post Infusion 
collects clinical information if a study participant has symptoms, infusion reaction or adverse 
events. 
Bamlanivimab IND 
RPh Admit_v3d 20210      
Bamlanivimab IND 
Admission_v7d 20210      
Bamlanivimab Post 
Infusion_v4 20210120 
2. Please see the informed consent form as attached. 
      
Lilly Pragmatic 
Trial_Informed_Conse 
3. Please see the questionnaire of symptom checker and clinical action below. 
--------------------------------------------------------------------------------------------------------------------- 
Participant ID:  
Assessment Date: (DD/MMM/YYYY) 
 
High priority  
Cough – requires immediate medical attention if severe symptoms reported  
○ Yes 
○ Mild 
○ Moderate 
○ Severe 
○ No (Absent) 
 
High priority  
Shortness of breath – requires immediate medical attention if moderate to severe symptoms 
reported 
○ Yes 
○ Mild 
○ Moderate 
○ Severe 
○ No (Absent) 


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High priority  
Feeling feverish – requires immediate medical attention if high fever is reported. For 
moderate fever, clinical call required 
○ Yes 
○ Low grade (<100 degrees F) 
○ Moderate – (100-102 degrees F) 
○ High (anything above 102 degrees F) 
○ No (Absent) 
 
High priority  
Chills/Shaking – requires immediate medical attention if severe symptoms reported (severe 
symptoms being “shaking chills” – i.e. teeth chattering, body shakes) 
○ Yes 
○ Mild 
○ Moderate 
○ Severe 
○ No (Absent) 
 
High priority  
New symptom of loss of sensation in one or more extremities  – (requires immediate 
medical attention if “completely unable to feel an extremity” is reported) 
• Yes 
• No 
• Completely unable to feel an extremity 
 
High priority  
New symptom of loss of strength in one or more extremities – (requires immediate medical 
attention if “completely unable to move one or more extremities” is reported) 
• Yes 
• No 
• Completely unable to move one or more extremities  
Body aches and pain – requires immediate 
medical attention if severe symptoms 
reported 
Headache  
○ Yes 


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○ Yes 
○ Mild 
○ Moderate 
○ Severe 
○ No (Absent) 
○ Mild 
○ Moderate 
○ Severe 
○ No (Absent) 
 
Fatigue  
○ Yes 
○ Mild 
○ Moderate 
○ Severe 
○ No (Absent) 
Loss of taste  
○ Yes 
○ No 
 
Sore throat  
○ Yes 
○ Mild 
○ Moderate 
○ Severe 
○ No (Absent) 
Loss of smell  
○ Yes 
○ No 
 
Loss of appetite  
○ Yes 
○ Mild 
○ Moderate 
○ Severe 
○ No (Absent) 
 
Are you experiencing any additional symptoms/health issues beyond what was just asked 
above? 
○ Yes 
○ No 
If yes – please explain:  
 
Overall, how bad are your symptoms 
TODAY?  
○ No symptoms 
Overall, how is your general physical 
health TODAY? 
○ Poor 


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--------------------------------------------------------------------------------------------------------------------- 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
○ Mild 
○ Moderate 
○ Severe 
○ Very severe 
 
○ Fair 
○ Good 
○ Very good 
○ Excellent 


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6.3 Appendix 3: Mock TFLs 
Tables: 
Table A4. Characteristics of study participants and the pool of potential controls. For notation, n 
indicates the cohort size (i.e., number of subjects); IQR is interquartile range; SD is standard 
deviation; % indicates percentage. 
Characteristics Study 
participants 
(n = ) 
Pool of potential 
controls  
(n = ) 
p-value 
(study 
participants 
vs pool of 
potential 
controls) 
Age: median (IQR) or 
mean (SD) 
 
 
 
Age: n (%) 
65- 
65 – 69 
70 – 74 
75 – 79 
80+* 
*: We can separate 80+ 
into finer groups as 
needed. 
 
 
 
Gender: n (%) in 
female 
 
 
 
Race: n (%) 
White  
Black 
Asian 
Hispanic 
Other 
 
 
 
Urbanization of 
residence: n (%) 
Urban 
Suburban 
Rural  
 
 
 
Commented [XM55]: Please add footnotes for each table, such 
as abbreviation, test for p-value, model specification and locations 
for: 
Dataset: dataset location/dataset name.sas7bdat 
Program: program location/program name.sas             
Output: output location/output name.rtf 
…. 
Commented [XM56]: Would separate this table into two tables 
– before and after PS match, since there may be study participants 
could not find matched control  


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Socioeconomic index: 
median (IQR) or mean 
(SD) 
 
 
 
Dual status: n (%) 
With Medicaid 
Elixhauser 
comorbidity score: 
median (IQR) or mean 
(SD) 
 
 
 
Elixhauser 
conditions*: n (%) 
*: There are 29 conditions 
in total as shown in Table 
A1. 
 
 
 
Immunosuppressive 
treatment 6 months 
prior to date of 
COVID diagnosis: n 
(%) 
 
 
 
Stay in nursing 
facility during 
COVID test*: n (%) 
*: This availability 
depends on the claims lag. 
We may have partial 
information from the 
BCRT data but it’s only 
COVID related and 
doesn’t cover a stay of 
nursing facility due to 
other reasons. 
 
 
 
COVID-related 
hospitalization during 
Days 1-28 after the 
treatment (i.e.,  
Bamlanivimab 
infusion for study 
participants, synthetic 
treatment for control 
candidates and 
matched controls): n 
(%) 
 
 
 


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Dataset: dataset location (data table name with the corresponding database in Google BigQuery) 
Program: program location/program name.R 
Output: output location/output name.csv or .xlsx 
 
Repeat Table A4: 
Table A4.2 
   Patient demographics and other baseline characteristics 
   Propensity score matched population 
Programming notes:  
Replace “study participants” with “study participants after propensity score matching” and 
replace “the pool of potential controls” with “matched controls” in the header.  
 
Table A5. Risk factors of COVID hospitalization for the UHC members satisfying the eligibility 
criteria of this study, study participants and the two cohorts together. For categorical variables 
(e.g., age, gender, race etc.), we will use the category with the largest size as the baseline. Note 
that the variables listed below are what we plan to use, and it’s subject to change when we 
present the final results according to model performance via likelihood ratio test and AIC. 
Characteristics 1: Selected 
UHC members 
Test statistic 
(p-value) or 
Odds ratio 
(95% CI) 
2: Study 
participants 
Test statistic 
(p-value) or 
Odds ratio 
(95% CI) 
3: Selected 
UHC members 
+ study 
participants  
Test statistic 
(p-value) or 
Odds ratio 
(95% CI) 
Significance in 
which cohort with 
level = 0.1 
Age (continuous or 
categorical): 
65- 
65 – 69 
70 – 74 
75 – 79 
80+* 
*: We can separate 80+ 
into finer groups as 
needed. 
 
 
 
1, 2, 3 or 
1 or 
1, 3 
Gender  
 
 
 
Commented [JAT57]: I made this up. It just describes what to 
expect in this column. 


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Race:  
White  
Black 
Asian 
Hispanic 
Other 
 
 
 
 
Urbanization of 
residence: 
Urban 
Suburban 
Rural  
 
 
 
 
Socioeconomic index  
 
 
 
Dual status 
With Medicaid 
Elixhauser 
comorbidity score 
 
 
 
 
Elixhauser conditions* 
*: There are 29 conditions 
in total as shown in Table 
A1. 
 
 
 
 
Immunosuppressive 
treatment 6 months 
prior to date of 
COVID diagnosis 
 
 
 
 
Dataset: dataset location (data table name with the corresponding database in Google BigQuery) 
Program: program location/program name.R 
Output: output location/output name.csv or .xlsx 
 
Table A6. Evaluation of the risk factor models of COVID-19 hospitalizations. Assume that the 
number of models presented is M. 
Statistics 
Model 1 
… 
Model M 
Pseudo R2 
 
 
 
Somers’ Dxy 
 
 
 
C-statistic 
 
 
 


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Dataset: dataset location (data table name with the corresponding database in Google BigQuery) 
Program: program location/program name.R 
Output: output location/output name.csv or .xlsx 
 
Table A7. Summary of the cohort size before and after matching. 
Group 
Study 
participants 
Pool of 
potential 
controls 
All 
 
 
Matched 
 
 
Unmatched 
 
 
Dataset: dataset location (data table name with the corresponding database in Google BigQuery) 
Program: program location/program name.R 
Output: output location/output name.csv or .xlsx 
 
Table A8. Standardized difference in mean and variance ratio of the propensity score and all 
matching covariates before propensity score matching 
Variable 
Standardized difference in mean Variance ratio 
Propensity score 
 
 
Age (continuous or 
categorical): 
65- 
65 – 69 
70 – 74 
75 – 79 
80+* 
*: We can separate 80+ 
into finer groups as 
needed. 
 
 
Gender 
 
 
Race:  
White  
Black 
 
 
Commented [XM58]: Would report both results before and after 
match 


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Asian 
Hispanic 
Other 
Urbanization of 
residence: 
Urban 
Suburban 
Rural  
 
 
Socioeconomic index 
 
 
Dual status 
With Medicaid 
Elixhauser 
comorbidity score 
 
 
Elixhauser conditions* 
*: There are 29 conditions 
in total as shown in Table 
A1. 
 
 
Immunosuppressive 
treatment 6 months 
prior to date of 
COVID diagnosis 
 
 
Dataset: dataset location (data table name with the corresponding database in Google BigQuery) 
Program: program location/program name.R 
Output: output location/output name.csv or .xlsx 
 
Repeat Table A8: 
Table A8.2 
   Standardized difference in means and variance ratio of the propensity score and all matching 
covariates after propensity score matching 
   Propensity score matched population 
Programming notes:  
Replace “before propensity score matching” with “after propensity score matching” in the table 
title. 
 


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Table A9. Unadjusted and adjusted measures of COVID-19 hospitalization risk in study 
participants and matched controls. 
Outcome 
Results (e.g., 95% 
CI and p-value) 
Rate of COVID-19 hospitalization w/o 
considering censoring on Day 28 (i.e. 
binary endpoint) among study participants 
vs matched controls   
 
Adjusted odds ratio w/o considering 
censoring on Day 28 (i.e. binary endpoint) 
among study participants vs matched 
controls 
 
Adjusted hazard ratio with considering 
censoring on Day 28 among study 
participants vs matched controls 
 
Dataset: dataset location (data table name with the corresponding database in Google BigQuery) 
Program: program location/program name.R 
Output: output location/output name.csv or .xlsx 
 
Table A10. Summary of the sensitivity analyses.  
Statistic/Method 
Results 
E-value (95% CI) 
 
𝜀 (95% CI) 
 
Dataset: dataset location (data table name with the corresponding database in Google BigQuery) 
Program: program location/program name.R 
Output: output location/output name.csv or .xlsx 
 
 
 
 
Table A11. Standardized difference in mean and variance ratio of all matching covariates before 
double score matching 
Variable 
Standardized difference in mean Variance ratio 
Commented [XM59]: Would include 95% CI and p value.  
Commented [XM60]: Not sure what this mean? 
Commented [JAT61R60]: This means a binary response. If we 
don’t observe a covid-19 hospitalization for a patient within 28 days 
after his/her treatment, we define this response as 0, instead of 
censoring on Day 28. 
Commented [XM62]: Add tables for the death results and safety 
analyses  
Commented [JAT63R62]: I add Table A12 and A12.2 for 
safety analyses. No tables for all-cause death since it’s not the 
primary outcome here. 


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Age (continuous or 
categorical): 
65- 
65 – 69 
70 – 74 
75 – 79 
80+* 
*: We can separate 80+ 
into finer groups as 
needed. 
 
 
Gender 
 
 
Race:  
White  
Black 
Asian 
Hispanic 
Other 
 
 
Urbanization of 
residence: 
Urban 
Suburban 
Rural  
 
 
Socioeconomic index 
 
 
Dual status 
With Medicaid 
Elixhauser 
comorbidity score 
 
 
Elixhauser conditions* 
*: There are 29 conditions 
in total as shown in Table 
A1. 
 
 
Immunosuppressive 
treatment 6 months 
prior to date of 
COVID diagnosis 
 
 


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Dataset: dataset location (data table name with the corresponding database in Google BigQuery) 
Program: program location/program name.R 
Output: output location/output name.csv or .xlsx 
 
Repeat Table A11: 
Table A11.2 
   Standardized difference in means and variance ratio of all matching covariates after double 
score matching 
   Double score matched population 
Programming notes:  
Replace “before double score matching” with “after double score matching” in the table title. 
 
 
Table A12. Listing of adverse events (AEs) for study participants. 
Patient ID 
Age/Gender 
AE* 
Start/Stop date 
No. of days after 
the Bamlanivimab 
infusion 
  
 
 
 
  
 
 
 
*: adverse events (AEs) include nausea, diarrhea, dizziness, headache, pruritus, and vomiting. 
We will report the table if available. 
 
Repeat Table A12: 
Table A12.2 
   Listing of serious adverse events (SAEs) for study participants. 
Programming notes:  
Replace “SAE” with “AE” in the column of AE. 
SAE includes secondary infection, respiratory failure requiring non-invasive or invasive mechanical ventilation, 
major acute cerebrocardiovascular event, coagulopathy, acute kidney injury, and multi-organ dysfunction syndrome. 
 
Figures: 
 
 
Commented [JAT64]: We’re checking this information 
internally and will keep you posted. 


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Figure A1. Flow chart of the enrollment process 
 
Figure A2:  
Distribution of propensity score in study participants and matched controls before and after 
matching.  
The x-axis is propensity score, and the y-axis is density. 
Figure A3: 
Distribution of selected matching covariates in study participants and matched controls as 
needed. 
The x-axis is a selected matching covariate, and the y-axis is density.  
If there are multiple variables, we will display a panel of multiple plots. 
No. of members reached out 
No. of members consented to 
track symptoms vis ProtectWell
No. of members track 
symptoms via ProtectWell
No. of members with symptoms onset
No. of members with COVID positive
No. of COVID positive members who 
consent to receive the Bamlanivimab 
infusion (i.e., study participants) 
No of study participants enrolled 
through Days 1-28 after the 
Bamlanivimab infusion
Commented [XM65]: Would be before and after match 


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Figure A4: 
Kaplan-Meirer curves of COVID-19 hospitalization among study participants and matched 
controls. 
The x-axis is time (unit: day), and the y-axis is Kaplan-Meier estimate of survival probability. 
 


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7. References 
 
Bonvini, M. a. (2020). Sensitivity analysis via the proportion of unmeasured confounding. 
Journal of the American Statistical Association. 
Cinelli, C. a. (2020). Making sense of sensitivity: extending omitted unmeasured bias. Journal of 
the Royal Statistical Society - B, 39-67. 
Crump, R. K. (2009). Dealing with limited overlap in estimation of average treatment effects. . 
Biometrika, 96, no. 1: 187-199. 
Guo, S. a. ( 2014). Propensity score analysis: Statistical methods and applications. SAGE 
publications. Vol. 11. . 
Ioannidis, J. P. (2019). Limitations and misinterpretations of E-values for sensitivity analyses of 
observational studies. Annal of Internal Medicine, 108-111. 
Johson, R. A. (1992). Applied Multivariate Statistical Analysis. New Jersey: Prentice-Hall 
International. 
Lee, W.-C. (2014). Detecting and correcting the bias of unmeasured factors using perturbation 
analysis: a data-mining approach. BMC Medical Research Methodology. 
Lipsitch, M. E. (2010). Negative controls: a tool for detecting confounding and bias in 
observational studies. Epidemiology (Cambridge, Mass.) , 21, no. 3: 383. 
McCulloch, C. E. (2014). Generalized linear mixed models. Wiley StatsRef: Statistics Reference 
Online.  
Moore, B. a. (2017). Identifying increased risk of readmission and in-hospital mortality using 
hospital administrative data: the AHRQ Elixhauser comorbidity index. Medical Care, 
698-705. 
Nelder, J. A. (1972). Generalized linear models. Journal of the Royal Statistical Society: Series A 
(General) , 135, no. 3: 370-384. 
Rosenbaum, P. R. (1984). Reducing bias in observational studies using subclassification on the 
propensity score. Journal of the American statistical Association , 79, no. 387: 516-524. 
Smith, J. A. (2005). Does matching overcome LaLonde's critique of nonexperimental estimators? 
Journal of econometrics, 305-353. 
Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal 
Statistical Society: Series B (Methodological) , 58, no. 1: 267-288. 
Van Buuren, S. (2018). Flexible imputation of missing data. CRC press. 
VanderWeele, T. J. (2017). Sensitivity Analysis in Observational Research: Introducing the E-
Value. Annals of internal medicine. 
Yang, S. Z. (2020). Multiply robust matching estimators of average adn quantile treatment 
effects. arXiv. 


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Zhang, X. D. (2018). Addressing unmeasured confounding in comparative observational 
research. Pharmacoepidemiology and drug safety , 373-382.