The UPMC OPtimizing Treatment and Impact of Monocolonal antIbodieS Through Evaluation for COVID-19 Trial
Phase: PHASE4
Conditions: Covid19
Sponsor: Erin McCreary

NCT04790786 
UPMC OPTIMISE-C19 Trial, a COVID-19 Study (OPTIMISE-C19) 
Document date: 5/2/2022 
 
 

1 
 
 
 
Supplementary Materials  
 
The Comparative Effectiveness of COVID-19 Monoclonal Antibodies:  
A Learning Health System Randomized Clinical Trial 
 
 
Table of Contents 
 
Table S1: Department of Health and Human Services - Food and Drug Administration Emergency Use 
Authorizations and UPMC Monoclonal Antibody Policy Changes Over Time……………………………………….1 
 
Table S2: Comparison of 28-Day Hospitalization Rates by Treatment and Site Location ...........................................3 
 
Table S3: Adverse Events in Patients Receiving Monoclonal Antibody Treatment…………………………………..4  
 
Table S4: Baseline Characteristics of Patients Who Received a Randomized mAb Allocation, by Infusion Status       
(March 10 – June 25, 2021)…………………………………………………………………………………………...5 
 
Figure S1: Treatment heterogeneity across variant date prevalence epochs…………………………………………..6  
 
Figure S2: SARS-CoV-2 variants of concern proportion in Pennsylvania during the study………………………….7 
 
Figure S3: Number of infused patients over time and distribution of patients within each of the time buckets used to 
estimate time trends in the analysis model (primary analysis)………………………………………………………...8 
 
Figure S4: Stacked proportion of hospital free days for each time epoch (primary analysis; as-infused population)...9 
 
Study Protocol………………………………………………………………………………………………………...10 
 
CONSORT Extension for Pragmatic Trials Checklist………………………………………………………………..18 
 
Statistical Analysis Plan………………………………………………………………………………………………20 
 
 
 
 
 
 
 

2 
 
Table S1: U.S. Department of Health and Human Services - Food and Drug Administration Emergency Use 
Authorizations and UPMC Monoclonal Antibody Policy Changes Over Time 
Date 
Change 
Explanation 
11-9-2021 
DHHS/FDA provides mAb EUAs and treatment for 
COVID-19 eligibility criteria 
 
 
Eligibility Criteria - High risk is defined as patients who 
meet at least one of the following criteria:  
• Have a body mass index (BMI) ≥35a  
• Have chronic kidney disease  
• Have diabetes  
• Have immunosuppressive diseaseb  
• Are currently receiving immunosuppressive treatment  
• Are ≥65 years of age  
• Are ≥55 years of age AND have  
    cardiovascular disease, OR  
    hypertension, OR  
    chronic obstructive pulmonary disease/other chronic 
respiratory disease.  
• Are 12–17 years of age AND have   
    BMI ≥85th percentilea for their age and gender based on 
CDC growth charts,a OR  
    sickle cell disease, OR  
    congenital or acquired heart disease, OR  
    neurodevelopmental disorders, for example, cerebral 
palsy, OR  
    a medical-related technological dependence, for 
example, tracheostomy, gastrostomy, or positive pressure 
ventilation (not related to COVID-19), OR  
    asthma, reactive airway or other chronic respiratory 
disease that requires daily medication for control. 
EUAs for  
• 
bamlanivimab  
• 
bamlanivimab and etesevimab (available 09-
02-2021) 
• 
casirivimab and imdevimab 
 
03-10-2021 
UPMC – Opened study enrollment  
Go-live date for trial with bamlanivimab, and casirivimab 
and imdevimab only and following  
COVID-19 Treatment Guidelines. 
03-16-2021 
UPMC - Incorporation of bamlanivimab-etesevimab into 
random allocation  
 
Start incorporating bamlanivimab and etesevimab into 
most sites. There was a 1-week delay to use etesevimab at 
six sites due to low initial supply. 
02-23-2021 
UPMC - mAb treatment expanded to Emergency 
Departments  
Based on published data from our health system 
demonstrating significant decrease in hospitalizations and 
deaths with bamlanivimab, the decision was made to 
invest resources in Emergency Department expansion to 
increase access to mAb treatment. 
03-25-2021 
DHHS/FDA EUA for distribution of bamlanivimab alone 
was halted   
 
A memorandum on the Fact Sheet for bamlanivimab alone 
– Update was issued on 03/17/21. Pseudovirus 
neutralization data for SARS-CoV-2 variant substitutions 
with bamlanivimab alone was unsuccessful. 
 
3-31-2021 
UPMC – haulted bamlanivimab alone   
Emerging data about lack of efficacy with SARS-COV-2 
variants resulted in System COVID-19 Pharmacy & 
Therapeutics Committee’s decision to remove 
bamlanivimab alone from formulary. All sites put on view 
to use bamlanivimab only with etesevimab.  
04-16-2021 
DHHS/FDA EUA for bamlanivimab was revoked   
EUA revoked due to lack of efficacy with SARS-COV-2 
variants.  
04-25-2021 
UPMC - Protocol Amendment 1 to remove use of 
bamlanivimab alone 
Updated trial protocol to reflect clinical practice of no 
longer using bamlanivimab alone. 
 
04-26-2021 
UPMC – expanded study enrollment to observation status 
patients  
Established process for treating observation status patients 
at UPMC hospitals. 
05-06-2021 
UPMC - Interim Analysis  
Data supported it was safe to proceed. 
05-14-2021 
DHHS/FDA EUA expands eligibility criteria  
 
Eligibility Criteria - The following medical conditions or 
other factors may place adults and pediatric patients (age 
12–17 years and weighing at least 40 kg) at higher risk for 
progression to severe COVID-19:  
• Older age (for example age ≥65 years of age)  

3 
 
Date 
Change 
Explanation 
• Obesity or being overweight (for example, adults with 
BMI >25 kg/m2a  or if age 12–17, have BMI ≥85th 
percentilea for their age and gender based on CDC growth 
charts  
• Pregnancy  
• Chronic kidney disease  
• Diabetes  
• Immunosuppressive diseaseb or immunosuppressive 
treatment  
• Cardiovascular disease (including congenital heart 
disease) or hypertension  
• Chronic lung diseases (for example, chronic obstructive 
pulmonary disease, asthma [moderate-to-severe], 
interstitial lung disease, cystic fibrosis, and pulmonary 
hypertension) • Sickle cell disease  
• Neurodevelopmental disorders (for example, cerebral 
palsy) or other conditions that confer medical complexity 
(for example, genetic or metabolic syndromes and severe 
congenital anomalies)  
• Having a medical-related technological dependence 
(e.g., tracheostomy, gastrostomy, or positive pressure 
ventilation [not related to COVID-19]) 
 
05-26-2021 
DHHS/FDA provides an EUA for sotrovimab  
Sotrovimab is a new mAb treatment. 
06-03-2021 
DHHS/FDA EUA revised to decrease dose for casirivimab 
and imdevimab 
 
Casirivimab 600mg and imdevimab  
600 mg 
06-15-2021 
UPMC - decreased dose for casirivimab and imdevimab 
Casirivimab and imdevimab dose decreased from 2400 mg
to 1200 mg. 
 
06-25-2021 
DHHS/FDA EUA for distribution of bamlanivimab and 
etesevimab was paused  
FDA recommends not to use bamlanivimab and 
etesevimab together. 
06-25-2021 
UPMC - paused bamlanivimab and etesevimab 
UPMC removes bamlanivimab and etesevimab from 
inventory. 
  
06-25-2021 
UPMC - Protocol Amendment 2:    
remove bamlanivimab and etesevimab,   
add sotrovimab, and update EUA expanded eligibility 
criteria  
Updated trial protocol to reflect clinical practice of 
pausing use of bamlanivimab and etesevimab, add use of 
sotrovimab, and expanded eligibility criteria. 
  
Abbreviations: mAb, monoclonal antibodies; DHHS/FDA, Department of Health and Human Services/Food and Drug Administration; EUA, 
emergency use authorization; BMI, body mass index.  
aCalculated as weight in kilograms divided by height in meters squared. 
bImmunosuppressive disease or treatment was defined as a history of HIV, cancer, transplant (solid organ, stem cell, bone marrow), 
chemotherapy treatment, lupus, rheumatoid arthritis, or liver disease.  
 
 
 
 

4 
 
Table S2: Comparison of 28-Day Hospitalization Rates by Treatment and Site Location 
All Patients 
 
N 
 
No. (%) 
 
95% C.I. 
Risk 
Difference 
 
95% C.I. 
Exceeds Upper Limit of 95% C.I. 
Treatment 
3% 
4% 
5% 
6% 
B + E 
885 
130 (14.7%) 
(12.4% to 17.4%) 
0.4% 
(-2.9% to 3.6%) 
Yes 
No 
No 
No 
C + I 
922 
132 (14.3%) 
(12.1% to 16.8%) 
Reference 
Reference 
ED Patients 
 
 
 
 
 
 
 
 
 
B + E 
422 
100 (23.7%) 
(19.7% to 28.1%) 
2.0% 
(-3.6% to 7.5%) 
Yes 
Yes 
Yes 
Yes 
C + I 
460 
100 (21.7%) 
(18.0% to 25.8%) 
Reference 
Reference 
IC Patients 
 
 
 
 
 
 
 
 
 
B + E 
463 
30 (6.5%) 
(4.4% to 9.1%) 
-0.5% 
(-3.7% to 2.8%) 
No 
No 
No 
No 
C + I 
462 
32 (6.9%) 
(4.8% to 9.6%) 
Reference 
Reference 
Abbreviations: B + E, bamlanivimab and etesevimab; C + I, casirivimab and imdevimab; C.I., confidence interval; ED, emergency department; 
IC, infusion center. 
 
 
 
 
 

5 
 
Table S3: Adverse Events in Patients Receiving Monoclonal Antibody Treatment 
Patient ID 
mAb 
Received 
Mild 
Reaction 
Severe 
Reaction 
Reaction Description 
1 
Bamlanivimab and 
Etesevimab 
Yes 
.. 
Hypoglycemic, hypotension: infusion stopped treated with 
diphenhydramine and fluid, patient discharged stable to home. 
 
2 
Bamlanivimab and 
Etesevimab 
Yes 
.. 
Patient developed tightness in head/sinus area, and tightness in chest. 
Infusion not finished. Discharged home. 
 
3 
Bamlanivimab and 
Etesevimab 
Yes 
.. 
Patient reported chest tightness, became lightheaded and dizzy. Infusion 
not finished. Discharged home. 
 
4 
Bamlanivimab and 
Etesevimab 
Yes 
.. 
Patient developed hypotension; responded to 500 mL Lactated Ringers, 
discharged home normotensive. 
 
5 
Bamlanivimab and 
Etesevimab 
Yes 
.. 
Headache, responded to acetaminophen. 
6 
Bamlanivimab and 
Etesevimab 
Yes 
.. 
Flushed, chest pain. Stopped infusion. 
7 
Bamlanivimab and 
Etesevimab 
Yes 
.. 
Low back pain, radiating. Decreased infusion rate in half. Patient stated 
pain alleviated and denied any complaints. 
 
8 
Bamlanivimab and 
Etesevimab 
Yes 
.. 
Patient reported nausea, hot flash, severe lower back pain 1 minute into 
infusion. 
 
9 
Bamlanivimab and 
Etesevimab 
.. 
Yes  
Patient stated flushed/throat "closing". Stopped infusion. Sent to 
emergency department for evaluation. 
 
10 
Bamlanivimab and 
Etesevimab 
Yes 
.. 
Flushing and shortness of breath. 
11 
Bamlanivimab and 
Etesevimab 
Yes 
.. 
Mild itchiness and redness at infusion site. 
12 
Bamlanivimab and 
Etesevimab 
Yes 
.. 
Became itchy, developed hives, received diphenhydramine. 
13 
Casirivimab and 
Imdevimab 
Yes 
.. 
Headache. 
14 
Casirivimab and 
Imdevimab 
.. 
Yes  
Sudden onset nausea, vomiting, and weakness after 1 hour observation 
period; checked into emergency department and admitted post infusion. 
 
15 
Casirivimab and 
Imdevimab 
Yes 
.. 
Patient developed dizziness and chills which resolved prior to discharge. 
16 
Casirivimab and 
Imdevimab 
Yes 
.. 
Right before discharge patient mentioned dizziness and lower blood 
pressure. Discharged to home. 
 
17 
Casirivimab and 
Imdevimab 
.. 
Yes 
Hypertension and headache post-infusion, transferred to emergency 
department.  
 
18 
Casirivimab and 
Imdevimab 
.. 
Yes 
Patient became hot, red face, reported chest pain 9/10. Oxygen saturation 
went from 95% to 85% to 73%. Sent to emergency department. 
 
19 
Casirivimab and 
Imdevimab 
Yes 
.. 
Chills, flushing, chest tightness, headache. Infusion stopped. 
20 
Casirivimab and 
Imdevimab 
.. 
Yes 
Chest pain, shortness of breath, and back pain 2 minutes into infusion. 
Paramedics called. 
 
21 
Casirivimab and 
Imdevimab 
Yes 
.. 
Shortness of breath, chest tightness 3 minutes into infusion. Medication 
stopped and symptoms resolved. 
Abbreviations: mAb, monoclonal antibody. 
 
 

6 
 
Table S4: Baseline Characteristics of Patients Who Received a Randomized Monoclonal Antibody Allocation 
by Infusion Status (March 10–June 25, 2021)  
 
 
Variable 
Randomized, infused 
(n=1,935) 
Randomized, not 
infused/analyzed 
(n=443) 
No. 
Mean, (SD) 
No. 
Mean, (SD) 
Age in years 
1935 
55.5 (16.0) 
443 
53.1 (18.6) 
Body mass indexa 
1302 
34.8 (8.5) 
306 
34.8 (8.5) 
Charlson Comorbidity Indexb 
1166 
1.1 (1.5) 
299 
0.8 (1.1) 
.. 
No. 
No. (%) 
No. 
No. (%) 
Age in years (categories) 
1935 
.. 
443 
.. 
     0 to 30 
.. 
157 (8.1) 
.. 
59 (13.3) 
     31 to 50 
.. 
511 (26.4) 
.. 
132 (29.8) 
     51 to 60 
.. 
484 (25.0) 
.. 
84 (19.0) 
     61 to 70 
.. 
474 (24.5) 
.. 
92 (20.8) 
     71 to 80 
.. 
218 (11.3) 
.. 
44 (9.9) 
     81 and older 
.. 
91 (4.7) 
.. 
32 (7.2) 
Female sexc 
1935 
1041 (53.8) 
443 
274 (61.9) 
Raced 
1884 
.. 
428 
.. 
  White 
.. 
1498 (79.5) 
.. 
345 (80.6) 
   Black 
.. 
335 (17.8) 
.. 
79 (18.5) 
   Othere  
.. 
51 (2.7) 
.. 
4 (0.9) 
Body mass index (categories)a 
1302 
.. 
306 
.. 
   Less than 18.5 
.. 
7 (0.5) 
.. 
7 (2.3) 
   18.5 to less than 25.0 
.. 
119 (9.1) 
.. 
27 (8.8) 
   25.0 to less than 30.0 
.. 
273 (21.0) 
.. 
51 (16.7) 
   30.0 to less than 35.0 
.. 
309 (23.7) 
.. 
75 (24.5) 
   35.0 to less than 40.0 
.. 
296 (22.7) 
.. 
68 (22.2) 
   40.0 or higher 
.. 
298 (22.9) 
.. 
78 (25.5) 
History of hypertension 
1414 
782 (55.3) 
352 
152 (43.2) 
History of obstructive sleep apnea 
1414 
370 (26.2) 
352 
81 (23.0) 
History of allergic rhinitis 
1414 
189 (13.4) 
352 
49 (13.9) 
History of diabetes 
1414 
383 (27.1) 
352 
63 (17.9) 
History of coronary artery disease 
1414 
172 (12.2) 
352 
35 (9.9) 
History of congestive heart failure 
1414 
117 (8.3) 
352 
32 (9.1) 
History of atrial fibrillation 
1414 
102 (7.2) 
352 
20 (5.7) 
History of valvular heart disease 
1414 
97 (6.9) 
352 
26 (7.4) 
History of stroke 
1414 
84 (5.9) 
352 
22 (6.2) 
History of dyspnea 
1414 
105 (7.4) 
352 
20 (5.7) 
History of asthma 
1414 
517 (36.6) 
352 
136 (38.6) 
History of chronic obstructive pulmonary disease 
1414 
278 (19.7) 
352 
78 (22.2) 
History of chronic kidney disease 
1414 
112 (7.9) 
352 
27 (7.7) 
History of fatty liver disease 
1414 
83 (5.9) 
352 
14 (4.0) 
History of viral hepatitis 
1414 
40 (2.8) 
352 
6 (1.7) 
History of cancer 
1414 
186 (13.2) 
352 
32 (9.1) 
aCalculated as weight in kilograms divided by height in meters squared. 
bCharlson Comorbidity Index is calculated as S – result from point addition; CCI ten year survival=0.983A  where A=e (S x 0.9). 
cSex was reported by the patients. 
dRace was reported by the patients. 
eOther includes Chinese, Filipino, Hawaiian, American Indian/Alaskan Native, Asian, 
Hawaiian/other Pacific Islander, Middle Eastern, Native American, or Pacific Islander. 
 
 
 
 

7 
 
 Figure S1: Treatment Heterogeneity Across Variant Date Prevalence Epochs 
 
 Red represents worse outcomes and blue represents better outcomes. 
 
 
 
 

8 
 
 Figure S2: SARS-CoV-2 Variants of Concern Proportion in Pennsylvania During the Study  
 (March 10–June 25, 2021) 
 
Red represents Alpha B.1.1.7, purple is Beta B.1.351, green is Delta B.1.617.2, and blue is Gamma P.1. 
 
 
3/10/21
3/24/21
4/7/21
4/21/21
5/5/21
5/19/21
6/2/21
6/16/21
0.0
0.5
1.0
Time (weeks)
Proportion of cases
Variants of Concern
Alpha B.1.1.7
Beta B.1.351
Delta B.1.617.2
Gamma P.1

9 
 
Figure S3: Number of Infused Patients Over Time and Distribution of Patients Within Each of the Time 
Buckets Used to Estimate Time Trends in the Analysis Model (Primary Analysis) 
 
The time buckets are derived so that the first bucket is the most recent 4 weeks going backwards in time from the most recently infused patient in 
the dataset that has a complete hospital-free days outcome. Thereafter, each bucket is defined as the next 2-week interval backwards in time. The 
vertical dashed line indicates the infusion date for the last patient who has passed 28 days and has a known outcome on the primary endpoint at 
the time of this analysis. 
 

10 
 
Figure S4: Stacked Proportion of Hospital Free Days for Each Time Epoch (Primary Analysis; As-infused 
Population)  
 
Red represents worse outcomes and blue represents better outcomes. 
 
 
 
 

11 
 
Study Protocol  
Final Version 1.3, June 30, 2021 
 
UPMC OPTIMISE-C19 (OPtimizing Treatment and Impact of  
Monoclonal antIbodieS through Evaluation for COVID-19) 
 
A Pragmatic Evaluation of Monoclonal Antibody Treatments in Participants with COVID-
19 Illness (ClinicalTrials.gov, NCT04790786) 
 
 
Summary of Protocol Changes for Amendment on April 25, 2021  
Page # 
Protocol Section 
Reason for Change 
1 
Protocol Title Page 
Title changed to reflect new abbreviation being used for study 
2-3 
Summary Table 
Summary table updated to reflect protocol changes 
10 
4.1 
Clarification added to section 4.1 to reflect changing landscape of FDA EUA and 
revocation 
12 
5 
Clarification added to section 5 to reflect changing landscape of FDA EUA and revocation 
12 
5.1 
Removed section 5.1 bamlanivimab due to EUA revoked 
17 
8.1 
Updated to provide more detail on the monitoring of data by UPMC clinical leadership 
18 
9 
EUA for bamlanivimab removed 
 
Summary of Protocol Changes for Amendment on June 30, 2021 
 
 
 
 
 
 
 
 
 
 
Page # 
Protocol Section 
Reason for Change 
NA 
NA 
Minor administrative changes throughout the document to reflect Amendment #2 
documentation 
2-3 
Summary table 
Summary table updated to reflect protocol changes 
7 
2.1. 
Information regarding new monoclonal antibody sotrovimab 
7 
2.1. 
Information added regarding the revocation of EUA approved of bamlanivimab and 
etesvimab 
8 
2.2. 
Information added to reflect current COVID trends 
10-11 
4.1.1. 
Updated Inclusion Criteria as per EUA requirements 
12 
5 
Information added regarding the revocation of EUA approved of Bamlanivimab 
12 
5.1. 
Removal of section 5.1. – bamlanivimab and etesvimab 
12-13 
5.2. 
Addition of newly approved monoclonal antibody – sotrovimab 
15 
6.3. 
Information added to reflect UPMC will utilize a pharmacy manual if provided by 
the drug manufacturer 
19 
9.0. 
Exhibits added and removed to reflect current EUA approved monoclonal antibodies 

12 
 
Protocol Summary 
Background 
• 
FDA Emergency Use Authorization (EUA) exists for multiple monoclonal antibodies (mAb) to treat 
COVID-19; the EUAs stipulate eligibility criteria, patient-physician communication, and clinical 
monitoring. 
• 
UPMC provides mAbs as routine care; physicians order a mAb infusion and pharmacies assign 
whichever mAb is available under a therapeutic interchange approach. If scarcity exists, a lottery system 
is used. 
• 
Physicians review with patients the EUA Fact Sheet for each mAb and explain they could be assigned 
any of the EUA-governed mAbs. 
Approach 
• 
Structure the therapeutic interchange policy and lottery system using a UPMC pharmacy embedded 
assignment system that allows a comparative effectiveness evaluation of the multiple mAbs. 
• 
Collect data from clinically performed UPMC processes and EUA requirements for routine care. 
Treatments 
• 
Monoclonal antibodies (mAb) for COVID-19  
Inclusion 
Criteria 
These criteria are as per the FDA EUAs for COVID-19 mAbs as of June 2021. 
• 
Adult (> 18 years old)  
• 
Children > 12 years old weighing at least 40 kg 
• 
With a positive SARS-CoV-2 antigen or PCR test and within 10 days of symptom onset 
• 
High risk of disease progression 
High risk is defined as patients who meet at least one of the following criteria:  
• 
Are ≥ 65 years old 
• 
A Body Mass Index (BMI)>25, or if age 12–17, BMI > 85th percentile  
• 
Pregnancy 
• 
Have chronic kidney disease 
• 
Have diabetes 
• 
Have immunosuppressive disease 
• 
Are currently receiving immunosuppressive treatment  
• 
Cardiovascular disease (including congenital heart disease) or hypertension 
• 
Chronic lung disease 
• 
sickle cell disease 
• 
neurodevelopmental disorders (e.g., cerebral palsy) or other conditions that confer medical complexity 
(e.g., genetic, or metabolic syndromes and severe congenital anomalies) 
• 
a medical-related technological dependence, for example, tracheostomy or gastrostomy) 
 
The EUAs note that other medical conditions or factors (for example, race or ethnicity) may also place individual 
patients at high risk for progression and authorization of mAb treatment under the EUA is not limited to the medical 
conditions or factors listed above. 
Exclusion 
Criteria 
These criteria are as per the FDA EUAs for COVID-19 mAbs as of April 2021. 
• 
Are hospitalized for the treatment of COVID-19 
• 
Require oxygen therapy for the treatment of COVID-19 
• 
Require an increase in baseline oxygen flow rate due to COVID-19 in those on chronic oxygen therapy 
due to underlying non-COVID-19 related comorbidity 
• 
Have a known hypersensitivity to any antibody ingredient 
Primary 
evaluation 
metric  
Total hospital free days at 28 days 
 
 
  
 

13 
 
1. ABBREVIATIONS 
AE 
Adverse Events 
BMI 
Body Mass Index 
C + I 
casirivimab + imdevimab 
CDC 
Centers for Disease Control and Prevention 
COPD 
Chronic Obstructive Pulmonary Disease 
COVID-19 
Coronavirus disease 2019 
CVD 
Cardiovascular Disease 
EUA 
Emergency Use Authorization 
FDA 
Food and Drug Administration 
HHS 
Health and Human Services 
HFD 
Hospital Free Days 
HTN 
Hypertension 
kDa 
Kilodaltons 
IgG1 
Immunoglobulin G1 
KG 
Kilograms 
OPTIMISE-C19 
OPtimizing Treatment and Impact of Monoclonal antIbodieS through 
Evaluation  
mAb 
Monoclonal Antibodies 
PCR 
Polymerase Chain Reaction 
SAEs 
Serious Adverse Events 
SARS-CoV-2 
Severe Acute Respiratory Syndrome Coronavirus 2 
UATRC 
UPMC Antibody Treatment and Evaluation Center  
Vir-7831 
sotrovimab 
 
2. 
BACKGROUND and RATIONALE 
 
3. 
BACKGROUND 
 
While COVID-19 vaccination will reduce COVID-19-related morbidity and mortality, the learned immune response 
may vary between individuals. This means interventions such as monoclonal antibodies (mAb) will still be needed to 
prevent progression of COVID-19 illness. Monoclonal antibodies seek to mimic or enhance the natural immune 
system response against a pathogen and are often used in the care of patients with cancer or infection.  
For viral infections, mAbs are created by exposing a white blood cell to a particular viral protein, which is then cloned 
to mass produce antibodies to target that virus. For SARS-CoV-2, the virus that causes COVID-19, IgG1 mAbs target 
the spike protein of SARS-CoV-2 and block viral attachment and entry into cells.  
 
The SARS-CoV-2 mAbs bamlanivimab and etesevimab, and the REGN-COV2 combination (casirivimab + 
imdevimab) reduce nasopharyngeal viral burden plus clinical outcomes including future emergency department visits 
and hospitalizations (Weinreich 33332778, NEJM, Gottlieb 33475701). Each received FDA Emergency Use 
Authorization (EUA) for use in selected populations (Exhibit); in April 2021 FDA revoked the EUA for 
bamlanivimab monotherapy, and in June 2021 FDA recommended bamlanivimab and etesevimab not be used. 
Additional trials of pre-exposure prophylaxis (NCT04497987) and other applications are underway, and additional 
mAbs are in development. 
 
In May 2021, it was announced that sotrovimab demonstrated clinical efficacy (85%) in reducing hospitalizations for 
more than 24 hours or death in those that received sotrovimab as compared to placebo (NCT04545060). Subsequently, 
it received EUA approval in select populations. Additional trials are underway. 
 
The trials demonstrated the greatest impact of the REGN-COV2 dual therapy among patients who lacked neutralizing 
antibodies against SARS-CoV-2 at baseline and in those with high nasopharyngeal viral loads. Additionally, few 
patients in the bamlanivimab/etesevimab trial developed treatment-emergent SARS-CoV-2 resistance. This latter 
phenomenon may further enhance the need for therapies given the recent emergence of SARS-CoV-2 variants that 
may escape vaccination. However, the relative effectiveness of each mAb compared to the other is unknown, as is 
their effectiveness for emerging virus variants. 

14 
 
 
This Appendix to the UPMC Pilot Core (PittPro 20040210) describes the approach of the UPMC OPTIMISE-C19 
evaluation. We will conduct a pragmatic evaluation of monoclonal antibody treatments in participants with COVID-
19 illness, starting with the patient population approved under the current FDA mAb EUAs. 
 
4. 
RATIONALE 
 
As of June 2021, there are over 10,000 new cases of COVID-19 diagnosed daily in the US https://covid.cdc.gov/covid-
data-tracker/#trends_dailytrendscases, with over 1500 daily COVID-19 related hospital admissions Microsoft Power 
BI (powerbigov.us). Although case volumes are currently declining, COVID-19 remains a significant public health 
threat. 
Despite the EUAs, the clinical use of mAbs is low due in part to lack of patient access, complexities in drug allocation, 
and lack of knowledge among providers are contributing factors. Further, the comparative effectiveness of different 
mAbs is unknown and not yet directly studied. The National Academies of Sciences, Engineering, and Medicine 
recently called for expanded access and clinical use of mAbs, noting it is “critical to collect data and evaluate whether 
they are working as predicted.”  
This evaluation seeks to expand access to mAbs at UPMC and determine their relative effects versus each other, 
starting with those governed by EUAs.  
 
5. 
OBJECTIVES AND METRICS 
 
6. 
OBJECTIVES 
 
The primary objective is to evaluate the clinical and biological effect of multiple monoclonal antibodies (mAbs) in 
patients with COVID-19. 
The primary hypothesis is clinical and biological effect will vary between mAbs, by SARS-CoV-2 variants, and patient 
characteristics. 
 
7. 
METRICS 
 
The primary evaluation metric is total hospital free days (HFD) at 28 days after mAb receipt calculated as 28 minus 
the number of days during the index stay minus the number of days readmitted during the 28 days after treatment. 
Death within 28 days is recorded as -1 HFD.  
Secondary evaluation metrics include: 
• 
All-cause and all-location mortality at 28 and 90 days 
• 
Emergency department visits at 28 days 
• 
Organ-support free days at day 28  
• 
Where feasible: 
• 
SARS-CoV-2 nasopharyngeal and plasma viral loads among participants from baseline and longitudinally 
through day 28 
• 
SARS-CoV-2 antibody titers, antibody neutralization, and other immune responses at baseline and 
longitudinally through day 28 
• 
Detection of SARS-CoV-2 variants through next-generation sequencing at baseline and longitudinally 
through day 28 
• 
Determining the duration of SAR-CoV-2 infectivity and non-culture surrogates for SARS-CoV-2 infectivity 
among patients with persistent nasopharyngeal swab viral shedding  
 
8. 
DESIGN 
 

15 
 
We will conduct a pragmatic evaluation of participants with COVID-19 illness under existing UPMC processes for 
the clinical care of COVID-19 positive patients, including EUA requirements for mAb administration. A patient who 
presents to a UPMC facility and tests positive for COVID-19 will, as per current common care, be offered monoclonal 
antibodies. Data that are already collected according to UPMC procedures and EUA requirements are used for 
analysis. 
 
9. 
POPULATION 
 
We will evaluate patients that present to UPMC Emergency Departments, urgent care sites, infusions centers and other 
facilities that can or do provide mAbs for COVID-19. As of June 30, 2021, there are 2 EUAs for COVID-19 mAbs, 
with common inclusion and exclusion criteria, and we will evaluate patients that meet these criteria. As FDA antibody 
decisions change (E.g., FDA revokes or grants EUAs, or changes eligibility criteria), eligibility criteria will change.  
 
10. 
INCLUSION CRITERIA 
 
As per the current EUA criteria (June 2021), the following patients are included: 
• 
Adult (> 18 years old)  
• 
Children > 12 years old weighing at least 40 kg 
• 
With a positive SARS-CoV-2 antigen or PCR test and within 10 days of symptom onset 
• 
High risk of disease progression 
High risk is defined as patients who meet at least one of the following criteria:  
• 
Are ≥ 65 years old 
• 
A Body Mass Index (BMI)>25, or if age 12–17, BMI > 85th percentile  
• 
Pregnancy 
• 
Have chronic kidney disease 
• 
Have diabetes 
• 
Have immunosuppressive disease 
• 
Are currently receiving immunosuppressive treatment  
• 
Cardiovascular disease (including congenital heart disease) or hypertension 
• 
Chronic lung disease 
• 
sickle cell disease 
• 
neurodevelopmental disorders (e.g., cerebral palsy) or other conditions that confer medical complexity (e.g., 
genetic, or metabolic syndromes and severe congenital anomalies) 
• 
a medical-related technological dependence, for example, tracheostomy or gastrostomy) 
 
The EUAs note that other medical conditions or factors (for example, race or ethnicity) may also place individual 
patients at high risk for progression and authorization of mAb treatment under the EUA is not limited to the medical 
conditions or factors listed above. 
 
11. 
EXCLUSION CRITERIA 
 
As per the current EUA criteria (June 2021), the following are excluded: 
• 
Are hospitalized for the treatment of COVID-19 
• 
Require oxygen therapy for the treatment of COVID-19 
• 
Require an increase in baseline oxygen flow rate due to COVID-19 in those on chronic oxygen therapy due 
to underlying non-COVID-19 related comorbidity 
• 
Have a known hypersensitivity to any antibody ingredient 
 
12. 
EVALUATED TREATMENTS 
 
We will evaluate mAbs governed by FDA EUAs. Patients will receive COVID-19 mAbs governed by FDA EUAs, 
when their treating physician orders a mAb and they meet EUA criteria. As FDA antibody decisions change (e.g., 
FDA revokes or grants EUAs, provides full approval, or changes eligibility criteria), available evaluated treatments 

16 
 
will change. In April 2021, FDA revoked the EUA for bamlanivimab monotherapy and in June 2021 FDA 
recommended bamlanivimab and etesevimab not be used. 
As of June 30, 2021, the EUA-approved mAbs are as listed below. 
 
13. 
CASIRIVIMAB and IMDEVIMAB 
 
Casirivimab, a human immunoglobulin G-1 (IgG1) monoclonal antibody (mAb), is covalent eterotetramer consisting 
of 2 heavy chains and 2 light chains produced by recombinant DNA technology in Chinese hamster ovary (CHO) cell 
suspension culture and has an approximate molecular weight of 145.23 kDa. 
Imdevimab, a human IgG1 mAb, is a covalent heterotetramer consisting of 2 heavy chains and 2 light chains produced 
by recombinant DNA technology in Chinese hamster ovary (CHO) cell suspension culture and has an approximate 
molecular weight of 144.14 kDa. 
 
14. 
SOTROVIMAB 
 
Vir 7831 (sotrovimab) is a recombinant human IgG1k monoclonal antibody that binds to a conserved epitope on the 
spike protein receptor binding domain of SARS-CoV-2. Sotrovimab does not compete with human ACE2 receptor 
binding. 
 
15. 
CONCOMITANT THERAPY 
 
All care and concomitant therapy are as per the treating providers.  
 
16. 
CONDUCT 
 
17. 
DATA COLLECTION 
 
The EUAs require that healthcare facilities and providers report therapeutic information and utilization data through 
HHS Protect, Teletracking, or National Healthcare Safety Network as directed by the US Department of Health and 
Human Services. 
 
We will collect data including baseline demographics and underlying conditions, results of SARS-COV-2 PCR or 
antibody testing, and initial care including mAb infusion completion. We will collect post-randomization healthcare 
encounters, including hospitalization, emergency department visits, ICU care, and other measures of healthcare 
utilization. We will use an electronic health record data collection process to augment existing UPMC data collection 
processes, as necessary.  
 
All data will be handled and secured as per University of Pittsburgh and UPMC data guidelines. 
 
There will be no research activities involving direct interaction with subjects performed as part of this evaluation. 
 
In addition to the primary and secondary outcome data referenced in this submission, data collected will include the 
below areas. All data will be abstracted directly from the electronic health record and handled anonymously. 
• 
mAb was administered, including date, time, and infusion completion as well as the location of the infusion 
• 
Demographics (including age, sex, race, body weight, vaccination status) 
• 
Healthcare encounters, including hospital and ICU admission status if applicable 
• 
Medication usage and doses 
• 
Hospital and ICU admission status, if applicable 
• 
Administration of medications related to COVID-19, if applicable 
• 
Remnant blood availability 
• 
Laboratory and microbiology data, including COVID-19 testing done for clinical purposes 
 

17 
 
18. 
BIOSPECIMENS 
 
Where feasible, we will collect discarded remnant blood samples and nasal/oropharyngeal swab samples to quantify 
the viral load and host response to the virus. As noted under data collection, we will record laboratory and 
microbiology data performed for clinical purposes. 
 
19. 
ANTIBODY ADMINISTRATION  
 
Antibodies will be administered as per the EUAs, UPMC Pharmacy and Therapeutics policies and the respective 
Pharmacy Manuals (as generated by the pharmaceutical companies), if applicable. Providers will explain mAb risks 
and benefits and provide the EUA Fact Sheets for Patients, Parents and Caregivers as per EUA requirements. 
 
 
20. 
mAb assignment  
 
The COVID-19 mAbs are currently routinely used at UPMC. Once any order for mAb infusion is approved by the 
UPMC system oversight group, the pharmacy provides whichever EUA-governed mAb is available under a 
therapeutic interchange approach. Ordering physicians review with the patient the EUA Fact Sheet for each mAb and 
explain that the patient could receive any of the mAbs governed by FDA EUAs. 
 
If demand for mAb exceeds supply, UPMC has a lottery system to allot who receives the therapy once requested by a 
physician.  
 
Our current proposal is a UPMC system quality improvement initiative, embracing and extending the current lottery 
system and therapeutic interchange policy for EUA-governed mAbs for COVID-19 as follows: 
1. The Physician orders mAb. 
a. If scarcity present and lottery system allow provision, proceed. 
2. The Pharmacy fills order with one of the EUA-governed COVID-19 mAbs using an embedded assignment system 
akin to current mAb provision. This system will allow a comparative effectiveness evaluation of the multiple mAbs 
by effectively ensuring random allocation. 
3. The Physician can agree to the assigned mAb or can request a specific mAb. 
It is the treating physician’s choice to accept the assigned mAb or not, and therefore patient consent for the mAb 
assignment is not required. Patients will be told which mAb they are receiving, along with an EUA Fact Sheet, as per 
EUA requirements. 
 
21. 
STATISTICAL CONSIDERATIONS 
 
22. 
STRATA 
 
Predefined strata will include patients discharged home after infusion, patients admitted to hospital after infusion, 
prior vaccination, and if known, presence of virus variants of concern at baseline and presence of neutralizing 
antibodies to SARS-CoV-2 at baseline. 
 
23. 
NUMBER of PARTICIPANTS 
 
Sample size is determined by case volume throughout the course of the pandemic. 
 
24. 
STATISTICAL ANALYSIS 
 
The primary evaluation metric is the number of days free from hospitalization to day 28. We will finalize a 
statistical analysis plan which will consider mAb assignment, heterogeneity of treatment effect by patient 
characteristics and virus variants, and interaction with other treatments. Due to uncertainty in sample size, we will 
use a Bayesian adaptive design to ensure ability to provide statistical inference despite variable sample size. 

18 
 
 
25. 
ETHICAL CONSIDERATIONS 
 
26. 
DATA MONITORING 
 
UPMC clinical leadership will regularly monitor monthly reports on enrollment, patient characteristics, and outcomes. 
Leadership will also receive regular interim analyses from the adaptive statistical model to inform UPMC clinical 
policy. 
 
27. 
CONSENT 
 
As per EUA requirements, physicians will discuss the risks and benefits of mAbs and patients will consent to receive 
a mAb as part of usual care, should they desire mAb treatment. As per UPMC policy, the ordering physician reviews 
with patients the EUA Fact Sheet for each mAb and explain that the patient could receive any of the mAbs governed 
by FDA EUAs. 
 
28. 
ADVERSE EVENTS and SERIOUS ADVERSE EVENTS 
 
The EUAs require providers and/or their designees report all medication errors and serious adverse events potentially 
related to the antibodies within seven calendar days from the onset of the event. Serious adverse events are defined as 
death, life-threatening event, inpatient hospitalization or prolongation of existing hospitalization, substantial 
disruption of ability to conduct normal life functions, a congenital anomaly/birth defect, or an intervention to prevent 
death, a life-threatening event, hospitalization, disability, or congenital anomaly. 
The EUAs require adverse event reports be submitted to FDA MedWatch via one of multiple methods. Copies of all 
FDA MedWatch forms are also to be sent to the antibody manufacturer.  
 
Thus, there already exist reporting requirements for UPMC associated with mAb prescription. We will track and 
record these reported data and adverse events by mAb assignment. 
  
29. 
SAFETY and RISK MITIGATION 
 
The EUAs stipulate warnings including hypersensitivity, clinical worsening, and side effects. As per EUA 
requirements, warnings will be communicated by providers to patients, adverse events will be reported as above, and 
post-infusion clinical monitoring will be done. Administration of mAbs for patients with COVID-19 is routine care at 
UPMC, and their administration is not a research procedure. 
 
30. 
MANAGEMENT of INFUSION REACTIONS 
 
As per the EUAs, all participants should be monitored closely, as there is a risk of infusion reaction and 
hypersensitivity (including anaphylaxis) with any biological agent. Symptoms and signs that may occur as part of an 
infusion reaction include, but are not limited to fever, chills, nausea, headache, bronchospasm, hypotension, 
angioedema, throat irritation, rash including urticaria, pruritus, myalgia, and dizziness.  
 
31. 
EXHIBITS 
 
EUA fact sheets for health care providers: 
https://www.fda.gov/media/149534/download (sotrovimab). 
https://www.regeneron.com/downloads/treatment-covid19-eua-fact-sheet-for-hcp.pdf (casirivimab and imdevimab). 
 
 

19 
 
CONSORT Extension for Pragmatic Trials Checklist 
 
Section 
Item 
Standard CONSORT Description 
Pragmatic Trials Extension 
Page 
Title and abstract 
1 
How participants were allocated to interventions 
(e.g., “random allocation,” “randomized,” or 
“randomly assigned”) 
 
Title 
page 1 
& 
Abstract 
page 4-5 
Introduction 
 
Background 
2 
Scientific background and explanation of 
rationale 
Describe the health or health service 
problem that the intervention is 
intended to address and other 
interventions that may commonly be 
aimed at this problem 
6 
Methods 
 
Participants 
3 
Eligibility criteria for participants; settings and 
locations where the data were collected 
Eligibility criteria should be 
explicitly framed to show the degree 
to which they include typical 
participants and/or, where 
applicable, typical providers (e.g., 
nurses), institutions (e.g., hospitals), 
communities (or localities e.g., 
towns) and settings of care (e.g., 
different healthcare financing 
systems) 
8 
Interventions 
4 
Precise details of the interventions intended for 
each group and how and when they were actually 
administered 
Describe extra resources added to (or 
resources removed from) usual 
settings in order to implement 
intervention. Indicate if efforts were 
made to standardize the intervention 
or if the intervention and its delivery 
were allowed to vary between 
participants, practitioners, or study 
sites 
 8-10 
 
 
Describe the comparator in similar 
detail to the intervention 
 
Objectives 
5 
Specific objectives and hypotheses 
6 
Outcomes 
6 
Clearly defined primary and secondary outcome 
measures and, when applicable, any methods 
used to enhance the quality of measurements 
(e.g., multiple observations, training of assessors) 
Explain why the chosen outcomes 
and, when relevant, the length of 
follow-up is considered important to 
those who will use the results of the 
trial 
9 
Sample size 
7 
How sample size was determined; explanation of 
any interim analyses and stopping rules when 
applicable 
If calculated using the smallest 
difference considered important by 
the target decision maker audience 
(the minimally important difference) 
then report where this difference was 
obtained 
10-11 
Randomization—
sequence generation 
8 
Method used to generate the random allocation 
sequence, including details of any restriction 
(e.g., blocking, stratification) 
7-9 
Randomization—
allocation concealment 
9 
Method used to implement the random allocation 
sequence (e.g., numbered containers or central 
telephone), clarifying whether the sequence was 
concealed until interventions were assigned 
7-9 
Randomization—
implementation 
10 
Who generated the allocation sequence, who 
enrolled participants, and who assigned 
participants to their groups 
7-9 
Blinding (masking) 
11 
Whether participants, those administering the 
interventions, and those assessing the outcomes 
were blinded to group assignment 
If blinding was not done, or was not 
possible, explain why 
10-11 

20 
 
Section 
Item 
Standard CONSORT Description 
Pragmatic Trials Extension 
Page 
Statistical methods 
12 
Statistical methods used to compare groups for 
primary outcomes; methods for additional 
analyses, such as subgroup analyses and adjusted 
analyses 
  
10-12 
Results 
 
Participant flow 
13 
Flow of participants through each stage (a 
diagram is strongly recommended)—specifically, 
for each group, report the numbers of participants 
randomly assigned, receiving intended treatment, 
completing the study protocol, and analyzed for 
the primary outcome; describe deviations from 
planned study protocol, together with reasons 
The number of participants or units 
approached to take part in the trial, 
the number which were eligible, and 
reasons for non-participation should 
be reported 
11-12 
Recruitment 
14 
Dates defining the periods of recruitment and 
follow-up 
11-12 
Baseline data 
15 
Baseline demographic and clinical characteristics 
of each group 
13-14 
Numbers analyzed 
16 
Number of participants (denominator) in each 
group included in each analysis and whether 
analysis was by “intention-to-treat;” state the 
results in absolute numbers when feasible (e.g., 
10/20, not 50%) 
14 
Outcomes and estimation 
17 
For each primary and secondary outcome, a 
summary of results for each group and the 
estimated effect size and its precision (e.g., 95% 
CI) 
17-20 
Ancillary analyses 
18 
Address multiplicity by reporting any other 
analyses performed, including subgroup analyses 
and adjusted analyses, indicating which are 
prespecified and which are exploratory 
17-20 
Adverse events 
19 
All-important adverse events or side effects in 
each intervention group 
20 
Discussion 
 
Interpretation 
20 
Interpretation of the results, considering study 
hypotheses, sources of potential bias or 
imprecision, and the dangers associated with 
multiplicity of analyses and outcomes 
 
21-23 
Generalizability 
21 
Generalizability (external validity) of the trial 
findings 
Describe key aspects of the setting 
which determined the trial results. 
Discuss possible differences in other 
settings where clinical traditions, 
health service organization, staffing, 
or resources may vary from those of 
the trial 
21-23 
Overall evidence 
22 
General interpretation of the results in the context 
of current evidence 
 
21-23 
 
 
 
 

21 
 
Statistical Analysis Plan Final Version 1.1, July 26, 2021 
 Design for UPMC Antibody Treatment and Evaluation Center 
Berry Consultants 
 
Summary of Statistical Analysis Plan Changes for Amendment 1 on July 26, 2021 
Page # 
Plan Section 
Reason for Change 
3 
Model Convergence 
Clarify handling of model convergence.  
6 
Modeling Treatment 
Heterogeneity 
Additional use of Pennsylvania statewide variant data as a surrogate for patient-level 
variant data  
6 
Appendix Read-out 
Provided details of the final analytical read-out.  
 
Trial Design Introduction 
This trial is a platform trial investigating the relative safety and efficacy of multiple monoclonal antibody (mAb) 
regimens for the treatment of COVID-19 illness. This document describes the statistical details for the trial 
investigating the relative efficacy of multiple mAb regimens for patients meeting the FDA emergency use 
authorization (EUA). 
 
The trial randomly allocates which mAb regimen patients receive and will evaluate their comparative effectiveness. 
Adaptive randomization will be utilized where mAb arms that are performing better will be given higher 
randomization probabilities. Different mAb arms may be added during the course of the trial and different mAb 
strategies may be dropped for futility. For the primary analysis of patients within the FDA EUA, there will be no 
control arm and all arms will be directly compared to all other arms for relative efficacy.  
Treatment Arms 
The trial may investigate multiple mAb strategy arms. Let k the number of active mAb strategies at any time in the 
trial. We label these arms as a=1,…,k.   
Primary Endpoint 
The primary endpoint in the trial is hospital-free days (HFDs). The endpoint is a composite of death and number of 
days alive and free of the hospital. The worst outcome is that a patient dies within 28 days. This outcome is labeled 
as a –1 HFDs. For patients who do not die within 28 days the primary endpoint is the number of days alive and free 
of hospitalization. For patients alive at day 28 the endpoint is characterized as an integer value with the number of 
days free of hospital admission, with possible values 0, 1, 2, 3, …, 28.  For statistical analyses the endpoint is 
modeled as an ordered categorical variable. If a patient has intervening days free of hospital and then has a re-
hospitalization the patient will be given credit for the intervening days as “free” of the hospital.  
Primary Analysis Population 
The primary analysis population, used for all adaptive analyses is the “As-Infused” population. The intent-to-treat 
population includes all patients randomized to an mAb arm. The “as-infused” population includes those patients that 
show up and are infused for their mAb. Given that all arms are a mAb arm, there is no anticipated relationship 
between lack of infusion and the assigned arm. Hence all adaptive analyses and safety analyses will be based on the 
as-infused population (where patients are coded by the mAb arm they receive). Patients who receive a randomized 
mAb allocation and are not infused may be tracked as a real-world evidence arm of outcome for a non-mAb control 
(see secondary analyses).  
Primary Analysis Model 

22 
 
The primary analysis model for the primary endpoint is a cumulative proportional odds model. Let the probability of 
an outcome of less than or equal to y be 𝜋𝑦= 𝑃𝑟(𝑌≤𝑦). Let a be the indicator of treatment arm (a=1,…,k). The 
model adjusts for the following baseline variables: 
1. ED or infusion center (0=infusion center, 1=ED) 
2. Age (with categories of <30, 30-39, 40-49, 50-59, 60-69, 70-79, and ≥80; 60-69 will be used as the 
referent) 
3. Sex (sex at birth, male is the referent) 
4. COVID-19 variant (0=unknown/uncollected (referent), categorical endpoint for each known variant) 
5. Time (two-week epochs of time are used for adjustments; the most current 4-week period is the referent) 
 
The primary analysis model is based on a cumulative logistic regression, where 𝜋𝑦= Pr(𝑌≤𝑦), where 
 
log (
𝜋𝑦
1−𝜋𝑦) = 𝛼𝑦−𝜃𝑎𝛿[𝑎] −∑
𝛽𝑣𝑗𝛿𝑣𝑗
4
𝑣=1
−𝜆[𝑡𝑖𝑚𝑒];  𝑦= −1,0,1,2, … 27. 
 
The additive covariate effects across all treatment arms for each patient are modeled through the 𝛽 parameters. The 
𝛿 parameters are indicator functions for the treatment arm and covariate values for the baseline covariates. The 
efficacy of the treatment arms is modeled with the 𝜃 parameters. The ordinal effect parameters (𝛼𝑦) are modeled 
with a Dirichlet distribution with equal weight on each outcome and a sum of 1.  
 
The baseline covariate effects are modeled with independent weak prior distributions: 
 
𝛽𝑣𝑗~𝑁(0,102), 𝑣= 1, … ,4;  𝑗= 1, … , 𝑛𝑣. 
 
The appropriate coefficients will be set to 0 within each covariate for identifiability (the goal will be the largest 
category set to 0).  
 
The effects of time are adjusted within the model using two-week epochs and a smoothing model over time. The 
modeling of the time effects is set up with the most current period (2 epochs combined being the most recent month 
are set to 0): 
 
𝜆1 ≡𝜆2 ≡0 
 
𝜆2 −𝜆1~𝑁(0, 0.152) 
 
𝜆𝑇−2𝜆𝑇−1+ 𝜆𝑇−2~𝑁(0, 𝜏𝜆
2); 𝑇≥3 
 
𝜏𝜆
2~𝐼𝐺(0.25, 0.00562) 
 
The prior distributions for the mAb treatment effects are weak: 
 
𝜃𝑎~𝑁(0,10), 𝑎= 1, … , 𝑘. 
Arms 
One of the treatment arms is selected as the referent arm for treatment effects and assigned a treatment effect 
of 𝜽𝒂= 𝟎. The treatment arm at the first adaptive analysis with the largest sample size will be specified as the 
referent arm for the remainder of the trial.  
Model Convergence  
Given the complexity of the model, conventions may be taken by the analysis team if there are convergence issues 
or model stability issues. For example, there may be outcome categories in the 30 possible primary outcome values 
(e.g., k number of hospital-free days, patient death)  that do not occur. If this happens at analysis, the cells will be 

23 
 
combined to achieve model convergence. For example, if the 4 hospital-free day outcome value does not occur it 
will be combined with 3, and so on, until every cell has occurred. Additional model stability conventions will be 
taken to preserve the model stability.  
Missing and Partial Data 
If there are missing covariates for a patient in the as-infused patient population, the following conventions will be 
used.  
1. If the treatment arm is missing the patient will be ignored. 
2. If a baseline covariate is missing the referent value for that covariate will be used 
 
For all model analyses, only patients who have achieved 28-days of follow-up from the date of the index infusion 
will be used in the analysis. No use or imputation of patient data for patients with less than 28 days will be 
conducted. 
 
Given the EHR-based data summaries there will be no missing outcome data. If there is deemed to be a corrupted 
outcome that patient will be ignored. Some patients may have 28 hospitalization-free days that at subsequent 
analyses are found to have out of system hospitalizations. The data will be updated at future analyses.  
Trial Inferences 
 
For the primary analysis, there is no “control” treatment and so all inferences are made comparing the individual 
treatment arms to each other. The main quantity of interest will be the relative odds ratio between any two 
treatments arms 
 
𝑂𝑅𝑖𝑗= exp(𝜃𝑖)/exp (𝜃𝑗). 
 
The posterior probability that the odds ratio for arm i compared to arm j is greater than 1 (signifying that treatment i 
is superior to treatment j) is used as a comparison between arms. Additionally, the posterior mean and 95% 
confidence interval between arms will be used to summarize relative treatment effects.  
Adaptive Design 
The trial design is adaptive. A sequence of frequent interim analyses will be conducted as a function of enrollment 
rate. The expectation is to conduct monthly adaptive analyses. The following decision triggers will be addressed at 
each adaptive analysis: 
 
Arm Inferiority: If one of the arms has a 99% chance of being inferior to any of the other available arms then the 
inferior arm will be declared inferior and may be removed from the trial. There may be conditions of the pandemic 
(e.g., variant frequency, new variants) or drug supply concerns that an arm is retained. 
 
Equivalence: Any two arms in the trial may reach a declaration of equivalence. It is anticipated that no actions 
would take when equivalence is reached but a declaration and public disclosure may be made. There is a sliding 
scale of equivalence with different levels of equivalence bounds. A declaration of equivalence will be tied to the 
equivalence level. Equivalence with a bound of d is declared if the posterior probability of the odds ratio is with d is 
at least 95%: 
 
Pr (
1
1 + 𝛿≤𝑂𝑅𝑖𝑗≤1 + 𝛿) ≥0.95 
 
The following levels are pre-defined: 
1. The first level of equivalence occurs when there is 95% posterior probability that the odds-ratio is within a 
bound of d=0.25 

24 
 
2. The second level of equivalence occurs when there is a 95% posterior probability that the odds-ratio is 
within a bound of d=0.20 
3. The third level of equivalence occurs when there is a 95% posterior probability that the odds-ratio is within 
a bound of d=0.15 
4. The fourth level of equivalence occurs when there is a 95% posterior probability that the odds-ratio is 
within a bound of d=0.10 
5. The fifth level of equivalence occurs when there is a 95% posterior probability that the odds-ratio is within 
a bound of d=0.05 
 
Combination Futility: When combinations of mAb are used the combination therapy may be compared to individual 
components of the combination. If there is more than a 95% probability that the effect of the combination mAb is no 
better than a 20% improvement in the odds ratio compared to each individual component then the combination may 
be declared not clinically relevantly superior (futile) to the individual components and the combination will be 
stopped. The comparison between a combination and the individual components will be declared when the 
combination is included within the platform trial. 
 
Trial Read-Outs: There may be periodic “unblinding” of the trial results. When an arm is removed for inferiority or 
futility, the results for the inferior and the superior or the combination and its components of the two arms in the 
trigger will be unblinded and publicly released. The trial will continue and will utilize all data for any new 
inferences in the trial. Additionally, there may be need for periodic disclosure of the current trial results, such as 
when FDA revokes authorization for a given mAb. These disclosures will be made and the trial will continue 
unchanged.  
 
Response-Adaptive Randomization: Assuming R arms available in the trial, response adaptive randomization will be 
utilized. The response adaptive randomization is conducted based on the probability that each arm has the optimal 
treatment effect (largest 𝜃). Let 𝑞𝑎 be the posterior probability that arm a is the optimal arm among the R arms in the 
randomization arm space: 
 
𝑞𝑎= Pr(𝜃𝑎= max{𝜃1, … , 𝜃𝑅}) 
 
The randomization probabilities are weighted toward being equal to maintain sufficient randomization to each arm 
and because the assignment is open-label, to prevent any obvious patterns of assignments. The allocation probability 
for each arm is  
 
Allocation probability = 
𝑞𝑎+1/𝑅
1+1 . 
 
If an arm joins the trial and has no data on the primary endpoint, then the value of 𝑞𝑎 for that arm is assigned to be 
1/R and the remaining arms probability of optimal summing to 1 −
1
𝑅. This convention will create fixed 
randomization to a new arm in the trial until there is at least 1 observation of the primary endpoint for modeling. 
 
Modeling Treatment Heterogeneity 
 
During the course of the trial, and during a trial read-out, inferences of relative treatment effect by different 
subgroups may be utilized. In these cases, the treatment effect is modeled as a function of subgroup within the single 
larger model. Each level of the subgroup will be identified and added as a covariate in the model and then the 
treatment effect, 𝜃𝑎, will be modeled separately within each subgroup, 𝑠= 1, … , 𝑆, as 𝜃𝑎1, 𝜃𝑎2, … , 𝜃𝑎𝑆, with 
hierarchical prior distribution: 
 
𝜃𝑎𝑠~𝑁(𝜇𝑎, 𝜏𝑎
2), 𝑎= 1, … , 𝑘;  𝑠= 1, … , 𝑆. 
 
𝜇𝑎~𝑁(0,102); 𝜏𝑎
2~𝐼𝐺(0.25, 0.1). 
 

25 
 
 
Assessment of COVID-19 Variants 
The COVID-19 variant type is a predefined subgroup for analysis. The variant type for each patient is unknown at 
baseline, but samples will be collected and reported upon sequencing. The posterior median odds ratios and 95% 
credible intervals will be reported for each arm comparison within each variant subset. In addition, because a large 
percentage of mAb infused patients will not have variant sequencing data (at least in near real time), a second 
“surrogate” approach will be used to compare the relative efficacy of the mAb regimens in the full treated 
population over time and by variant type.  
Using Pennsylvania statewide data, we will estimate the prevalence of a given variant type by time. Then, we can 
categorize patients into various time epochs relative to the variant prevalence. These categories will be provided 
based upon the changing distributions over time in our region. 
 
To consider the internal validity of this “surrogate” approach, we will use data from the subset of patients with 
actual genotype sequencing, compare the proportion of patients with prevalence of the variant type of interest across 
levels of the surrogate classification (i.e., Cochran Mantel-Haenszel test of trend).  
 
Adaptive Analyses Reporting 
The primary analysis will read out with summaries of the potential arm triggers for each arm actively in the 
“regimen space.”  These summaries will include the probability each mAb is optimal in the active regimen space 
and the randomization probabilities.  
 
A second analysis will be conducted with all arms (even those closed) in the regimen space, to report the probability 
each regimen is optimal among the larger regimen space.  
 
As the EHR system contains the same covariate and baseline data for both mAb treated and untreated patients, we 
have continuously updated flags for each patient in the system as being “mAb eligible” (or not) based on EUA 
criteria on the date the patient became COVID-19 positive. With appropriate selection, this affords 2 untreated 
control groups for analysis that we will describe using mean (SD), median [IQR}, and proportions, as appropriate: 
mAb eligible patients who were never randomly assigned to a respective mAb regimen, and mAb eligible patients 
who were randomly assigned to a mAb regimen yet did not receive treatment.  
 
Readout 
Introduction 
This document describes the detail of the analysis read-out from July 26, 2021. This document is an appendix to the 
Statistical Analysis Plan for the “UPMC Antibody Treatment and Evaluation Center” with the details for this 
analysis read-out.  
 
• 
We will analyze and report three treatment arms (below) as the first two have been administratively closed 
to enrollment due to FDA decisions. 
 
• 
Unblinded data from this and prior interim analyses can be shared with investigators after the last 
randomized allocations (June 25) to the two mAb treatment arms closed by FDA.  
 
• 
This unblinding is appropriate as future analyses of patients randomized to the third arm (C+I) will not be 
compared to the first two arms, there are no “control” arms as all patients receive mAb treatment, and 
future comparisons will be of C+I vs newer mAbs. 
 

26 
 
• 
Enrollment continues in the currently available treatment arms (C+I and S). “S” refers to sotrovimab, 
produced by GSK and Vir. 
Treatment Arms 
There are three treatment arms that will be included in this analysis. The treatment arms are 
1. B (Bamlanivimab) 
2. B+E (Bamlaniviman/Etesevimab combination)  
3. C+I (casirivimab/imdevimab combination) 
Primary Endpoint 
The primary endpoint for this read-out is hospital-free days.  
Primary Analysis Population 
The primary analysis population for this read-out is the “As-Infused” population. This analysis will include each 
patient randomized from March 10, 2021 until June 25, 2021. The date of the data set snapshot is on July 26, 2021.  
Primary Analysis Model 
The primary analysis model is as described in the trial SAP.  
 
The primary analysis for the primary endpoint is a cumulative proportional odds model. Let the probability of an 
outcome of less than or equal to y be 𝜋𝑦= 𝑃𝑟(𝑌≤𝑦). Let a be the indicator of treatment arm (a=1,…,k). The 
model adjusts for the following baseline variables: 
• 
ED or infusion center (0=infusion center, 1=ED) 
• 
Age (with categories of <30, 30-39, 40-49, 50-59, 60-69, 70-79, and ≥80; 60-69 will be used as the 
referent) 
• 
Sex (sex at birth, male is the referent) 
• 
Covid variant is not modeled in this primary analysis 
• 
Time (two-week epochs of time are used for adjustments; the most current 4-week period is the referent) 
 
The primary analysis model is based on a cumulative logistic regression, where 𝜋𝑦= Pr(𝑌≤𝑦), where 
 
log (
𝜋𝑦
1−𝜋𝑦) = 𝛼𝑦−𝜃𝑎𝛿[𝑎] −∑
𝛽𝑣𝑗𝛿𝑣𝑗
4
𝑣=1
−𝜆[𝑡𝑖𝑚𝑒];  𝑦= −1,0,1,2, … 27. 
 
The additive covariate effects across all treatment arms for each patient are modeled through the 𝛽 parameters. The 
𝛿 parameters are indicator functions for the treatment arm and covariate values for the baseline covariates. The 
efficacy of the treatment arms is modeled with the 𝜃 parameters. The ordinal effect parameters (𝛼𝑦) are modeled 
with a Dirichlet distribution with equal weight on each outcome and a sum of 1.  
 
The baseline covariate effects are modeled with independent weak prior distributions: 
 
𝛽𝑣𝑗~𝑁(0,102), 𝑣= 1, … ,4;  𝑗= 1, … , 𝑛𝑣. 
 
The appropriate coefficients will be set to 0 within each covariate for identifiability (the goal will be the largest 
category set to 0).  
 
The effects of time are adjusted within the model using two-week epochs and a smoothing model over time. The 
modeling of the time effects is set up with the most current period (2 epochs combined being the most recent month 
are set to 0): 

27 
 
 
𝜆1 ≡𝜆2 ≡0 
 
𝜆2 −𝜆1~𝑁(0, 0.152) 
𝜆𝑇−2𝜆𝑇−1+ 𝜆𝑇−2~𝑁(0, 𝜏𝜆
2); 𝑇≥3 
 
𝜏𝜆
2~𝐼𝐺(0.25, 0.00562) 
 
The prior distributions for the mAb treatment effects are weak: 
 
𝜃𝑎~𝑁(0,10), 𝑎= 1,2,3. 
 
Arms 
The treatment arms with the largest sample size should be selected as the referent arm (label a=1)_for 
treatment effects and assigned a treatment effect of 𝜽𝟏= 𝟎.  
Model Convergence  
Given the complexity of the model, conventions may be taken by the analysis team if there are convergence issues 
or model stability issues. For example, there may be outcome categories in the 30 possible primary outcome values 
(e.g., k number of hospital-free days, patient death)  that do not occur. If this happens at analysis, the cells will be 
combined to achieve model convergence. For example, if the 4 hospital-free day outcome value does not occur it 
will be combined with 3, and so on, until every cell has occurred. Additional model stability conventions will be 
taken to preserve the model stability.  
Missing and Partial Data 
If there are missing covariates for a patient in the as-infused patient population, the following conventions will be 
used. 
• 
If the treatment arm is missing the patient will be ignored. 
• 
If a baseline covariate is missing the referent value for that covariate will be used 
 
For all model analyses, only patients who have achieved 28-days of follow-up from the date of the index infusion 
will be used in the analysis. No use or imputation of patient data for patients with less than 28 days will be 
conducted. 
 
Given the HER-based data summaries there will be no missing outcome data. If there is deemed to be a corrupted 
outcome that patient will be ignored. Some patients may have 28 hospitalization-free days that at subsequent 
analyses are found to have out of system hospitalizations. The data will be updated at future analyses.  
Trial Inferences 
For the primary analysis, there is no “control” treatment and so all inferences are made comparing the individual 
treatment arms to each other. The main quantity of interest will be the relative odds ratio between any two 
treatments arms 
 
𝑂𝑅𝑖𝑗= exp(𝜃𝑖)/exp (𝜃𝑗). 
 
The posterior probability that the odds ratio for arm i compared to arm j is greater than 1 (signifying that treatment i 
is superior to treatment j) is used as a comparison between arms. Additionally, the posterior mean and 95% 
confidence interval between arms will be used to summarize relative treatment effects.  
 

28 
 
Arm Inferiority: If one of the arms has a 99% chance of being inferior to any of the other available arms then the 
inferior arm will be declared inferior and may be removed from the trial. There may be conditions of the pandemic 
(variation frequency, new variations) or drug supply concerns that an arm is retained. 
 
Equivalence: Any two arms in the trial may reach a declaration of equivalence. It is anticipated that no actions 
would take when equivalence is reached but a declaration and public disclosure may be made. There is a sliding 
scale of equivalence with different levels of equivalence bounds. A declaration of equivalence will be tied to the 
equivalence level. Equivalence with a bound of d is declared if the posterior probability of the odds ratio is with d is 
at least 95%: 
 
Pr (
1
1 + 𝛿≤𝑂𝑅𝑖𝑗≤1 + 𝛿) ≥0.95 
 
The following levels are pre-defined: 
• 
The first level of equivalence occurs when there is 95% posterior probability that the odds-ratio is within a 
bound of d=0.25 
• 
The second level of equivalence occurs when there is a 95% posterior probability that the odds-ratio is 
within a bound of d=0.20 
• 
The third level of equivalence occurs when there is a 95% posterior probability that the odds-ratio is within 
a bound of d=0.15 
• 
The fourth level of equivalence occurs when there is a 95% posterior probability that the odds-ratio is 
within a bound of d=0.10 
• 
The fifth level of equivalence occurs when there is a 95% posterior probability that the odds-ratio is within 
a bound of d=0.05 
 
Combination Futility: For comparing B+E to B the combination (B+E) will be compared to the individual 
component arm (B). If there is more than a 95% probability that the effect of the combination B+E is no better than 
a 20% improvement in the odds ratio compared to B, then the combination will be declared not clinically relevantly 
superior (combination futile) to N.  
Modeling Treatment Heterogeneity Across Variant Date Prevalence Epochs 
 
For this read out, the following time epochs will be modeled with different treatment effects using the Treatment 
Heterogeneity analysis model.  
 
The treatment effect, 𝜃𝑎, will be modeled separately within each epoch, 𝑠= 1, … , 𝑆, as 𝜃𝑎1, 𝜃𝑎2, … , 𝜃𝑎𝑆, with 
hierarchical prior distribution: 
𝜃𝑎𝑠~𝑁(𝜇𝑎, 𝜏𝑎
2), 𝑎= 1, … ,3;  𝑠= 1, … , 𝑆. 
 
𝜇𝑎~𝑁(0,102); 𝜏𝑎
2~𝐼𝐺(0.25, 0.1). 
 
The following time epochs are specified: 
1. March 10 - March 31 
2. April 1 – April 30 
3. May 1 – May 31 
4. June 1 – June 25 
 
 
 
 
 
 
 
 

29 
 
Specific Analyses 
# 
Status 
Population 
Endpoint 
Other 
1 
Primary 
As-Infused to B, B+I, or C+I on or 
before June 25, 2021 
HFD 
 
2 
Variant 
Secondary 
As-Infused to B, B+I, or C+I on or 
before June 25, 2021 
HFD 
Differential efficacy by time epoch 
specified 
 
The Primary Analysis  
Quantity of Interest 
Posterior Probability 
B+E superior to B 
 
B+E combination futile to B 
 
C+I superior to B 
 
C+I superior to B+E 
 
 
Statistical Triggers Met for Equivalence with Delta Ranges of Equivalence 
Odds-Ratio 
Parameter 
Mean 
SD 
Median 
95% Credible Interval 
Age < 30 
 
 
 
 
Age 30 – 39 
 
 
 
 
Age 40 – 49 
 
 
 
 
Age 50 – 59  
 
 
 
 
Age 60 – 69  
1 
 
 
 
Age 70 – 79   
 
 
 
 
Age 80+ 
 
 
 
 
Female 
 
 
 
 
Time Bucket 1 
 
 
 
 
… 
 
 
 
 
Time Bucket k-1 
 
 
 
 
Referent Arm 
1 
 
 
 
Arm #2 
 
 
 
 
Arm #3 
 
 
 
 
Arm #2/Arm #3 
 
 
 
 
 
 
Graphical summaries 
1. Stacked bar plots and cumulative distributions of HFDs by treatment arm 
2. Stacked bar plots and cumulative distributions of HFDs by time epochs 
3. Stacked bar plots and cumulative distributions of HFDs by sex 
4. Stacked bar plots and cumulative distributions of HFDs by treatment arm by time epoch 
 
The Variant Secondary Analysis  
Quantity of Interest 
Posterior Probability 
B+E superior to B 
 
B+E combination futile to B 
 
C+I superior to B 
 
C+I superior to B+E 
 
 
 
Statistical Triggers Met for Equivalence with Delta Ranges of Equivalence 
Odds-Ratio 
Parameter 
Mean 
SD 
Median 
95% Credible Interval 
Age < 30 
 
 
 
 
Age 30 – 39 
 
 
 
 
Age 40 – 49 
 
 
 
 
Age 50 – 59  
 
 
 
 
Age 60 – 69  
1 
 
 
 
Age 70 – 79   
 
 
 
 
Age 80+ 
 
 
 
 
Female 
 
 
 
 
Time Bucket 1 
 
 
 
 
… 
 
 
 
 

30 
 
Time Bucket k-1 
 
 
 
 
Referent Arm 
1 
 
 
 
For each time-epoch 
Arm #2 
 
 
 
 
Arm #3 
 
 
 
 
Arm #2/Arm #3