Emulation of a Comparative Effectiveness Study of Pembrolizumab and Chemotherapy vs. Chemotherapy for the First-line Treatment of Metastatic Non-small Cell Lung Cancer
Conditions: Non-small Cell Lung Cancer, Metastatic Lung Cancer
Sponsor: Aetion, Inc.

 
 
 
1. Title Page 
Title 
CARE Initiative Pilot Emulation Study: Emulation of a comparative effectiveness study 
of pembrolizumab and chemotherapy vs. chemotherapy for the first-line treatment of 
metastatic non-small cell lung cancer 
Objective 
In alignment with the KEYNOTE-189 randomized controlled trial, the objective is to 
assess the comparative effectiveness of pembrolizumab, platinum therapy, and 
pemetrexed vs. platinum therapy and pemetrexed in patients with metastatic non-
small cell lung cancer and without EGFR/ALK mutations, and to calibrate results 
against the KEYNOTE-189 trial. 
Protocol version 
1.2 
Last update date 
9/8/2023 
Protocol authors 
David Merola, PharmD, PhD 
Aetion, Inc 
50 Congress St.  
Suite 1025 
Boston, MA 02109 
 
Jennifer Rider, ScD, MPH 
Aetion, Inc 
50 Congress St.  
Suite 1025 
Boston, MA 02109 
 
Ulka Campbell, PhD 
Aetion, Inc 
5 Penn Plaza 
7th floor 
New York, NY 10001 
 

 
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David Lenis, PhD 
Aetion, Inc 
5 Penn Plaza 
7th floor 
New York, NY 10001 
Sponsor 
This work was collectively sponsored by Abbvie, Amgen, AstraZeneca, Bayer, Gilead, 
Janssen, and Pfizer 
Conflict of interest 
DM, JR, UC, and DL are shareholders and employees of Aetion, Inc. 
Study ID (clinicaltrials.gov) 
NCT05908799 
 

 
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Table of contents 
1. Title Page ................................................................................................................................................................................................ 1 
2. Abstract ................................................................................................................................................................................................ 4 
3. Amendments and updates .............................................................................................................................................................. 4 
4. Research question and objectives................................................................................................................................................. 5 
5. Research methods ............................................................................................................................................................................. 6 
5.1. Study design .............................................................................................................................................................................................................................................................................. 6 
5.2. Setting ............................................................................................................................................................................................................................................................................................ 9 
Context and rationale for definition of time 0 (and other primary time anchors) for entry to the study population ........................ 9 
Context and rationale for study inclusion criteria:..................................................................................................................................................................................... 11 
Context and rationale for study exclusion criteria.................................................................................................................................................................................... 12 
5.3. Variables .................................................................................................................................................................................................................................................................................... 15 
Context and rationale for exposure(s) of interest .................................................................................................................................................................................... 15 
Context and rationale for outcome(s) of interest ..................................................................................................................................................................................... 17 
Context and rationale for follow-up  ................................................................................................................................................................................................................... 18 
Context and rationale for covariates .................................................................................................................................................................................................................. 21 
5.4. Data analysis .........................................................................................................................................................................................................................................................................26 
Context and rationale for analysis plan ...........................................................................................................................................................................................................26 
5.5. Data sources ......................................................................................................................................................................................................................................................................... 36 
Context and rationale for data sources .......................................................................................................................................................................................................... 36 
5.6. Data management...........................................................................................................................................................................................................................................................42 
5.7. Quality control .......................................................................................................................................................................................................................................................................42 
5.8. Study size and feasibility ..............................................................................................................................................................................................................................................42 
5.8.1. CONSORT-style diagram for the primary intention-to-treat cohort .....................................................................................................................................42 
5.8.2. Initial analysis of study cohort, unstratified by exposure (primary intention-to-treat cohort) .................................................................... 43 
5.8.3. Final power and covariate balance assessment, unstratified by exposure (primary intention-to-treat cohort) ........................ 45 
6. Limitation of the methods .............................................................................................................................................................. 46 
7. References .......................................................................................................................................................................................... 47 
8. Appendices ........................................................................................................................................................................................ 48 
 

 
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2. Abstract 
The Coalition to Advance Real-World Evidence through Randomized Controlled Trial Emulation (CARE) Initiative is a program designed to build an 
empirical evidence base for the use of real-world data in clinical and regulatory decision-making. Using results from randomized clinical trials as a 
benchmark for causal effect estimates, a series of randomized controlled trial emulations will be conducted using real-world data (RWD) to better 
understand when observational methods and secondary data collected from routine healthcare practice can provide reliable conclusions on drug 
effectiveness. This protocol describes the first of several studies and seeks to emulate the KEYNOTE-189 randomized controlled trial.1 KEYNOTE-189 aimed to 
assess the comparative effectiveness of pembrolizumab, platinum therapy, and pemetrexed vs. platinum therapy and pemetrexed in patients with 
metastatic non-small cell lung cancer (NSCLC) and without epidermal growth factor receptor (EGFR)/anaplastic lymphoma kinase (ALK) gene mutations. 
The KEYNOTE-189 trial provided primary evidence of effectiveness to support regulatory approval of pembrolizumab for this patient population.2 This 
emulation study will use data derived from an electronic health record database linked with a cancer registry. 
3. Amendments and updates 
Version date 
Version number 
Section of protocol 
Amendment or update 
Reason 
8-10-2023 
V1.1 
 Study Design, 
Variables, Data 
Analysis 
The treatment contrast of the per-
protocol analyses was corrected. 
The term “cycles” was incorporated 
into the treatment regimen 
specification. 
perf_status variable was corrected 
to include a time window for the 
per-protocol analysis 
The comparator group 
regimen incorrectly stated 
that chemotherapy was to 
be used as maintenance 
therapy in portions of the 
protocol. The description of 
the treatment contrast was 
updated to state “cycles” 
address ambiguity. 
9-8-2023 
V1.2 
Data Analysis 
Time-varying covariates were 
removed from the marginal 
structural model specified for the 
per-protocol analysis 
Cluster-robust variance estimators 
were added as a possible 
Correction of a typo and 
permitting more flexibility in 
methods due to high 
dimensionality of data. 

 
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approach to deriving interval 
estimates. 
4.  Research question and objectives 
A summary comparison of the key elements of study design and analysis plan in the KEYNOTE-189 trial vs. this study is available in the appendix. 
Table 1.  Research question and objective 
 
Objective: 
In alignment with the KEYNOTE-189 trial, the objective is to compare the overall survival of 
patients receiving pembrolizumab, platinum therapy, and pemetrexed vs. platinum therapy 
and pemetrexed for the treatment of metastatic NSCLC without EGFR/ALK mutations, and to 
calibrate results against the KEYNOTE-189 trial. 
Causal effect of interest: 
Average treatment effect 
Hypothesis: 
Within the population of patients with the tumor type of interest who receive pemetrexed and 
a platinum therapy, those additionally treated with pembrolizumab will have better survival 
than those not treated with pembrolizumab. The results will be congruent with the KEYNOTE-
189 trial. 
Population (mention key inclusion-exclusion 
criteria): 
Patients with metastatic NSCLC who have not previously received systemic therapy for 
advanced disease and do not have EGFR or ALK mutations. 
Exposure: 
Initiation of pembrolizumab, pemetrexed, and platinum therapy (cisplatin or carboplatin)  
Comparator: 
Initiation of pemetrexed and platinum therapy (cisplatin or carboplatin)  
Outcome: 
Overall survival, defined as the time from treatment initiation to death due to any cause. 
Time (when follow-up begins and ends): 
Follow-up initiated on the day after exposure regimen ascertainment window and stopped at 
earliest of: death, loss to follow-up or end of available data 
Setting: 
Outpatient and inpatient routine care setting 
Main measures of effect: 
Hazard ratio (intention-to-treata) and 12-month survival proportion  

 
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a Intention-to-treat is defined by treatment initiation (i.e., agnostic to adherence) 
5.  Research methods 
5.1.  Study design 
Overview of key design elements of the KEYNOTE-189 trial: 
The KEYNOTE-189 trial was a randomized controlled, double-blind trial comparing the efficacy of pembrolizumab vs. placebo when taken in combination 
with platinum therapy (investigator’s choice of cisplatin or carboplatin) and pemetrexed for the first-line treatment of non-squamous non-small cell lung 
cancer without EGFR or ALK mutations. Patients with active brain metastases, on chronic systemic steroids or other immunosuppressive agents, and a 
history of other primary malignancies were excluded. Follow-up was initiated at treatment randomization (baseline) and stopped at the earliest of death, 
unacceptable toxic effects, investigator decision, or patient withdrawal of consent. The primary outcome was overall survival, defined as the time from 
randomization to death due to any cause. An intention-to-treat analysis was performed to estimate the relative hazard of mortality and 12-month survival 
probability using a Cox proportional hazards model and Kaplan-Meier estimator, respectively. More details on the design elements of the KEYNOTE-189 
trial and how these compare to the present real-world evidence emulation study are available in the appendix along with study design diagrams for each 
of the studies’ designs. 
Overview of key design elements of the real-world evidence emulation study:  
Primary (intention-to-treat) analysis: This is a retrospective cohort study comparing patients with incident use of pembrolizumab, pemetrexed, and 
platinum therapy vs. pemetrexed and platinum therapy following the first indication of metastatic disease in their electronic health record. The study 
design is illustrated in Figure 1-A. Patients will be required to have a diagnosis of non-small cell lung cancer and have no evidence of EGFR or ALK 
mutations prior to treatment initiation. Patients with evidence of brain metastases in the 2 weeks prior to treatment initiation, records in the 90 days prior 
to treatment initiation indicating use of systemic steroids or other immunosuppressive agents, or a history of other primary malignancies will be excluded. 
Exposure will be ascertained within a 14-day window of time (i.e., “exposure regimen ascertainment window”), beginning on the day of first record 
indicating use of a study drug (pembrolizumab, pemetrexed, or platinum therapy), and ending 14-days later (“time zero"). Patients’ exposure will be 
classified based on the receipt of all study drugs in each regimen within the 14-day exposure regimen ascertainment window, which will be incident with 
respect to metastatic disease; study drug initiation may occur on or after date of metastatic disease. Patients will be required to be alive and at risk of the 
event throughout the exposure ascertainment window. Confounders will be assessed during the baseline period, defined as all time prior to and including 
time zero. Follow-up will begin on the day after the 14-day exposure regimen ascertainment window and proceed until the outcome event (death), loss to 
follow-up (>90-day gap in health record activity), 640 days of follow-up, or administrative end of available data occurs (i.e., last date of record in the 
dataset). Time-fixed inverse probability weights will be used to adjust for potential confounding. The effect of treatment initiation on the outcome of 

 
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interest will be estimated using an intention-to-treat estimator. This primary analysis will be agnostic to treatment adherence and will not account for 
potential selection bias induced by informative censoring or time-varying confounding. Rationale for this design is described in the next section. 
Secondary (per-protocol) analysis: A secondary analysis will be conducted that will employ the same eligibility criteria as the primary analysis, but with a 
modified exposure definition and analysis plan. Two static treatment regimens will be compared in the per-protocol population as follows: 
1. 
Exposure: First-line, concurrent use of pembrolizumab, platinum therapy (cisplatin or carboplatin), and pemetrexed every 21 days for 4 cycles, 
followed by concurrent use of pembrolizumab and pemetrexed every 21 days thereafter until intolerance/toxicity, progression, or mortality. 
2. Comparator: First-line, concurrent use of platinum therapy (cisplatin or carboplatin) and pemetrexed every 21 days for 4 cycles, followed by 
pemetrexed every 21 days thereafter until intolerance/toxicity, progression, or mortality. Upon failure of the original first-line regimen, may use 
pembrolizumab monotherapy every 21 days. 
The study design is illustrated in Figure 1-B. Given that some components of the exposure and comparator treatment regimens are overlapping (i.e., 
platinum therapy and pemetrexed), a “clone and censor” approach3 described by Hernan and Robins will be undertaken. In this approach, the analytic 
cohort will be cloned to create 2 exact copies of every patient, where each “clone” is assigned to one of two static treatment regimens (defined in 
variables section) and censored upon deviation from their assigned regimen. The justification for using this method is that if a patient experiences an 
event during follow-up but has a treatment pattern that complies with both exposure strategies of interest (e.g., taking platinum therapy and pemetrexed 
between days 1-10 of follow-up), the event will be attributed to both groups rather than one group differentially. In alignment with the KEYNOTE-189 trial, 
cross-over from the non-pembrolizumab group to pembrolizumab monotherapy will be permitted. For this secondary analysis, patients will be required to 
have adhered to static treatment regimens (i.e., treatment regimens dependent only on treatment history, but not upon other covariates) during follow-
up that are detailed in the variables section of this protocol. Time-fixed confounders (assessed at baseline) and time-varying confounders (assessed at 
each interval of follow-up) will be controlled for using inverse probability of treatment weighting. Potential informative censoring induced by the artificial 
censoring rules imposed in this analysis will be accounted for using inverse probability of censor weighting. The effect of the treatment on the outcome 
will be estimated using a per-protocol estimator, where follow-up will begin on the day after treatment initiation and proceed until an outcome event 
(death), treatment cessation (deviation from regimens described in the variables section of this protocol), loss to follow-up (>90-day gap in health record 
activity), 640 days of follow-up, or end of available data occurs (i.e., last date of record in the dataset). Rationale for this design is described in the next 
section. 
 
 

 
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A 
 
B 
 
Figure 1. Example patient timelines for the primary (intention-to-treat) and secondary (per-protocol) analysis. Panel A Primary (intention-to-treat) 
analysis. All patients shown are distinct and are classified on the basis of their treatment initiation within a 14-day exposure regimen ascertainment 
window that is incident with respect to each patient’s first record of metastasis. Notably, this analysis is agnostic to adherence and patients are 
followed up from the end of the exposure regimen ascertainment window until occurrence of an outcome or censor event. Additionally, multiple records 
for each study drug may occur within the ascertainment window, as observed in Patients D and H. Panel B Secondary (per-protocol) analysis. Patients 
1-4 are “cloned” and assigned to each treatment strategy upon initiation of a study drug. Patient 1 adheres perfectly to the exposure treatment strategy 
and experiences the outcome event; therefore, all person-time observations are contributed to the exposed group. Patient 1 also contributes a small 
amount of person-time to the comparator group, as the patient’s treatment history is aligned with the comparator treatment strategy for some follow-
up time prior to initiation of D3. Patient 2 in the “exposed” group is censored upon initiation of a cancer treatment that is not a part of her assigned 
regimen (Dx) and does not contribute person-time to the comparator group since D3 is not a component of the comparator regimen. Patient 3 in the 
”exposed” group is censored at the end of the 21-day interval because that is the point at which the patient’s treatment history is no longer compliant 
with the exposure strategy. Patient 4 in the “comparator” group has a treatment history that is compatible with the comparator strategy and 
experiences an outcome event but is censored in the “exposed” group due to a lack of “compliance” with the exposed treatment strategy. Notably, if an 
event occurs during an interval of follow-up time that is compatible with both treatment strategies, the outcome is attributed to both groups. 
 

 
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Rationale for study design choices:  
Study design decisions reflect the bias-variance trade-off that is inherent to nearly all epidemiologic investigations, as well as the goal to emulate the 
KEYNOTE-189 trial results. The primary analysis was constructed to maximize the number of eligible patients for analysis and resultant statistical power, 
while the secondary analysis was designed to mitigate bias as much as possible at the expense of a greater variance. Both analyses use weighting to 
obtain marginal treatment effects and censor patients administratively at 21 months (640 days) of follow-up, which may align more with the randomized 
controlled trial result than conditional treatment effects. The 14-day exposure regimen ascertainment window in the primary analysis was chosen to allow 
for some variability in treatment patterns that are likely to occur in routine care. Ascertaining exposure in shorter intervals (e.g., on a single day) may be 
too restrictive and result in few eligible patients, while longer time periods may result in treatment patterns in the study population that are vastly different 
from the trial. In the per-protocol analysis, a 21-day treatment administration was chosen to align with the dosing schedule prescribed in the KEYNOTE-189 
trial. 
In the primary analysis, the use of inverse probability weighting and time-fixed treatment strategies may allow certain patients to contribute information 
that would have otherwise been discarded during matching procedures or lack of treatment adherence. Furthermore, treatment adherence may be 
relatively high among patients with lung cancer due to the nature of the disease. Adjustment for informative censoring can introduce additional variability 
(and reduce precision of) effect estimators, due to the presence of extreme weights. Although the analytic approach in the primary analysis more closely 
resembles that of the KEYNOTE-189 trial, permitting adjustment for informative censoring and assessing the per-protocol effect (as will be done in the 
secondary analysis) may give rise to results that more closely align with randomized controlled trials, where relatively high levels of adherence are often 
observed. At 21 months of follow-up, 33.8% and 17.8% of patients were on the assigned therapy in the pembrolizumab-containing and placebo-containing 
group of the KEYNOTE-189 trial, respectively.1 Lastly, time-varying confounding is likely to be present in the real-world oncology population, where clinical 
condition and performance status can change dramatically over follow-up. 
5.2.  Setting 
Context and rationale for definition of time 0 (and other primary time anchors) for entry to the study population  
The criteria defining time zero for the primary (intention-to-treat) and secondary (per-protocol) populations are specified below in Table 2 and illustrated 
in Figure 1 in the appendix. In the primary population, time zero will be defined by the end of the 14-day exposure regimen ascertainment window (incident 
with respect to the first record of metastasis) described in the Study Design section. In the secondary population, time zero will be defined by incident 
receipt of a study drug of interest (also incident with respect to an indication of metastasis in the data source). Time zero will be restricted to dates on or 
after October 7, 1995 to reflect the timing of availability of published evidence supporting the use of platinum-based therapies in the first-line advanced 
disease setting.4–6 Permitting “historical” controls will allow for a greater study sample size and precision of estimates, under the assumption that 
decisions driving treatment initiation are constant over time. A “washout” window, in which a patient has no records indicating study drug use between 

 
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their indicator of metastatic disease to initiation of first study drug of interest, will be applied to the primary and secondary populations to ensure that the 
exposure regimen identified is each patient’s first-line treatment for metastatic disease. 
Table 2.  Operational Definition of Time 0 (index date) and other primary time anchors 
 
Study population name(s) 
Time Anchor Description 
Number 
of entries 
Type of 
entry 
Washout 
window 
Code 
Typea 
Incident 
with 
respect 
to… 
Patients initiating first-line 
treatment for metastatic 
disease (primary, intention-to-
treat population) 
For the exposed and comparator groups, 
time zero will be defined as the last day of 
the 14-day exposure regimen 
ascertainment window. Exposure regimen 
ascertainment is described in the variables 
section of this protocol. 
Single 
entry 
Incident 
occurrence 
of multi-
drug 
exposure 
sequence 
[first date of 
metastatic 
disease 
indicatorb, first 
date of study 
treatment of 
interest 
administration] 
NDC, 
HCPCS/CP
T, RxNorm 
Metastati
c disease 
indicatorb 
Patients initiating first-line 
treatment for metastatic 
disease (secondary, per-
protocol population) 
For the exposed and comparator groups, 
time zero will be defined as the first day 
after incident occurrence of any study drug 
(i.e., platinum therapy, pemetrexed, or 
pembrolizumab). 
Single 
entry 
Incident 
study drug 
occurrence 
[first date of 
metastatic 
disease 
indicatorb, first 
date of study 
treatment of 
interest 
administration] 
NDC, 
HCPCS/CP
T, RxNorm 
Metastati
c disease 
indicatorb 
Note: Some study drugs are used in the adjuvant and neoadjuvant settings, as well as the metastatic disease setting. To distinguish the indication for 
these study drugs, treatments occurring after the first observed metastatic disease indicator will be deemed as treatments for metastatic disease. 
a See appendix for listing of clinical codes for each study parameter 
b Indicators of metastatic disease include records indicating stage 4 disease, indicators of metastasis derived from the data vendor, or International 
Classification of Diseases for Oncology (ICD-O) diagnostic codes indicating metastasis. Information on metastatic disease is drawn from a linked tumor 
registry and tumor morphology descriptors contained within the health record. 

 
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Context and rationale for study inclusion criteria:  
Operational definitions for study inclusion criteria are shown in Table 3, and were selected to proxy those of the KEYNOTE-189 trial as closely as possible.1 
Numerous variables in the data source were used to define each criterion in order to harness all available information. For example, treatments were 
selected using National Drug Code (NDC), Healthcare Common Procedure Coding System (HCPCS)/Current Procedural Terminology (CPT), and RxNorm 
codes. “Observability” requirements (e.g., continuous activity in health record database) will not be imposed during the baseline period to maximize study 
sample size and statistical precision of outcome estimates. 
Platinum therapy is used in both the adjuvant/neoadjuvant setting, as well as the metastatic setting. Therefore, in defining “No prior systemic treatment 
for metastatic disease,” the date range for assessment (between stage IV diagnosis and the index treatment) was chosen to differentiate between 
therapies used perioperatively and therapies used in the metastatic setting. We assume that patients are being seen within-network and all relevant 
healthcare encounters are captured during the baseline period. 
Table 3.  Operational Definitions of Inclusion Criteria 
Criterion 
Description 
Variable (Data Table 
Name(s)) 
Order of 
application 
Assessment 
windowa 
Code 
Type(s)b 
Lung cancer 
Diagnosis code indicating 
cancer of the lung or bronchus 
code (Diagnosis) 
tumor_site_code (Tumor, 
Tumor Properties) 
1 
[-all 
available 
data, 0] 
ICD 
Stage IV disease 
Evidence of metastasis 
metastatic (Tumor) 
stage_code (Tumor) 
morphology_code 
(Tumor, Tumor Properties) 
code (Diagnosis) 
2 
[-all 
available 
data, 0] 
ICD, derived 
Non-small cell histology 
Lack of evidence of other lung 
cancer types that are NOT non-
small cell lung cancer (e.g., 
mesothelioma, small cell 
carcinoma, squamous cell 
carcinoma, etc) 
morphology_code 
(Tumor) 
tumor_site_code (Tumor) 
3 
[-all 
available 
data, 0] 
ICD 
No epidermal growth 
factor receptor (EGFR) or 
Does not have evidence of EGFR 
or ALK mutations. 
code (Genomic) 
4 
[-all 
available 
data, 0] 
HGVS 

 
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anaplastic lymphoma 
kinase (ALK) mutations 
No prior systemic 
treatment for metastatic 
disease 
Patient has no evidence of use 
of guideline-recommended 
systemic cancer therapyc for 
metastatic NSCLC  
code (Medication Drug, 
Medication Ingredient, 
Procedure) 
5 
ITT analysis: 
[-Stage IV 
disease, -15] 
 
PP analysis: 
[-Stage IV 
disease, -1] 
CPT/HCPCS, 
NDC, RxNorm 
Note: Criteria in this table apply to both, the primary and secondary analyses.  
CPT = Current Procedural Terminology, HCPCS = Healthcare Common Procedure Coding System, HGVS = Human Genome Variation Society, ICD = 
International Classification of Diseases, LOINC = Logical Observation Identifiers Names and Codes, NSCLC = non-small cell lung cancer, RxNorm = a 
standardized nomenclature for clinical drugs 
a Assessment times shown are inclusive. 
b See appendix for listing of clinical codes for each study parameter. 
c Systemic cancer therapies were identified from the National Comprehensive Cancer Network Clinical Practice Guidelines for non-squamous non-small 
cell lung cancer and include: pembrolizumab, carboplatin, cisplatin, pemetrexed, atezolizumab, paclitaxel, nivolumab, ipilimumab, cemiplimab, 
tremelimumab, durvalumab, docetaxel, etoposide, gemcitabine, and vinorelbine. 
 
Context and rationale for study exclusion criteria 
As with the inclusion criteria, operational definitions for study exclusion criteria were selected to mimic those of the KEYNOTE-189 trial (Table 4).1 Numerous 
variables in the data source were used to define each criterion in order to harness all available information.  
Table 4.  Operational Definitions of Exclusion Criteria 
Criterion 
Description 
Variable (Data Table 
Name(s)) 
Order of 
application 
Assessment 
windowa 
Code 
Type(s)b 
Applied to 
study 
populations: 
Squamous cell tumor 
morphology 
The patient has evidence of 
squamous cell lung cancer 
morphology 
morphology_code 
(Tumor) 
 
1 
[-all 
available 
data, 0] 
ICD, derived 
Primary and 
secondary 

 
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Biologic cancer therapy 
for metastatic disease  
Evidence of biologic therapyc for 
metastatic NSCLC (including ALK- and 
EGFR-directed therapies) 
code (Medication 
Drug, Medication 
Ingredient, 
Procedure) 
2 
ITT analysis: 
[-all 
available 
data, 0] 
CPT/HCPCS, 
NDC, RxNorm 
Primary and 
secondary 
Prior non-lung 
malignancy 
Has evidence of primary 
malignancies beyond the lung tissue, 
except basal cell carcinoma of the 
skin, superficial bladder cancer, 
squamous cell carcinoma of the skin, 
in situ cervical cancer, or other in situ 
cancers 
tumor_site_code 
(Tumor) 
3 
[-all 
available 
data, 0] 
ICD 
Primary and 
secondary 
CNS metastases 
Has no diagnosis codes of central 
nervous system (CNS) metastases 
and/or carcinomatous meningitis in 
the 2 weeks prior to treatment 
initiation 
code (Diagnosis) 
tumor_site_code 
(Tumor) 
4 
ITT analysis: 
[-28, -15] 
PP analysis: 
[-14, -1] 
ICD 
Primary and 
secondary 
Treatment with disease 
modifying agents, 
corticosteroids, or 
immunosuppressive 
drugs 
Has a record of systemic treatmentsd 
for autoimmune disease in the 2 
years prior to the index date 
code (Medication 
Drug, Medication 
Ingredient, 
Procedure) 
5 
[-730, 0] 
CPT/HCPCS 
NDC, RxNorm 
Primary and 
secondary 
Chronic corticosteroids 
Has at least two records 60 days 
apart indicating use of corticosteroid 
agentse in the 3 months prior to the 
index date 
code (Medication 
Drug, Medication 
Ingredient, 
Procedure) 
6 
[-90, 0] 
CPT/HCPCS 
NDC, RxNorm 
Primary and 
secondary 
Prior treatment with PD-L1 
or PD-L2 agent or 
antibody targeting other 
immuno-regulatory 
receptors or mechanisms. 
Has evidence of use of one of the 
following agents prior to the index 
date: pembrolizumab, nivolumab, 
cemiplimab, atezolizumab, 
durvalumab 
code (Medication 
Drug, Medication 
Ingredient, 
Procedure) 
7 
ITT analysis: 
[-all 
available 
data, -15] 
PP analysis: 
[-all 
available 
data, -1] 
CPT/HCPCS 
NDC, RxNorm 
Primary and 
secondary 

 
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Mortality at baseline 
Patients experiencing mortality during 
the exposure regimen ascertainment 
window or during the assessed 
baseline period. 
 
Note: Both, death during the baseline 
period and death during the exposure 
regimen ascertainment window will 
be evaluated separately. It is possible 
for a patient to have a recorded 
mortality event during the baseline 
period because mortality is assumed 
to occur on the first day of the month 
and year of death recorded in the 
database.  
 
Exact dates of mortality are not 
recorded in the data to preserve 
patient privacy. 
month_year_death 
(Patient 
Demographic) 
8 
ITT analysis: 
[-14, 0] 
PP analysis:  
[-all 
available 
data, 0] 
Derived 
Primary and 
secondary 
CPT = Current Procedural Terminology, HCPCS = Healthcare Common Procedure Coding System, HGVS = Human Genome Variation Society, ICD = 
International Classification of Diseases, LOINC = Logical Observation Identifiers Names and Codes, NSCLC = non-small cell lung cancer, RxNorm = a 
standardized nomenclature for clinical drugs 
a Assessment times shown are inclusive. 
b See appendix for listing of clinical codes for each study parameter. 
c Biologic therapies, including those targeting EGFR and ALK mutations, identified from the National Comprehensive Cancer Network Clinical Practice 
Guidelines for non-squamous non-small cell lung cancer: erlotinib, afatinib, gefitinib, dacomitinib, crizotibib, and cetuximab. 
d Systemic treatment(s) for autoimmune disease were defined based on investigators’ substantive knowledge as follows: methotrexate, sulfasalazine, 
hydroxychloroquine, leflunomide, azathioprine, etanercept, adalimumab, infliximab, certolizumab, golimumab, anakinra, tocilizumab, sarilumab, 
abatacept, rituximab, tofacitinib, barivitinib, upadacitinib, mycophenolate. 
e Corticosteroid drugs were defined using investigators’ substantive knowledge and include the following: prednisone, prednisolone, methylprednisolone, 
dexamethasone, hydrocortisone. 

 
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5.3.  Variables 
Context and rationale for exposure(s) of interest 
Primary analysis (intention-to-treat) exposure definition: The two treatment strategies that will be compared in the intention-to-treat population will be 
as follows: 
1. 
Exposure: Initiation of first line pembrolizumab, platinum therapy, and pemetrexed within a 14-day exposure assessment window 
2. Comparator: Initiation of first line platinum therapy and pemetrexed within a 14-day exposure assessment window  
A patient will be classified as having “initiated” each multi-drug regimen if they had an incident order, dispensation, and/or administration for all 
component study drugs of that regimen within a 14-day exposure assessment window. For both treatment groups, the exposure regimen ascertainment 
window will be incident with respect to the first record of metastatic disease to distinguish between therapies that are being used in the neoadjuvant or 
adjuvant setting versus those being used as first-line therapy for metastatic disease. All available information in the data source will be used to ascertain 
exposure status, including procedure codes (i.e., HCPCS/CPT “J” codes), as well as NDC and RxNorm codes available from the medication-related data 
tables. This exposure definition is illustrated in Figure 1-A in the Study Design section. 
Secondary analysis (per-protocol) exposure definition: The two static treatment regimens that will be compared in the per-protocol population are as 
follows: 
1. 
Exposure: First-line, concurrent use of pembrolizumab, platinum therapy (cisplatin or carboplatin), and pemetrexed every 21 days for 4 cycles, 
followed by concurrent use of pembrolizumab and pemetrexed every 21 days thereafter until intolerance/toxicity, progression, or mortality. 
2. Comparator: First-line, concurrent use of platinum therapy (cisplatin or carboplatin) and pemetrexed every 21 days for 4 cycles, followed by 
pemetrexed every 21 days thereafter until intolerance/toxicity, progression, or mortality. Upon failure of the original first-line regimen, may use 
pembrolizumab monotherapy every 21 days. 
 
These real-world data treatment regimen definitions were chosen as a compromise between 1) the heterogeneity of adherence patterns observed in 
routine clinical practice, and 2) the goal of emulating the KEYNOTE-189 trial as closely as possible. By permitting patients to take treatments any time 
within a 21-day gap more patients may be eligible for classification into an exposure of interest without compromising the general adherence pattern 
imposed in the KEYNOTE-189 trial. Discontinuation of treatments or treatment switch will be assumed to be due to intolerance/toxicity or progression 
events. 
A patient will be classified as having “initiated” each multi-drug regimen if they had an incident order, dispensation, and/or administration for any 
component study drugs of that regimen. As with the intention-to-treat exposure definition, the “incident” order, dispensation, and/or administration will be 
with respect to the first record of metastatic disease. Also similar to the primary analysis, all available information in the data source will be used to 

 
16 
 
determine exposure status, including procedure codes (i.e., HCPCS/CPT “J” codes), as well as NDC and RxNorm codes available from the medication-
related data tables. Patients with evidence of non-study drugs between the initial metastatic disease indicator and incident study drug use will be 
excluded from the analysis by design to ensure the study population being captured is receiving first-line treatment. Refer to Figure 1-B in the Study 
Design section for several illustrations of the static treatment regimens.  
Table 5.  Operational Definitions of Exposure  
Exposure group name(s) 
Details 
Exposure 
Regimen 
Ascertainme
nt Windowa 
Variable (Data 
Table Name(s)) 
Code Typeb 
Applied to 
study 
populations: 
Incident 
with 
respect to… 
Exposure group 
(intention-to-treat 
analysis): 
Pembrolizumab and 
chemotherapy  
A time-fixed treatment, defined as 
follows: 
1) Incident use of pembrolizumab, 
platinum therapy (cisplatin or 
carboplatin), or pemetrexed 
2) Occurrence of remaining drugs 
not captured in 1) within a 14-day 
window following 1)  
[-14,0] 
code 
(Medication 
Drug, 
Medication 
Ingredient, 
Procedure) 
CPT/HCPCS 
NDC, 
RxNorm 
Primary 
Metastatic 
disease 
indicatorc 
Comparator group 
(intention-to-treat 
analysis): Chemotherapy  
1) Incident use of platinum therapy 
(cisplatin or carboplatin) or 
pemetrexed 
2) Occurrence of remaining drug not 
captured in 1) within a 14-day 
window following 1)  
[-14,0] 
code 
(Medication 
Drug, 
Medication 
Ingredient, 
Procedure) 
CPT/HCPCS 
NDC, 
RxNorm 
Primary 
Metastatic 
disease 
indicatorc 
Exposure group (per-
protocol analysis): 
Pembrolizumab and 
chemotherapy  
A static treatment regime, defined 
as follows: 
Concurrent use of pembrolizumab, 
pemetrexed, and platinum therapy 
(cisplatin or carboplatin) every 21 
days for 4 cycles, followed by 
concurrent use of pembrolizumab 
and pemetrexed every 21 days 
N/A 
code 
(Medication 
Drug, 
Medication 
Ingredient, 
Procedure) 
CPT/HCPCS 
NDC, 
RxNorm 
Secondary 
Metastatic 
disease 
indicatorc 

 
17 
 
Comparator group (per-
protocol analysis): 
Chemotherapy  
A static treatment regime, defined 
as follows: 
Concurrent use of pemetrexed and 
platinum therapy (cisplatin or 
carboplatin) every 21 days for 4 
cycles, followed by use of 
pemetrexed every 21 days. May 
cross over to pembrolizumab 
monotherapy. 
N/A 
code 
(Medication 
Drug, 
Medication 
Ingredient, 
Procedure) 
CPT/HCPCS 
NDC, 
RxNorm 
Secondary 
Metastatic 
disease 
indicatorc 
a Assessment times shown are inclusive. 
b See appendix for listing of clinical codes for each study parameter. 
c Indicators of metastatic disease include records indicating stage 4 disease, indicators of metastasis derived from the data vendor, or ICD-O diagnostic 
codes indicating metastasis. Information on metastatic disease is drawn from a linked tumor registry and tumor morphology descriptors contained within 
the health record. 
 
Context and rationale for outcome(s) of interest 
The outcome of interest is overall survival, which will be estimated as the probability of survival beyond 12 months from time zero and as a marginal 
relative hazard of mortality comparing the exposure vs. comparator groups. Month and year of mortality are available for patients in the data source; 
however, exact dates of death are not available to preserve patient privacy. Date of death will be assumed to have occurred on the first day of the month 
of death given in the data source. 
Table 6.  Operational Definitions of Outcome  
Outcome name 
Details 
Assessment 
window 
Type of 
outcome 
Variable (Data Table 
Name(s)) 
Applied to study 
populations: 
12-month survival  
Probability of surviving at 
least 12 months from time 
zero 
[1, end of 
follow-up] 
Binary 
month_year_death 
(Patient 
Demographic) 
Primary and secondary 
Hazard ratio for mortality 
Relative hazard of all-cause 
mortality 
[1, end of 
follow-up] 
Time-to-event 
month_year_death 
(Patient 
Demographic) 
Primary and secondary 

 
18 
 
Context and rationale for follow-up  
Primary analysis (intention-to-treat): The effect of treatment initiation on the outcome of interest will be estimated (i.e., “intention-to-treat effect”), where 
follow-up will begin on the day after a 14-day exposure regimen ascertainment window and proceed until the outcome event (death), loss to follow-up 
(>90-day gap in health record activity), 640 days after the index date, or end of available data occurs. “Health record activity” will be defined by lab 
results, treatments, and vitals records. See Figure 1-A for an illustration of follow-up in the primary intention-to-treat analysis. Administrative censoring at 
21 months (640 days) will be imposed to align with the duration of the KEYNOTE-189 trial at the time initial results were published.   
Secondary analysis (per-protocol): The effect of the treatment on the outcome will be estimated (i.e., “per-protocol effect”), where follow-up will begin on 
the day after initiation of a study drug in each regimen of interest and proceed until an outcome event (death), treatment cessation (deviation from 
regimens described in the variables section of this protocol), loss to follow-up (>90-day gap in health record activity), 640 days after the index date, or 
end of available data occurs. “Health record activity” will also be defined by lab results, treatments, and vitals records (similar to the primary analysis). In 
aligning with the KEYNOTE-189 trial, cross-over from the non-pembrolizumab group to pembrolizumab monotherapy will be permitted. Administrative 
censoring at 21 months (640 days) will be imposed to align with the duration of the KEYNOTE-189 trial at the time initial results were published. See Figure 1-
B for illustrations of follow-up in the secondary per-protocol analysis. 
 
 

 
19 
 
Table 7.  Operational Definitions of Follow-Up 
 
Primary analysis (intention-to-treat) 
  
  
  
  
Follow-up starta 
Day 1 
    
Follow-up endb 
Select all that 
apply 
  
Specify 
Date of outcome 
Yes 
 
Mortality 
End of observation in data 
Yes 
 
>90-day gap in any health record activity (lab 
results, treatments, and vitals records) 
OR 
End of data 
Day X following index date 
  
Yes 
 
Day 640 (End of study period) 
End of study period  
No 
 
 
End of exposure  
  
No 
  
Date of add to/switch from exposure  
  
No 
  
 
Other date  
No 
  
 
 
 
 

 
20 
 
 
Secondary analysis (per-protocol) 
 
  
  
  
  
Follow-up starta 
Day 1 
    
Follow-up endb 
Select all that 
apply 
  
Specify 
Date of outcome 
Yes 
 
Mortality 
End of observation in data 
Yes 
 
>90-day gap in any health record activity (lab 
results, treatments, and vitals records) 
Day X following index date 
Yes 
 
Day 640 
End of study period 
Yes 
 
End of data 
Date of treatment change or cessation  
Yes 
  
Patients will be censored upon deviation of their 
“assigned” static treatment regime described in 
the Exposure section 
Other date 
No 
  
 
 
a Follow-up begins at time shown relative to ‘time zero’ (see Time Zero for more information). 
b Follow-up ends at the first occurrence of any of the selected criteria that end follow-up. 
 
 

 
21 
 
Context and rationale for covariates  
Confounding variables were chosen on the basis of the investigators’ substantive knowledge and the backdoor criterion.7  
The Charlson comorbidity index has been shown to be a prognosticator of survival in patients with NSCLC, particularly those with no EGFR or ALK 
mutations.8,9 
Table 8.  Operational Definitions of Covariates  
Characteristic 
Details 
Type of 
variable 
Assessment 
window 
Time-
varyinga 
Variable (Data Table 
Name(s)) 
Code Typeb 
Applied to 
study 
populations: 
Age 
Years between year of index date 
and year of birth 
Continuous 
[0, 0] 
No 
year_of_birth (Patient 
Demographics) 
Derived 
Primary, 
Secondary 
Sex 
Patient’s sex 
Categorica
l 
[0, 0] 
No 
sex (Patient 
Demographics) 
Derived 
Primary, 
Secondary 
BMI 
Patient’s BMI 
Continuous  [0, 0] 
Yes 
code (Vitals) 
LOINC 
Primary, 
Secondary 
Marital status 
Patient’s marital status (married, 
single, unknown) 
Categorica
l 
[0, 0] 
No 
marital_status (Patient 
Demographics) 
Derived 
Primary, 
Secondary 
Race 
Patient’s race (American Indian or 
Alaska Native, Asian, Black or 
African American, Native Hawaiian 
or Other Pacific Islander, White, 
Unknown) 
Categorica
l 
[0, 0] 
No 
race (Patient 
Demographics) 
Derived 
Primary, 
Secondary 
Ethnicity 
Patient’s ethnicity (Hispanic or 
Latino, Not Hispanic or Latino, 
Unknown) 
Categorica
l 
[0, 0] 
No 
ethnicity (Patient 
Demographics) 
Derived 
Primary, 
Secondary 
Region 
Patient’s regional location in the 
United States (US) or outside the 
US. 
Categorica
l 
[0, 0] 
No 
patient_regional_location 
(Patient Demographics) 
Derived 
Primary, 
Secondary 
Diagnosis date 
Date of original primary cancer 
diagnosis (or, if unavailable, date 
Date  
[-all 
available 
data, 0] 
No 
diagnosis_date (Tumor) 
Derived 
Primary, 
Secondary 

 
22 
 
Characteristic 
Details 
Type of 
variable 
Assessment 
window 
Time-
varyinga 
Variable (Data Table 
Name(s)) 
Code Typeb 
Applied to 
study 
populations: 
of incident metastatic disease 
indicator) 
Performance 
status 
ECOG performance status (or 
equivalent value of ECOG that 
relates to Karnofsky performance 
status  
 
LOINC Code for ECOG: 89247-1 
TNX Code for ECOG: 2002 
LOINC Code for Karnofsky: 89243-0 
 
Categorize as follows:  
• 
Set to 0 if ECOG=0 or 
Karnofsky=[90, 100] 
• 
Set to 1 if ECOG=1 or 
Karnofsky=[70, 80] 
• 
Set to 2 if ECOG=2 or 
Karnofsky=[50, 60] 
• 
Set to 3 if ECOG=3 or 
Karnofsky=[30, 40] 
• 
Set to 4 if ECOG=4 or 
Karnofsky=[10, 20] 
 
 
Citation: Oken MM, Creech RH, Tormey DC, 
Horton J, Davis TE, McFadden ET, Carbone PP. 
Toxicity and response criteria of the Eastern 
Cooperative Oncology Group. Am J Clin 
Oncol. 1982 Dec;5(6):649-55. PMID: 7165009. 
Categorica
l 
ITT analysis: 
[-all 
available 
data, -15] 
most recent 
value 
PP analysis: 
[-all 
available 
data, 0] 
Yes 
code (Lab Results) 
LOINC, 
Derived 
Primary, 
Secondary 
Treatment history 
Prior systemic treatments 
 
See appendix for treatment types 
Categorica
l  
ITT analysis: 
[-diagnosis 
date, -first 
date 
indicating 
Yes 
code (Medication Drug, 
Medication Ingredient, 
Procedure) 
RxNorm, 
NDC, 
CPT/HCPCS 
Primary, 
Secondary 

 
23 
 
Characteristic 
Details 
Type of 
variable 
Assessment 
window 
Time-
varyinga 
Variable (Data Table 
Name(s)) 
Code Typeb 
Applied to 
study 
populations: 
metastatic 
disease] 
 
Neoadjuvant or 
Adjuvant Therapy 
Occurrence of at least 1 prior 
systemic treatment defined in 
‘Treatment history’ variable 
Binary 
N/A 
 
No 
 
 
 
Corticosteroid use 
Frequency of steroid use (count of 
records indicating corticosteroid 
use 30 days prior to treatment 
initiation) 
Continuous 
ITT analysis: 
[-45, -15] 
PP analysis: 
[-30, -1] 
Yes 
code (Medication Drug, 
Medication Ingredient, 
Procedure) 
RxNorm, 
NDC, 
CPT/HCPCS 
Primary, 
Secondary 
PD-L1 
expression/results 
PD-L1 test Report. Categorize as 
follows: 
• 
Set to 0 if <1%,  
• 
Set to 1 if ≥1%  
• 
Set to 2 if 1-49% 
• 
Set to 3 if ≥50% 
• 
otherwise set to “NA” 
<1%, ¸≥1%, 1-49%, ≥50%  
 
Assess up to 90 days after index date. 
Justification: PD-L1 test results may be 
delayed following treatment initiation and 
PD-L1 status is not assumed to be a 
mediator of the effect of treatment on the 
outcome.  
Categorica
l 
[-all 
available 
data, 90] 
Take latest 
value  
No 
code (Lab Result) 
LOINC 
Primary, 
Secondary 
Smoking status 
Current or former; never 
Categorica
l 
[-all 
available 
data, 0] 
most recent 
value 
No 
code (Diagnosis) 
ICD 
Primary, 
Secondary 

 
24 
 
Characteristic 
Details 
Type of 
variable 
Assessment 
window 
Time-
varyinga 
Variable (Data Table 
Name(s)) 
Code Typeb 
Applied to 
study 
populations: 
Brain metastases 
History of brain metastasis 
Binary 
ITT analysis: 
[-diagnosis 
date, -15] 
PP analysis: 
[-diagnosis 
date, 0] 
Yes 
code (Diagnosis) 
tumor_site_code (Tumor, 
Tumor Properties) 
ICD 
Primary, 
Secondary 
Thoracic 
radiotherapy 
History of thoracic radiotherapy 
for non-metastatic disease 
Binary 
ITT analysis: 
[-diagnosis 
date, -15] 
PP analysis: 
[-diagnosis 
date, 0] 
No 
code (Procedure) 
ICD, 
HCPCS/CPT  
Primary, 
Secondary 
Lung resection 
History of lung resection 
Binary 
ITT analysis: 
[-diagnosis 
date, -15] 
PP analysis: 
[-diagnosis 
date, 0] 
No 
code (Diagnosis) 
ICD, 
HCPCS/CPT 
Primary, 
Secondary 
Estimated 
glomerular 
filtration rate 
(eGFR) 
eGFR derived from data vendor 
Continuous 
ITT analysis: 
[-all 
available 
data, -15] 
PP analysis: 
[-all 
available 
data, 0] 
 
Most recent 
record 
Yes 
code (Lab Result) 
LOINC, 
Derived 
Primary, 
Secondary 

 
25 
 
Characteristic 
Details 
Type of 
variable 
Assessment 
window 
Time-
varyinga 
Variable (Data Table 
Name(s)) 
Code Typeb 
Applied to 
study 
populations: 
Venous 
thromboembolism 
History of deep vein thrombosis or 
pulmonary embolism 
Binary 
ITT analysis: 
[-all 
available 
data, -15] 
PP analysis: 
[-diagnosis 
date, 0] 
most recent 
value 
Yes 
code (Diagnosis) 
ICD, 
HCPCS/CPT 
Primary, 
Secondary 
Time since 
metastatic 
indicator 
Time between metastatic disease 
indicator and ‘time zero’ 
Continuous 
ITT Analysis: 
[-first date 
indicating 
metastatic 
disease, 0] 
PP analysis: 
-first date 
indicating 
metastatic 
disease, 0] 
No 
metastatic (Tumor) 
stage_code (Tumor) 
morphology_code 
(Tumor, Tumor Properties) 
code (Diagnosis) 
ICD, derived 
Primary, 
Secondary 
Vitals frequency 
Frequency of vitals records in year 
prior (proxy for in-network 
engagement) 
Continuous 
ITT analysis: 
[-380, -15] 
PP analysis: 
[-365, 0] 
Yes 
code (Vitals Signs) 
date (Vitals Signs) 
LOINC 
Primary, 
Secondary 
Charlson 
comorbidity index 
Charlson comorbidity index 
 
Categorica
l 
ITT analysis: 
[-380, -15] 
PP analysis: 
[-365, 0] 
 
most recent 
value 
Yes 
code (Diagnosis) 
ICD 
Primary, 
Secondary 

 
26 
 
Characteristic 
Details 
Type of 
variable 
Assessment 
window 
Time-
varyinga 
Variable (Data Table 
Name(s)) 
Code Typeb 
Applied to 
study 
populations: 
Histology 
(adenocarcinoma) 
Tumor histology 
(adenocarcinoma) 
Set equal to 1 if relevant codes in 
appendix occur anytime prior to 
or on 90 days post-index date. 
Otherwise set to 0. 
 
 
Binary 
[-all 
available 
data, 90] 
No 
morphology_code 
(Tumor)  
ICD-O 
Primary, 
Secondary 
a Time-varying covariates that will be considered for inclusion in statistical models for weight estimation in the secondary (“per-protocol”) analysis. 
Variables will be assessed in the “assessment window” stated in the table and updated at every discrete time interval of follow-up. 
b See appendix for listing of clinical codes for each study parameter 
 
5.4.  Data analysis 
Context and rationale for analysis plan 
Table 9.  Primary, secondary, and subgroup analysis specification 
A. Primary analysis (intention-to-treat) 
Hypothesis: 
The relative hazard of mortality is not equal among patients who initiated pembrolizumab and chemotherapy vs. 
chemotherapy alone. 
Exposure contrast: 
Patients initiating pembrolizumab, pemetrexed, and platinum therapy (cisplatin or carboplatin) vs. patients 
initiating pemetrexed and platinum therapy (cisplatin or carboplatin) within a 14-day exposure-assessment 
window following the first occurrence of metastatic disease 
Outcome: 
Marginal hazard ratio for mortality, standardized to the empirical distribution of baseline confounders of the entire 
study population. 
Analytic software:  
R Statistical Software (v4.1.2; R Core Team 2021) 
Packages: tidyverse (v2.0.0), mice (v3.15.0), survival (v3.1-8) 
Model(s):  
ℎ(𝑡, 𝐿0) = ℎ0(𝑡) ∗𝑒𝛽1𝐴0+𝛽2𝑇𝐿0 

 
27 
 
where ℎ(𝑡, 𝐿0) is the hazard of mortality at discrete time interval ‘t,’ conditional on the vector of potential 
confounders, “𝐿0,” assessed at baseline; ℎ0(𝑡) is the baseline hazard at discrete time interval ‘t’; and 𝐴0 is an 
indicator for treatment initiation, coded as “1” and “0” for the exposure and comparator group, respectively.  
The proportional hazards assumption will be checked through visual inspection of plots containing scaled 
Schoenfeld residuals vs. time for each covariate. The Breslow method of handling ties will be used. 
Confounding adjustment 
method  
 
 
Stabilized, time-fixed, inverse probability of treatment weights will be used to adjust for confounding. Individual-
level weights will be estimated by the following formula: 
𝑆𝑊𝐴=
𝑓(𝐴0)
𝑓(𝐴0|𝐿0) 
where 𝐴0 is the treatment that the patient received and 𝐿0 is a vector of baseline confounders. 
 
The quantity in the denominator will be estimated using a logistic regression model with 𝐴0 as the dependent 
variable and the 𝐿0 vector as the independent variables and the quantity in the numerator will be estimated with 
a logistic regression model containing 𝐴0 as the dependent and sole variable in the model. The model will be fit to 
a person level dataset (i.e., one row per patient, with each row representing a patient’s complete experience 
during baseline and follow-up). 
 
All potential confounder variables will be considered for inclusion in the weight estimation (Table 8). However, as it 
is not possible to predict the quantity of missing values and sparseness of the data at the time of writing this 
protocol, the precise functional form of the final regression model will be determined at the time of analysis. The 
most flexible functional form will be chosen to reduce the possibility of bias due to modelling assumptions. 
 
To verify that weights were estimated correctly, the mean weight will be checked to be approximately equal to 1. 
The empirical distribution of weights will be used to determine whether there are any extreme values, suggesting 
positivity violations. In the presence of suspected random positivity violations (e.g., due to sparse data), modelling 
assumptions will be altered to smooth over “zero” or “near-zero” quantities within levels of the exposure and 
covariates. If this approach is unsuccessful, weight trimming may be considered whereby patients with weights in 
the top and bottom 1st percentile will be removed from the analysis. Structural positivity violations should not be 
possible, given our eligibility criteria and treatment strategies. 
Missing data methods  
  

 
28 
 
      
The data source is comprised of data drawn from over 50 healthcare networks, some of which may contribute 
certain variables to varying degrees. This is one plausible reason for missing values, and while identifying 
information on contributing healthcare networks is not available in the database, it can be proxied by patient 
regional location (and all other measured variables in analysis). This theory is supported by dependence of 
missing indicators for various variables (bmi, marital status, etc.) on region. Therefore, the data are believed to be 
Missing at Random (MAR). 
 
To address missing values that are MAR, multiple imputation with chained equations (fully conditional 
specification) can be employed. This approach is appropriate to address data that are MAR and permits the 
flexibility needed to impute different variable types (continuous, categorical, etc.). A series of conditional models 
will be specified to estimate the parameters of a joint distribution from which the imputed values will be drawn. 
Predictive mean matching, ordered logistic regression, and multinomial logistic models will be considered to 
impute continuous, ordinal, and nominal variables that contain missing values, respectively. The final imputation 
models and variables to be imputed will be determined by the extent of missingness and structural relationship of 
the study variables of interest. Inference regarding the final effect estimates will account for the imputation 
procedure. 
 
Please refer to the appendix for more information on missing data. 
Subgroup Analyses 
 
 
Subgroup analyses will be conducted similarly to the KEYNOTE-189 trial1 to facilitate comparison: 
1. 
Age (<65, ≥65) 
2. Sex (male, female) 
3. ECOG performance Status (0, 1, 2+) 
4. Smoking status (current or former, never) 
5. Brain metastases at baseline (yes, no) 
6. PD-L1 tumor proportion score (<1%, ≥1%, 1-49%, ≥50%) 
7. Platinum-based drug (carboplatin, cisplatin) 
 
Hypothesis: 
The 12-month survival probability is not equal among patients who initiated pembrolizumab and chemotherapy 
vs. chemotherapy alone. 

 
29 
 
Exposure contrast: 
Patients initiating pembrolizumab, pemetrexed, and platinum therapy (cisplatin or carboplatin) vs. patients 
initiating pemetrexed and platinum therapy (cisplatin or carboplatin) within a 14-day exposure-assessment 
window following the first occurrence of metastatic disease. 
Outcome: 
Marginal 12-month survival probability, standardized to the empirical distribution of baseline confounders of the 
entire study population. 
Analytic software:  
R Statistical Software (v4.1.2; R Core Team 2021) 
Packages: tidyverse (v2.0.0), mice (v3.15.0), survival (v3.1-8), survminer (v0.4.9), boot (v1.3-28) 
Model(s):  
A Kaplan-Meier estimator, weighted by time-fixed inverse probability of treatment weights will be used to 
estimate the 12-month survival probability.11 The weighted survival probability 𝑆𝑎(𝑡) for treatment group ‘A=a’ at 
time ‘t’ will be as follows: 
𝑆𝑎(𝑡) = ∏1 −𝑑𝑡𝑎
𝑟𝑡𝑎
𝑡
 
where 𝑑𝑡𝑎= 𝑤𝑖𝑡∙𝑌𝑖𝑡∙𝐼(𝐴𝑖𝑡= 𝑎) denotes the weighted number of mortality events and 𝑟𝑡𝑎= ∑
𝑤𝑖𝑡∙𝐼(𝐴𝑖𝑡= 𝑎)
𝑁
𝑖=1
 denotes 
the weighted risk set. A non-parametric bootstrap or cluster-robust standard errors will be used to derive 95% 
confidence intervals around 12-month survival probability estimates. 
Confounding adjustment 
method  
 
Inverse probability of treatment weights will be estimated to control for potential confounding using the same 
approach outlined above (see intention-to-treat mortality hazard ratio). 
Missing data methods  
  
      
The process of accounting for missing values in this analysis will be the same as outlined above (see intention-
to-treat mortality hazard ratio). 
Subgroup Analyses 
List all subgroups 
 
Provided PD-L1 tumor proportion score is available in the data source, overall survival will be estimated within 
subgroups of  patients with a PD-L1 tumor proportion score of <1%, 1-49%, and ≥50% to facilitate comparison with 
the KEYNOTE-189 trial.1 
 
B. Secondary analysis (per-protocol) 
Hypothesis: 
The relative hazard of mortality is not equal among patients who adhered to the static treatment regimen of 
pembrolizumab and chemotherapy vs. chemotherapy alone, as illustrated and specified in the study design and 
variables section of this protocol. 

 
30 
 
Exposure contrast: 
Concurrent use of pembrolizumab, pemetrexed, and platinum therapy (cisplatin or carboplatin) every 21 days for 
4 cycles, followed by concurrent use of pembrolizumab and pemetrexed every 21 days 
Vs. 
Concurrent use of pemetrexed and platinum therapy (cisplatin or carboplatin) every 21 days for 4 cycles, followed 
by use of pemetrexed every 21 days. May cross over to pembrolizumab monotherapy. 
Outcome: 
Marginal hazard ratio for mortality, standardized to the empirical distribution of baseline and time-varying 
confounders of the entire study population. 
Analytic software:  
R Statistical Software (v4.1.2; R Core Team 2021) 
Packages: tidyverse (v2.0.0), mice (v3.15.0), survey (v4.1-1) 
Model(s): 
  
A weighted, pooled-over-time marginal structural logistic regression model will be used to estimate the per-
protocol mortality hazard ratio as follows: 
𝑙𝑜𝑔𝑖𝑡[Pr(𝐷𝑡= 1|𝐴, 𝐿0, 𝐷̅𝑡−1 = 0, 𝐶𝑡−1 = 0)] = 𝛼0,𝑡+ 𝛽1𝐴+ 𝛽2
𝑇𝐿0 
where 𝐷𝑡 denotes a death event at time ‘t’, ‘A’ is a time-updated binary indicator of adherence to the static 
treatment regime specified in the study design and variables section of this protocol (taking on value of 0 for the 
comparator group and 1 for exposed group at discrete time points ‘t’), 𝐿0 denotes a time-fixed vector of 
confounders assessed at baseline, 𝐶𝑡−1 is an indicator of censoring at time ‘t-1’ (e.g., due to non-adherence to 
regime of interest, loss to follow-up, or administrative end of data), and 𝛼0,𝑡 is a time-varying intercept (estimated 
as a polynomial function of ‘t’).  
Provided the probability of morality is less than 15% in each discrete time interval, the treatment coefficient of the 
pooled logistic model will approximate a relative hazard. 
As it is not possible to predict the quantity of missing values and sparseness of the data at the time of writing this 
protocol, the precise functional form of the final regression model will be determined at the time of analysis. The 
most flexible functional form will be chosen to reduce the possibility of residual bias arising from modelling 
assumptions. 
Confounding adjustment 
method  
 
To account for potential confounding bias, stabilized time-varying inverse probability of treatment weights will be 
applied to the study sample to generate a pseudo-population, in which the treatment at time ‘t’ is independent of 
confounder history. The weights will take on the following form: 
 

 
31 
 
𝑆𝑊𝐴= ∏
𝑓(𝐴𝑡|𝐴̅𝑡−1)
𝑓(𝐴𝑡|𝐴̅𝑡−1, 𝐿0, 𝐿̅𝑣)
𝑇
𝑡=0
 
 
where 𝐴𝑡 denotes time-varying treatment status that the patient actually received at discrete time ‘t’ (taking on 
values 0 for adherence to the comparator regimen and 1 for adherence to the exposed regimen), 𝐿0 denotes a 
vector of time-fixed baseline confounders, and 𝐿𝑣 denotes a vector of time-varying confounders. The overbar is 
used to represent treatment history.  
 
The conditional probability mass functions that comprise the numerator and denominator of 𝑆𝑊𝐴 will be 
estimated using a pooled logistic regression model that includes treatment status at time ‘t’, treatment history, 
covariate history (baseline and time-varying), and a time-varying intercept (using functional of time using 
splines). The model will be fit to a person-time level dataset (i.e., multiple rows per patient, each row representing 
a patient’s experience during a discrete time interval). Each person-time contribution to the weight will be 
calculated by taking the cumulative product of predicted probabilities from the model over each person-time 
interval from t=0 through event or censoring, “T,” where T = min(event, censoring). 
 
As illustrated in the study design and variables section of this protocol, patients will be censored when they do not 
adhere to the treatment regimens of interest. To account for potential informative censoring induced by this 
artificial censoring rule, inverse probability of censoring 𝑆𝑊𝐶 weights will be estimated and applied jointly with 
𝑆𝑊𝐴 to the study sample. The censoring weights will take on the following form: 
 
𝑆𝑊𝐶= ∏
𝑓(𝐶𝑡= 0|𝐴̅𝑡−1, 𝐶𝑡−1 = 0)
𝑓(𝐶𝑡= 0|𝐴̅𝑡−1,𝐶𝑡−1 = 0, 𝐿̅𝑣)
𝑇
𝑡=0
 
 
Intuitively, censoring weights weight patients who remain in the analysis, and have the same treatment and 
covariate history as those who were censored, more heavily to “compensate” for those that were censored. 
Therefore, the mean weight over follow-up time should be 1. This will be verified graphically to check for model 
misspecification. 
 
Using a similar approach to treatment weight estimation, the conditional probability mass functions that 
comprise the numerator and denominator of 𝑆𝑊𝐶 will be estimated using a pooled logistic regression model that 
includes censoring status at time ‘t’, treatment history, covariate history (time-varying), and a time-varying 

 
32 
 
intercept (using functional of time using splines) for the denominator, and the same variables (except for 
covariate history) for the numerator. 
 
The final study sample will be weighted by the product of the two weights at each person-time interval as follows: 
 
𝑆𝑊𝐴,𝐶= 𝑆𝑊𝐴∙𝑆𝑊𝐶 
 
Missing data methods  
  
      
The data source is comprised of data drawn from over 50 healthcare networks, some of which may contribute 
certain variables to varying degrees. This is one plausible reason for missing values, and while identifying 
information on contributing healthcare networks is not available in the database, it can be proxied by patient 
regional location (and all other measured variables in analysis). This theory is supported by dependence of 
missing indicators for various variables (bmi, marital status, etc.) on region. Therefore, the data are believed to be 
Missing at Random (MAR). 
 
To address missing values that are MAR, multiple imputation with chained equations (fully conditional 
specification) can be employed. This approach is appropriate to address data that are MAR and permits the 
flexibility needed to impute different variable types (continuous, categorical, etc.). A series of conditional models 
will be specified to estimate the parameters of a joint distribution from which the imputed values will be drawn. 
Predictive mean matching, ordered logistic regression, and multinomial logistic models will be considered to 
impute continuous, ordinal, and nominal variables that contain missing values, respectively. The final imputation 
models and variables to be imputed will be determined by the extent of missingness and structural relationship of 
the study variables of interest. Inference regarding the final effect estimates will account for the imputation 
procedure. 
 
Please refer to the appendix for more information on missing data. 
Subgroup Analyses 
List all subgroups 
 
Subgroup analyses will be conducted similarly to the KEYNOTE-189 trial1 to facilitate comparison: 
1. 
Age (<65, ≥65) 
2. Sex (male, female) 
3. ECOG performance Status (0, 1, 2+) 
4. Smoking status (current or former, never) 
5. Brain metastases at baseline (yes, no) 

 
33 
 
6. PD-L1 tumor proportion score (<1%, ≥1%, 1-49%, ≥50%) 
7. Platinum-based drug (carboplatin, cisplatin) 
 
Hypothesis: 
The 12-month survival probability is not equal among patients who adhered to the static treatment regimen of 
pembrolizumab and chemotherapy vs. chemotherapy alone, as illustrated and specified in the study design and 
variables section of this protocol. 
Exposure contrast: 
Concurrent use of pembrolizumab, pemetrexed, and platinum therapy (cisplatin or carboplatin) every 21 days for 
4 cycles, followed by concurrent use of pembrolizumab and pemetrexed every 21 days 
Vs. 
Concurrent use of pemetrexed and platinum therapy (cisplatin or carboplatin) every 21 days for 4 cycles, followed 
by use of pemetrexed every 21 days. May cross over to pembrolizumab monotherapy. 
Outcome: 
Marginal 12-month survival probability, standardized to the empirical distribution of baseline and time-varying 
confounders of the entire study population. 
Analytic software:  
R Statistical Software (v4.1.2; R Core Team 2021) 
Packages: tidyverse (v2.0.0), mice (v3.15.0), survival (v3.1-8), survminer (v0.4.9), boot (v1.3-28) 
Model(s):  
A Kaplan-Meier estimator, weighted by time-varying inverse probability of treatment and censoring weights will 
be used to estimate the 12-month survival probability.11 The weighted survival probability 𝑆𝑎(𝑡) for treatment group 
‘A=a’ at time ‘t’ will be as follows: 
 
𝑆𝑎(𝑡) = ∏1 −𝑑𝑡𝑎
𝑟𝑡𝑎
𝑡
 
 
where 𝑑𝑡𝑎= 𝑤𝑖𝑡∙𝑌𝑖𝑡∙𝐼(𝐴𝑖𝑡= 𝑎) denotes the time-specific weighted number of 𝑌𝑖𝑡 mortality events and 𝑟𝑡𝑎=
∑
𝑤𝑖𝑡∙𝐼(𝐴𝑖𝑡= 𝑎)
𝑁
𝑖=1
 denotes the weighted risk set. A non-parametric bootstrap or cluster-robust standard errors will 
be used to derived 95% confidence intervals around 12-month survival probability estimates. 
Confounding adjustment 
method  
 
Time-varying inverse probability of treatment and censoring weights will be estimated to control for potential 
confounding using the same approach outlined above (see “ 𝑆𝑊𝐴,𝐶 “ estimated for estimation of the per-protocol 
mortality hazard ratio).  
 
Missing data methods  
  

 
34 
 
      
The process of accounting for missing values in this analysis will be the same as that outlined above in the per-
protocol mortality hazard ratio section on missing data methods. 
Subgroup Analyses 
List all subgroups 
 
Provided PD-L1 tumor proportion score is available in the data source, overall survival will be estimated within 
subgroups of  patients with a PD-L1 tumor proportion score of <1%, 1-49%, and ≥50% to facilitate comparison with 
the KEYNOTE-189 trial.1 
 
Table 10.  Sensitivity and exploratory analyses – rationale, strengths and limitations 
 
What is being varied? How? 
Why?  
(What do you expect to learn?) 
Strengths of the sensitivity 
analysis compared to the 
primary 
Limitations of the sensitivity 
analysis compared to the 
primary 
Sensitivity Analyses 
Contemporaneous 
cohort 
The study population will be 
restricted to subjects with a 
time of cohort entry (“index 
date”) of June 6, 2017 or 
later.  
Pembrolizumab first received 
accelerated approval for use as 
a first-line agent in NSCLC on 
this date. Furthermore, the use of 
historical controls may introduce 
bias if time of cohort entry is a 
confounder. This analysis tests 
the implicit assumption of the 
main analysis that time 
calendar time is not a 
confounder. 
A contemporaneous cohort 
will allow for a more closely 
matched cohort across 
exposure groups with 
respect to calendar time, 
potentially minimizing the 
risk of confounding by 
calendar time. 
Because this analysis excludes 
subjects, the sample size may 
be substantially lower by 
design. Consequently, 
precision of outcome 
estimates may be reduced.  
Treatment 
administrations 
only 
Treatment will only be 
defined using procedure 
codes indicating 
medication administration 
(i.e., HCPCS/CPT codes) 
Medication orders, 
administrations, and 
dispensations are not clearly 
distinguished in the data source. 
For this reason, the primary 
analysis implicitly assumes all 
medication records were 
tantamount to administrations. 
Medication administrations 
are of particular interest, as 
they directly indicate a 
patient’s exposure to the 
drug (as opposed to 
medication orders or 
dispensations) 
HCPCS/CPT codes may not 
always appear in patient 
records when a drug is 
administered, leading to 
potential measurement error. 

 
35 
 
Robustness of our primary 
results to this analysis can test 
the reasonableness of this 
implicit assumption. 
Require treatment 
initiation within 6 
months of 
diagnosis 
metastatic disease 
Patients will be required to 
initiate treatment within 6 
months of the incident 
metastatic indicator used to 
establish ‘time zero’ 
Patients with long time lapses 
between metastasis and 
treatment initiation may be 
receiving out-of-network care 
and therefore have incomplete 
capture of key variables for the 
analysis. These patients may 
also indicate measurement error 
in the assessment of initial 
metastasis. Analysis of patients 
who initiate treatment soon after 
metastasis may better reflect 
the target population of interest 
and patients who have more 
complete healthcare 
information in the database. 
This sensitivity analysis 
indirectly tests the 
assumption that the initial 
occurrence of metastatic 
disease and the target 
population of interest are 
being ascertained 
accurately. 
This sensitivity analysis may 
reduce sample size and 
precision of effect estimates. It 
is also indirectly testing 
assumptions that can’t be 
verified empirically.  
Exploratory Analyses 
Differential 
treatment intensity 
The rate of engagement 
with the health-system 
(e.g., frequency of office 
visits, radiology 
reports/scans, or 
encounters) will be 
assessed within each 
treatment group over the 
follow-up period.  
Differential levels of care 
between the study groups may 
influence outcomes and serve 
as a proxy for residual or 
unmeasured confounding. 
This analysis may provide a 
sense of confidence (or lack 
thereof) in the comparability 
of treatment groups.  
Differential treatment intensity 
is not guaranteed to 
adequately capture all (or 
any) elements of residual or 
unmeasured confounding. 
Censoring event 
distributions 
The distribution of censoring 
event times over the study 
A differential pattern of 
censoring over the follow-up 
This analysis may provide a 
sense of confidence (or lack 
A differential pattern of 
censoring events does not 

 
36 
 
period will be assessed 
within each treatment 
group over the follow-up 
period 
period between treatment 
groups could indicate the 
presence of informative 
censoring.  
thereof) in the comparability 
of treatment groups. 
directly indicate the reasons 
for the observed pattern and 
further explorations may be 
needed. 
5.5.  Data sources 
Summary of data source: The data source used for this study is derived from electronic health records of 52 healthcare organizations from the US and the 
Asia-Pacific region. In particular, the data are drawn from a combination of structured fields in the health record (demographics, date-indexed 
encounters, diagnoses, procedures, and medications) and natural language processing of free-text provider notes. The data are enriched using 
tumor/cancer registries for oncology information and obituaries and Social Security records for mortality data. The healthcare organizations from which 
the data are drawn are varied and include acute care hospitals, networks of outpatient clinics, academic medical centers, pediatric hospitals, and 
cardiac care and surgical centers. Although the data are sourced from many institutions, those institutions are not identifiable in the data for privacy 
purposes. 
Context and rationale for data sources 
Reason for selection: The process for selecting a data source is described in a previously published manuscript.12 Briefly, data sources meeting operational 
definitions for eligibility criteria, exposure, outcome, and confounders determined by the investigators were considered for use. Among these initially 
identified data sources, additional factors were considered to select a final fit-for-purpose dataset, including data quality, quantity (sample size), and 
provenance. Because data is sourced from entire health care organizations, which house several in-network hospitals, clinics, and other institutions, 
patients’ experiences are captured more completely. 
Strengths of data source(s): Major strengths of the data source used in this study include its linkage with a tumor/cancer registry, availability of all 
(including non-oncology) diagnosis codes contained in the health records, and presence of medication history through a variety of sources (medication 
reconciliation, drug ordering system, administrations, dispensations, etc.). Information is captured from natural language processing of unstructured 
clinical notes, as well as structured data fields within the health record. Importantly, results of tumor biomarkers and genomic testing are available, 
facilitating appropriate selection of our cohort of interest (i.e., patients without EGFR or ALK mutations in tumor cells). Collectively, these qualities enable 
ascertainment of patients’ longitudinal clinical histories at a relatively high level of detail as well as identification of confounders that are not oncology-
related (e.g., comorbidities). 
Limitations of data source(s): Limitations include the inability to distinguish between medication record types (i.e., orders, administrations, and 
dispensations), unavailable information on out-of-network encounters (i.e., EHR discontinuity13), and limited drug dosing information. Natural language 
processing may also result in an unknown degree of measurement error, as the models used to derive variables have not been validated. Many of these 

 
37 
 
factors are limitations inherent to many EHR-based data sources. Mortality has not been validated in the database and is subject to measurement error 
as well. 
Table 11.  Data source provenance and completeness 
Provenance 
Information type 
Availability 
Notes 
Cancer registry data 
Cancer type/diagnosis 
X 
Cancer type is derived from standardized (ICD-O) codes drawn from the cancer 
registry 
Tumor histology 
X 
Tumor histology is derived from standardized (ICD-O) codes drawn from the 
cancer registry 
Disease state (e.g., TNM, 
AJCC staging) 
X 
Staging information is derived from standardized (ICD-O) codes drawn from the 
cancer registry 
Time of onset/original 
diagnosis 
X 
 
EHR (structured 
fields)  
Cancer type/diagnosis 
X 
Cancer type is derived from standardized (ICD) codes drawn from the structured 
fields of the health record. 
Tumor histology 
 
 
Disease state (e.g., TNM, 
AJCC staging) 
 
 
Time of onset/original 
diagnosis 
 
 
Comorbidities 
X 
Provided in the form of standardized codes (e.g., ICD) 
Procedures 
X 
Provided in the form of standardized codes (e.g., HCPCS/CPT, ICD) 
Lab results 
X 
Provided in the form of standardized codes (e.g., LOINC) 
Medication orders 
X 
Medication orders, administrations, and dispensations are indistinguishable 
Medication administration 
X 
Medication orders, administrations, and dispensations are indistinguishable 

 
38 
 
Medication dispensation 
X 
Medication orders, administrations, and dispensations are indistinguishable 
Demographics 
X 
 
Vitals 
X 
Provided in the form of standardized codes (e.g., LOINC) 
EHR (unstructured 
data (e.g., notes)) 
Cancer type/diagnosis 
 
 
Tumor histology 
 
 
Disease state (e.g., TNM, 
AJCC staging) 
 
 
Time of onset/original 
diagnosis 
 
 
Comorbidities 
X 
Some comorbidity data derived from natural language processing of clinical notes.  
Procedures 
X 
Some procedure data is derived from natural language processing of clinical 
notes.  
Lab results 
X 
Some laboratory data is derived from natural language processing (e.g., eGFR 
estimated from serum creatinine and other components of the Cockcroft-Gault 
equation). 
Medication orders 
X 
Some medication use data is derived from natural language processing of clinical 
notes.  
Medication administration 
X 
Some medication use data is derived from natural language processing of clinical 
notes.  
Medication dispensation 
X 
Some medication use data is derived from natural language processing of clinical 
notes.  
Demographics 
 
 
Vitals 
X 
Some vitals data is derived from natural language processing of clinical notes.  
External laboratory 
data 
Molecular diagnostics 
X 
Provided in the form of standardized codes (e.g., HGVS) 

 
39 
 
Insurance claims 
Outpatient diagnoses 
 
 
Outpatient procedures 
 
 
Outpatient medications 
 
 
Inpatient diagnoses 
 
 
Inpatient procedures 
 
 
Inpatient medications 
 
 
 
Table 12.  Metadata about data sources and software 
 
 
Data Source(s): 
[Redacted] 
Study Period: 
3/6/1946 – 1/17/2023 
Eligible Cohort Entry Period: 
3/6/1946 
Data Version (or date of last update): 
1/18/2023 
Data sampling/extraction criteria: 
* Must Have: C34 Malignant neoplasm of bronchus and lung 
 
 * Group 1A Tumor Registry: The terms in this group occurred at any time 
 
 * Must Have: C34 Malignant neoplasm of bronchus and lung [ACIDOPHIL CARCINOMA OR ACINAR CELL 
CARCINOMA OR ADENOCA IN FAMIL POLYP COLI OR ADENOCA. IN ADENOMA. POLYP OR ADENOCA. WITH METAPLASIA 
OR ADENOCARCINOFIBROMA OR ADENOCARCINOMA, NOS OR ADENOID CYSTIC & CRIBRIFORM CA. OR 
ADENOSQUAMOUS CARCINOMA OR ADRENAL CORTICAL CARCINOMA OR ALVEOLAR RHABDOMYOSARCOMA OR 
AMELANOTIC MELANOMA OR AMELOBLASTIC FIBROSARCOMA OR AMELOBLASTIC ODONTOSARCOMA OR 
AMELOBLASTOMA, MALIGNANT OR ANGIOLIPOMA OR ASTROBLASTOMA OR ASTROCYTOMA, NOS OR BASAL CELL 
CARCINOMA, NOS OR BASOPHIL CARCINOMA OR BENIGN LIPOMA OR BLOOD VESSEL TUMORS OR BLUE NEVUS, 
MALIGNANT OR BRENNER TUMOR, MALIGNANT OR BRONCHIOLO-ALVEOLAR ADENOCA. OR CARCINOID TUMOR, 
MALIGNANT OR CARCINOMA, NOS OR CARCINOMA, UNDIFF., NOS OR CARCINOSARCOMA, NOS OR CELLULAR BLUE 
NEVUS OR CEREBELLAR SARCOMA, NOS OR CERUMINOUS ADENOCARCINOMA OR CHOLANGIOCARCINOMA OR 

 
40 
 
CHONDROBLASTOMA, MALIGNANT OR CHONDROSARCOMA, NOS OR CHORDOMA OR CHORIOCARCINOMA OR 
CHROMOPHOBE CARCINOMA OR CHRONIC MYELOPROLIFERATIVE DIS. OR CLEAR CELL ADENOCARCINOMA, NOS OR 
CLEAR CELL SARC/NEPHROBLASTOMA OR COMB HEPATOCEL CA. & CHOLANG OR CRANIOPHARYNGIOMA OR 
CYSTADENOCARCINOMA, NOS OR DUCT CARCINOMA OR EMBRYONAL CARCINOMA, NOS OR EMBRYONAL 
RHABDOMYOSARCOMA OR ENDOCRINOMAS OR ENDOMETRIOID ADENOCARCINOMA OR EPENDYMOMA, NOS OR 
EPITHELIAL NEOPLASM OR EPITHELIOID CELL MELANOMA OR ERYTHROID LEUKEMIA OR EWING SARCOMA OR EXTRA-
ADRENAL PARAGANG., MAL OR FIBRILLARY ASTROCYTOMA OR FIBROMA OR FIBROMATOUS NEOPLASMS OR FIBROUS 
HISTIOCYTOMA, MAL. OR FOLLIC. & MARGINAL LYMPH, NOS OR FOLLICULAR ADENOCARCINOMA, NOS OR 
GANGLIONEUROBLASTOMA OR GERM CELL TUMORS OR GIANT & SPINDLE CELL CARCINOMA OR GLIOBLASTOMA, NOS 
OR GLIOMA OR GLOMANGIOSARCOMA OR GONADAL NEOPLASMS OR GRANULAR CELL CARCINOMA OR GRANULAR 
CELL TUMOR OR GRANULOSA CELL TUMOR, MAL. OR GT. CELL TUMOR OF BONE, MAL. OR HEMANGIOBLASTOMA OR 
HEMANGIOENDOTHELIOMA OR HEMANGIOPERICYTOMA OR HEPATOCELLULAR CARCINOMA, NOS OR HILUS CELL 
TUMOR OR HODGKIN LYMPHOMA OR HODGKIN LYMPHOMA, NOD. SCLER. OR IMMUNOPROLIFERATIVE DISEASES OR 
INFLAMMATORY CARCINOMA OR INTRADERMAL NEVUS OR JUVENILE FIBROADENOMA OR JUXTACORTICAL 
OSTEOSARCOMA OR KAPOSI SARCOMA OR LEUKEMIA, NOS OR LEYDIG CELL TUMOR, MALIGNANT OR LIPID CELL TUMOR, 
MAL. OR LIPOSARCOMA NEOPLASMS OR LOBULAR AND OTHER DUCTAL CA. OR LUTEOMA OR LYMPHANGIOSARCOMA 
OR LYMPHOEPITHELIAL CARCINOMA OR LYMPHOID LEUKEMIA, NOS OR MAL. MEL. IN JUNCT. NEVUS OR MAL. MELAN. IN 
GIANT PIGMT. NEVUS OR MALIGNANT LYMPHOMA, NOS OR MAST CELL TUMORS OR MEDULLARY CARCINOMA, NOS OR 
MEDULLOBLASTOMA, NOS OR MEGAKARYOBLASTIC LEUKEMIA OR MENINGIOMA OR MESENCHYMOMA, MALIGNANT OR 
MESONEPHROMA, MALIGNANT OR MESOTHELIOMA, MALIGNANT OR MIXED TUMOR, MALIGNANT, NOS OR ML, LARGE B-
CELL, DIFFUSE OR ML, SMALL B-CELL LYMPHOCYTIC OR MONOCYTIC/OTHER LEUKEMIA, NOS OR MUCINOUS 
ADENOCARCINOMA OR MUCINOUS CYSTADENOCARC., NOS OR MUCOEPIDERMOID CARCINOMA OR MULLERIAN 
MIXED TUMOR OR MYELODYSPLASTIC SYNDROMES OR MYELOID LEUKEMIA, NOS OR MYELOID SARCOMA OR 
MYELOLIPOMA OR MYELOPLASTIC/MYELOPROLIFERATIVE NEOPLASMS OR MYOMATOUS NEOPLASMS OR 
MYXOSARCOMA OR NEOPLASM OR NEOPLASMS OF HISTIOCYTES AND ACCESSORY LYMPHOID CELLS OR 
NEURILEMMOMA OR NEUROBLASTOMA, NOS OR NEUROFIBROSARCOMA OR NEVI & MELANOMAS OR NON-SMALL CELL 
CARCINOMA OR NONENCAPSUL. SCLEROSING CA. OR ODONTOGENIC CARCINOSARCOMA OR ODONTOGENIC 
NEOPLASM OR ODONTOGENIC TUMOR OR ODONTOGENIC TUMOR, MAL. OR ODONTOMA OR OLFACTORY 
NEUROGENIC TUMOR OR OLIGODENDROBLASTOMA OR OLIGODENDROGLIOMA, NOS OR OSSEOUS & 
CHONDROMATOUS NEOPLASMS OR OSTEOBLASTOMA OR OSTEOCHONDROMA OR OSTEOSARCOMA, NOS OR OTHER 
LEUKEMIAS OR OTHER MYELOID LEUKEMIAS OR OTHER SPEC. NON-HODGKIN LYMPHOMA OR OVARIAN STROMAL 
TUMOR, MAL. OR OXYPHILIC ADENOCARCINOMA OR PAGET DISEASE, MAMMARY OR PANCREATOBLASTOMA OR 
PAPILLARY & FOLLICULAR ADENOCA. OR PAPILLARY ADENOCARCINOMA, NOS OR PAPILLARY CARCINOMA, NOS OR 

 
41 
 
PAPILLARY CYSTADENOCA., NOS OR PAPILLARY SEROUS CYSTADENOCA OR PAPILLARY TRANS. CELL CARCINOMA OR 
PAPILLOMATOSIS OR PARAGANGLIOMA OR PERINEURIOMA OR PERIPHERAL NEUROECTODERMAL TUMOR OR 
PHEOCHROMOCYTOMA OR PHYLLODES TUMOR,MAL. OR PILOMATRIX CARCINOMA OR PLASMA CELL TUMORS OR 
PLEXIFORM NEUROFIBROMA OR POLYCYTHEMIA VERA OR PRECURS. CELL LYMPHOBLASTIC LYMPH. OR PRECURSOR 
LYMPHOID NEOPLASMS OR PROLYMPH/PRECURS LEUKEMIA OR PROTOPLASMIC ASTROCYTOMA OR REFRACTORY 
ANEMIA OR RENINOMA OR RETINOBLASTOMA, NOS OR RHABDOMYOSARCOMA, NOS OR SARCOMA, NOS OR 
SEBACEOUS/ECCRINE ADENOCA. OR SERTOLI CELL CARCINOMA OR SIGNET RING CELL CARCINOMA OR SKIN 
APPENDAGE CARCINOMA OR SMALL CELL CARCINOMA OR SOLID CARCINOMA, NOS OR SQUAMOUS CELL 
CARCINOMA, NOS OR STROMAL SARCOMA OR STRUMA OVARII, MALIGNANT OR SWEAT GLAND ADENOCARCINOMA 
OR SYNOVIAL SARCOMA, NOS OR T-CELL LYMPHOMAS OR TERATOMA OR THECOMA, MALIGNANT OR THERAPY 
RELATED AC. MYEL. LEUK. OR THYMOMA, MALIGNANT OR TRABECULAR ADENOCARCINOMA OR TRANSITIONAL CELL 
CARCINOMA, NOS OR TRICHILEMMOCARCINOMA] 
 
 * AND 
 
 * Group 2A Non-Squamous: The terms in this group occurred at any time 
 
 * Must Have: C34 Malignant neoplasm of bronchus and lung [ADENOCARCINOMA, NOS OR Solid carcinoma, NOS 
OR SOLID CARCINOMA, NOS OR null OR Bronchiolo-alveolar carcinoma, non-mucinous OR Adenocarcinoma with 
mixed subtypes OR PAPILLARY ADENOCARCINOMA, NOS OR CLEAR CELL ADENOCARCINOMA, NOS OR Fetal 
adenocarcinoma OR Mucinous cystadenocarcinoma, NOS OR Signet ring cell carcinoma OR SIGNET RING CELL 
CARCINOMA OR ACINAR CELL CARCINOMA OR Acinar cell carcinoma OR CARCINOMA, NOS OR Carcinoma, NOS 
OR NON-SMALL CELL CARCINOMA OR Non-small cell carcinoma OR null OR Giant cell and spindle cell carcinoma 
OR Giant cell carcinoma OR Spindle cell carcinoma, NOS OR Pseudosarcomatous carcinoma OR Basaloid 
carcinoma OR Mucoepidermoid carcinoma OR MUCOEPIDERMOID CARCINOMA OR Adenosquamous carcinoma 
OR ADENOSQUAMOUS CARCINOMA OR Pulmonary blastoma OR Carcinosarcoma, NOS OR CARCINOSARCOMA, 
NOS] 
Type(s) of data: 
Electronic Health Record 
Software for data management: 
RStudio Pro (v4.1.0) via Posit Workbench 
 

 
42 
 
5.6.  Data management 
Data management and processing will be done using RStudio Pro (v4.1.0) via Posit Workbench and will proceed through the following phases: 
1. 
Data explorations: During this phase, the investigator will become acquainted with the data source and identify the specific variables that can be 
used to ascertain eligibility criteria, exposure, outcome, and confounders. These variables and the data tables that they may be found in will be 
listed in a spreadsheet. No analyses will be conducted outside of simple univariate summary distributions of the variables in the raw data. The 
purpose of this phase will be to explore missingness patterns and determine the utility of specific variables in key variable (e.g., exposure, outcome, 
confounder, selection criteria) ascertainment.   
2. Cohort selection: Cohort selection will be carried out in a manner specified by the current version of the protocol. Cohort(s) generated in this phase 
will be used to conduct feasibility analyses that will further inform protocol development or, if no further protocol changes are needed, creation of 
the analytic file. 
3. Analytic file: Person-level and person-time level analytic files will be created for the primary and secondary analyses, respectively. Each variable 
will be created based on operational definitions provided in this protocol for the exposure, outcome, and confounders of interest.  
5.7.  Quality control 
Upon receiving the data, programmers will visually inspect all data tables to ensure correct formatting and labelling of variables. Summary distributions 
will be inspected during the “Data explorations” phase of the data management process described in the data management section of this protocol. 
Software code written for the purposes of data management and statistical analysis will be reviewed using a formal code review process established at 
Aetion, which has been adapted from industry standards, best practices, and the experience of internal team members. A central GitHub repository will be 
used to share and review code. One to two reviewers will be responsible for reviewing each line of code and ensuring correct application of functions and 
methods (e.g., through spot-checking randomly selected samples of observations before and after application of code).  
5.8.  Study size and feasibility 
5.8.1.  CONSORT-style diagram for the primary intention-to-treat cohort 
 
Patients remaining in cohort 
(N) 
Patients lost from cohort 
(N) 
Total Patients in Dataset 
 
 
Inclusion Criteria 
 
 

 
43 
 
Stage 4 or metastatic lung cancer 
 
 
Non-small cell histology 
 
 
No epidermal growth factor receptor (EGFR) or anaplastic lymphoma 
kinase (ALK) mutations 
 
 
No prior systemic treatment for metastatic disease 
 
 
Exclusion Criteria 
 
 
Squamous cell morphology 
 
 
Biologic cancer therapy for metastatic disease  
 
 
Prior non-lung malignancy 
 
 
CNS metastases 
 
 
Treatment with disease modifying agents, corticosteroids, or 
immunosuppressive drugs 
 
 
Chronic corticosteroids 
 
 
Prior treatment with PD-L1 or PD-L2 agent or antibody targeting other 
immuno-regulatory receptors or mechanisms 
 
 
Total Patients that Received Exposure Drug Regimen 
 
Total Patients that Received Comparator Drug Regimen 
 
 
5.8.2.  Initial analysis of study cohort, unstratified by exposure (primary intention-to-treat cohort) 
An analysis unstratified by the exposure group will be conducted to examine the sample size, distribution of outcome events, follow-up time, and 
censoring events in the entire study population prior to matching to explore alignment with the KEYNOTE-189 trial and feasibility. 
Table 13.  Initial analysis of study cohort, unstratified by exposure 
Description 
Intention-to-treat cohort 
(N=) 
Exposure group – number of patients (n, %) 
 
Comparator group – number of patients 
(n, %) 
 
Outcome events (n) 
 

 
44 
 
Risk of outcome event (%) 
 
Rate of outcome event (n per 1,000 person-
years) 
 
Censoring events (n, %) 
 
Loss to follow-up (n, %) 
 
Administrative end of data (n, %) 
 
End of study period (n, %) 
 
Follow-up time in days (median [IQR]) 
 
Exposure group (median [IQR]) 
 
Comparator group (median [IQR]) 
 
A power assessment14 will be conducted in a 1:1 propensity score matched population to determine whether the RWE study is powered to detect a point 
estimate similar to that observed in the KEYNOTE-189 trial. Only age and sex will be used in this first iteration as previously described.12 Conservative 
estimates of statistical power are desired; therefore, power calculations will be conducted on a 1:1 matched population, where some patients will likely be 
discarded due to the matching process or missing data. The outcome of interest will not be assessed within exposure strata at this stage to eliminate the 
possibility of a priori knowledge of the outcome informing the study design. 
The mortality hazard ratio in the intention-to-treat analysis of the KEYNOTE-189 trial was 0.49, favouring the pembrolizumab-exposed treatment group. 
There were 410 patients randomized to the pembrolizumab group and 206 patients randomized the placebo-containing group in the trial.1 This 
observational study will seek to achieve a power of at least 80% to detect a hazard ratio of the same magnitude as the KEYNOTE-189 trial at a two-sided 
alpha level of 0.05. 
 
Table 14.  Power and sample size in 1:1 propensity score matched population (matched using basic covariates only) 
Number of matched patients 
 
Exposed 
 
Comparator 
 
Risk of outcome event 
 
Desired HR from KEYNOTE-189 
 
Alpha (2-sided) 
 
 

 
45 
 
Number of expected outcome 
events 
 
Power 
 
 
Provide a plot of the propensity score distribution stratified by treatment group before and after matching. 
Provide a table of baseline characteristics before and after matching and stratified by treatment group, including absolute standardized differences. 
 
5.8.3.  Final power and covariate balance assessment, unstratified by exposure (primary intention-to-treat cohort) 
A final power assessment14 will be conducted in a 1:1 propensity score matched population to determine whether the RWE study is powered to detect a 
point estimate similar to that observed in the KEYNOTE-189 trial. Numerous covariates will be used in this second iteration of the feasibility analysis as 
previously described.12 Not all covariates may be used due to the extent of missing data and matching will be conducted among complete cases only. 
The outcome of interest will also not be assessed within exposure strata at this stage. 
Table 15.  Power and sample size in 1:1 propensity score matched population (matched using many covariates) 
Number of matched patients 
 
Exposed 
 
Comparator 
 
Risk of outcome event 
 
Desired HR from KEYNOTE-189 
 
Alpha (2-sided) 
 
 
Number of expected outcome 
events 
 
Power 
 
 
Provide a plot of the propensity score distribution stratified by treatment group before and after matching. 
Provide a table of baseline characteristics before and after matching, including absolute standardized differences and quantification of missing values for 
each potential confounder variable, stratified by treatment group. 
 

 
46 
 
6.  Limitation of the methods 
This study has several methodologic limitations, as well as limitations that pertain to the study’s comparability to the KEYNOTE-189 trial.  
As with all epidemiologic studies, the possibility of residual or unmeasured confounding cannot be ruled out. Residual confounding may result from 
misspecification of statistical models (e.g., when estimating inverse probability treatment and censoring weights). To mitigate the risk of this bias, a 
flexible functional form of covariates in each model will be used where possible (e.g., including polynomial and interaction terms) to make fewer 
assumptions about the relation between the independent and dependent variables. An advantage of the data source is that patients’ medical histories 
are available through standardized diagnosis and procedure codes, as well as medication use. Collectively, these histories can be leveraged to ascertain 
many known prognosticators of mortality to further reduce the risk of unmeasured confounding accordingly. In the oncology population, the possibility of 
time-varying confounding is an important consideration, as patients’ health can rapidly decompensate over study follow-up. Furthermore, cancer 
treatment regimens tend to vary over time, involving several drugs. To account for treatments and prognosticators that vary over follow-up, a secondary 
analysis will be conducted using a marginal structural model to estimate a per-protocol effect. This approach addresses time-varying confounding by 
generating a pseudo-population, in which treatment at each discrete time interval during follow-up is independent of confounder history (under the 
assumption that all confounders were measured (i.e., “conditional exchangeability”), consistency, positivity, and no model misspecification).7 
In addition to confounding, selection bias may occur due to the presence of missing data or informative censoring. Missing data may induce selection 
bias if the mechanism giving rise to missing values cannot be adequately described or proxied by all or some of the observed variables in the analysis 
(i.e., data are “missing not at random”). Provided that the observed variables do fully relate to missingness (i.e., data are either “missing completely at 
random” or “missing at random”), several methods are available to correct for potential bias. Once missing data are fully described, including the extent 
of missingness for key variables in each treatment group, the mechanism underlying missingness will be hypothesized and addressed accordingly. 
Informative censoring arises when patients who remain within each risk set at a given discrete time during follow-up have a differential risk of the 
outcome than patients who were censored. Our secondary (per-protocol) analysis plan accounts for this by using inverse probability of censoring 
weights, under the assumption that all common causes of censoring and the outcome at each discrete time during follow-up are measured and not 
misspecified in the weight models. 
Information bias is a possibility in our analysis. Inaccurate or miscoding of standardized codes in the data could lead to measurement error. Furthermore, 
in the data source, medication orders, dispensations, and administrations are not always distinguishable. In the present analysis, all such medication 
records are treated equal, and may not represent the patient’s true experience. For this reason, a sensitivity analysis in which medication administrations 
using HCPCS/CPT codes will be used to define treatment exposures for patients. Notably, the mortality outcome has not been validated and is subject to 
measurement error, particularly as only month and year or mortality are provided in the data source (we assume date of death is first of the month 
reported). The pattern of mortality is unlikely to vary across treatment group (i.e., non-differential), which would pose no risk of bias to relative effect 
measures, such as hazard ratios. However, systematic under-recording  may affect absolute effect measures, such as the 12-month survival probabilities.  

 
47 
 
Although the primary intention-to-treat analysis is the same estimand from the KEYNOTE-189 trial, it is agnostic to adherence. Since adherence may differ 
substantially between the trial participants and routine clinical practice, a per-protocol effect that accounts for treatment adherence will be conducted in 
this study. An additional limitation of the primary (intention-to-treat) analysis is the misalignment of treatment initiation with respect to calendar time. We 
address this with a sensitivity analysis restricting the cohort entry dates (time zero) to be contemporaneous with respect to exposure group. 
Lastly, this study is being performed to assess the ability of real-world data in this specific treatment setting to reach similar conclusions to randomized 
clinical trials using the KEYNOTE-189 trial as a benchmark for causal inference. This study may fail to align with the trial’s results for many reasons that are 
not rooted in bias, including but not limited to differences in study samples (particularly with respect to effect modifiers), random sampling variability, 
differences in adherence and follow-up, and differences in the measurement of study eligibility criteria, exposures, and outcomes. Therefore, it will be 
critical to evaluate the results of this study in light of all reasons that could explain any observed differences (or similarities). 
 
 
7.  References 
1. Gandhi L, Rodríguez-Abreu D, Gadgeel S, et al. Pembrolizumab plus Chemotherapy in Metastatic Non–Small-Cell Lung Cancer. N Engl J Med. 
2018;378(22):2078-2092. doi:10.1056/NEJMoa1801005 
2. Food and Drug Administration. FDA grants regular approval for pembrolizumab in combination with chemotherapy for first-line treatment of metastatic 
nonsquamous NSCLC. FDA. Published November 3, 2018. Accessed April 4, 2023. https://www.fda.gov/drugs/resources-information-approved-
drugs/fda-grants-regular-approval-pembrolizumab-combination-chemotherapy-first-line-treatment-metastatic 
3. Hernán MA, Robins JM. Using Big Data to Emulate a Target Trial When a Randomized Trial Is Not Available: Table 1. Am J Epidemiol. 2016;183(8):758-764. 
doi:10.1093/aje/kwv254 
4. Chemotherapy in non-small cell lung cancer: a meta-analysis using updated data on individual patients from 52 randomised clinical trials. Non-small 
Cell Lung Cancer Collaborative Group. BMJ. 1995;311(7010):899-909. 
5. Scagliotti GV, Parikh P, von Pawel J, et al. Phase III Study Comparing Cisplatin Plus Gemcitabine With Cisplatin Plus Pemetrexed in Chemotherapy-Naive 
Patients With Advanced-Stage Non–Small-Cell Lung Cancer. J Clin Oncol. 2008;26(21):3543-3551. doi:10.1200/JCO.2007.15.0375 
6. Schiller JH, Harrington D, Belani CP, et al. Comparison of Four Chemotherapy Regimens for Advanced Non–Small-Cell Lung Cancer. N Engl J Med. 
2002;346(2):92-98. doi:10.1056/NEJMoa011954 

 
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7. Hernán MA, Robins JM. Causal Inference: What If. Chapman & Hall/CRC; 2020. https://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/ 
8. Zeng X, Zhu S, Xu C, et al. Effect of Comorbidity on Outcomes of Patients with Advanced Non-Small Cell Lung Cancer Undergoing Anti-PD1 
Immunotherapy. Med Sci Monit Int Med J Exp Clin Res. 2020;26:e922576. doi:10.12659/MSM.922576 
9. Zhao L, Leung LH, Wang J, et al. Association between Charlson comorbidity index score and outcome in patients with stage IIIB-IV non-small cell lung 
cancer. BMC Pulm Med. 2017;17(1):112. doi:10.1186/s12890-017-0452-0 
10. Buuren S van. Flexible Imputation of Missing Data. CRC Press; 2012. 
11. Westreich D, Cole SR, Tien PC, et al. Time scale and adjusted survival curves for marginal structural cox models. Am J Epidemiol. 2010;171(6):691-700. 
doi:10.1093/aje/kwp418 
12. Merola D, Campbell U, Gautam N, et al. The Aetion Coalition to Advance Real‐World Evidence through Randomized Controlled Trial Emulation Initiative: 
Oncology. Clin Pharmacol Ther. Published online December 7, 2022:cpt.2800. doi:10.1002/cpt.2800 
13. Joshua Lin K, Jin Y, Gagne J, et al. Longitudinal Data Discontinuity in Electronic Health Records and Consequences for Medication Effectiveness Studies. 
Clin Pharmacol Ther. 2022;111(1):243-251. doi:10.1002/cpt.2400 
14. Chow SC, Shao J, Wang H, eds. Sample Size Calculations in Clinical Research. 2nd ed. Chapman & Hall/CRC; 2008. 
 
8.  Appendices 
Appendix I. Key variable descriptions 
Appendix II. Comparison of protocol elements between the real-world evidence study and the KEYNOTE-189 trial 
Appendix III. Standardized billing codes used to define key variables 
Appendix IV. Missing data: Mechanisms, assumptions, and methodological approach