Emulation of the PALOMA-2 Trial
Conditions: Advanced Breast Cancer
Sponsor: Brigham and Women's Hospital

 
 
 
 
 
 
Emulation of the PALOMA-2 trial using specialty oncology electronic 
health records databases 
 
NCT06659744 
 
18th August 2025 
 
 

 
 
 
1.  Title Page 
 
Title 
Emulation of the PALOMA-2 (NCT01740427) trial using specialty oncology 
electronic health records databases 
Research question & Objectives 
Emulation of the PALOMA2 (NCT01740427) trial, which compared 
palbociclib–letrozole to letrozole alone on progression-free survival (PFS) 
as primary and overall survival (OS) as secondary analysis in 
postmenopausal women with estrogen receptor (ER)-positive, human 
epidermal growth factor receptor 2 (HER2)-negative breast cancer  
Protocol version 
V1.1 
Last update date 
August 18, 2025 
Contributors 
Primary investigators contact information:  
Denys Shay, Shirley V. Wang  
Contributor names: 
Janick Weberpals 
Study registration  
Site: clinicaltrials.gov 
Identifier: NCT01740427 
Sponsor 
Organization: Food and Drug Administration 
Contact: n/a 
Conflict of interest 
SVW has been an ad hoc consultant to Exponent Inc, Cytel Inc, and MITRE 
a federally funded research and development center for the Centers for 
Medicare and Medicaid 
Protocol repository 
Clinicaltrials.gov  
Analytic code repository 
https://gitlab-scm.partners.org/drugepi/encore/paloma2-nct-01740427 
Quarto study report  
(including annotated code and output) 
https://drugepi.gitlab-pages.partners.org/encore/paloma2-nct-
01740427/ 
encore.io1 version 
0.2.0 (see attached documentation encore.io_0.2.0.pdf) 
1 Internally-developed R package to streamline analytics across all available databases and to enhance consistency, 
transparency and reproducibility in variable definitions and analytic workflows across trial emulations. 
 

2 
 
Table of contents 
1. Title Page ............................................................................................................................................. 1 
2. Abstract ............................................................................................................................................... 3 
3. Amendments and updates .................................................................................................................... 3 
4. Rationale and background .................................................................................................................... 3 
5. Research question and objectives ........................................................................................................ 4 
6. Research methods ................................................................................................................................ 7 
6.1. Data sources ...................................................................................................................................................... 7 
6.1.1. Context and rationale for data sources ............................................................................................................ 7 
6.2. Data management .............................................................................................................................................. 10 
6.3. Quality control ................................................................................................................................................... 10 
6.4. Study design ..................................................................................................................................................... 10 
6.5. Study design diagram .......................................................................................................................................... 10 
6.6. Setting ........................................................................................................................................................... 12 
6.6.1. Context and rationale for definition of time 0 (and other primary time anchors) for entry to the study population .... 12 
6.6.2. Context and rationale for study inclusion criteria ............................................................................................. 12 
6.6.3. Context and rationale for study exclusion criteria ............................................................................................ 12 
6.6.4. Context and rationale for exposure(s) of interest ............................................................................................ 12 
6.6.5. Context and rationale for outcome(s) of interest ............................................................................................. 13 
6.6.6. Context and rationale for follow up ............................................................................................................... 15 
6.6.7. Context and rationale for covariates .............................................................................................................. 15 
6.7. Data analysis .................................................................................................................................................... 20 
6.7.1. Context and rationale for analysis plan .......................................................................................................... 20 
6.8. Study size and feasibility ....................................................................................................................................... 28 
7. Limitation of the methods .................................................................................................................. 28 
8. Protection of human subjects ............................................................................................................. 28 
9. References ......................................................................................................................................... 29 
10. Appendices ....................................................................................................................................... 31 
10.1. CONSORT diagrams ........................................................................................................................................... 31 
10.2. Covariate balance figures .................................................................................................................................... 34 
10.3. Sample size/power calculations ............................................................................................................................. 37 
10.4. Additional Figures and Tables ................................................................................................................................ 38 
 

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2.  Abstract 
This trial emulation study aims to emulate the PALOMA-2 trial (NCT01740427) using real-world specialty oncology electronic health records data and to investigate 
the concordance between the trial’s original and the emulated treatment effect estimate on overall survival (OS). PALOMA-2 was a Phase III, randomized, double-blind 
study evaluating the efficacy and safety of palbociclib (125 mg orally, once daily for 21 days followed by 7 days off) in combination with letrozole (2.5 mg orally, once 
daily, continuously) versus placebo plus letrozole in postmenopausal women with estrogen receptor-positive (ER+), human epidermal growth factor receptor 2-
negative (HER2−) advanced breast cancer who had not received prior systemic treatment for their advanced disease. 
 
3.  Amendments and updates 
Version date 
Version number 
Section of protocol 
Amendment or update 
Reason 
June 11, 2025 
V1.0 
NA 
Initial version 
NA 
August 18 , 2025 
V1.1 
4. Rationale and 
background 
5. Research question 
and objective 
6. Research methods 
Refinement of text based on FDA 
feedback 
NA 
  
4.  Rationale and background 
Randomized controlled trials (RCTs) are generally regarded as the gold-standard of evidence for establishing efficacy of medical products. However, real-world data 
(RWD) are increasingly used to complement evidence from RCTs. Yet, to have confidence in the accuracy in the accuracy of treatment efficacy estimates from RWD, 
investigators need to know what questions can be validly answered, with which non-interventional study designs, and which analysis methods are appropriate, given 
the data that is available. Building on a process from the RCT DUPLICATE initiative.1-4 EmulatioN of Comparative Oncology trials with Real-world Evidence (ENCORE) 
is the trial emulation discussed in this protocol is part of the expansion project specific to oncology and aims to emulate 12 randomized oncology RCTs using multiple 
EHR data sources. 
The purpose of this protocol is to describe the emulation of the PALOMA-2 trial. PALOMA-2 was a Phase III, double-blind, randomized study assessing the efficacy and 
safety of palbociclib (125 mg orally, once daily for 21 days of a 28-day cycle) in combination with letrozole (2.5 mg orally, once daily, continuously) versus 
letrozole alone in postmenopausal women with estrogen receptor-positive (ER+), human epidermal growth factor receptor 2-negative (HER2−) advanced breast 
cancer who had not received prior systemic treatment for their advanced disease. 

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The primary trial endpoint was progression-free survival (PFS) with a hazard ratio (HR) of 0.58 (95% CI, 0.46 to 0.72), based on a median PFS of 24.8 months (95% 
CI, 22.1 to not estimable) in the palbociclib-plus-letrozole group compared with 14.5 months (95% CI, 12.9 to 17.1) in the letrozole-alone group. The FDA granted 
palbociclib accelerated approval in February 2015, in combination with letrozole for the treatment of ER-positive, HER2-negative advanced breast cancer as initial 
endocrine based therapy in postmenopausal women. 
Overall survival (OS) was a secondary trial endpoint with a hazard ratio of 0.96 (95% CI, 0.78 to 1.18; one-sided p = 0.34) based on a median OS of 53.9 months in the 
palbociclib-plus-letrozole group and 51.2 months in the letrozole-alone group. 
 
The results of the PFS endpoint were published in the NEJM on November 17, 2016 (PMID 27959613).5 
 
The results of the OS endpoint were published in the J. Clin. Oncol. on January 22, 2024 (PMID 38252901).6 
 
5.  Research question and objectives 
The primary and secondary research question is summarized in Table 1. 
A. Primary research question and objective 
Table 1. Primary and secondary research questions and objective. 
Objective: 
To compare the overall survival [OS] in patients who initiated palbociclib plus letrozole versus patients 
who initiated letrozole alone. 
Hypothesis: 
Initiation of palbociclib plus letrozole improves OS time as compared to initiation of letrozole alone. 
Population (mention key inclusion-exclusion 
criteria): 
• 
Age ≥18 years 
• 
Postmenopausal women with ER-positive, HER2-negative locally advanced or metastatic 
breast cancer who receive treatment without curative intent 
o The line of therapy for patients in EDB1 and EDB3 is implicitly advanced/metastatic 
because the line of therapy classification starts after their advanced/metastatic 
diagnosis in the respective database 
o In EDB4, patients must explicitly have any evidence of a metastasis prior initiating 
palbociclib plus letrozole or letrozole alone 
• 
Adenocarcinoma histology 

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• No prior systemic treatment for advanced/metastatic disease 
•  ECOG 0 or 1 
Exposure: 
Initiation of palbociclib plus letrozole 
Comparator: 
Initiation of letrozole 
Outcome: 
Primary: Time to all-cause mortality (OS) 
Secondary: Time to next treatment (TTNT) 
Time (when follow up begins and ends): 
From the day of treatment initiation until outcome. Death, last observed clinical activity/last sign of 
the patient being alive, or data cut-off, whichever occurred earliest 
Setting: 
1L ER-positive, HER2-negative locally advanced or metastatic adenocarcinoma of the breast 
Main measure of effect: 
Hazard ratio (95% CI) for OS 
 
The emulation of the main protocol elements of the PALOMA-2 is illustrated side by side in Table 2. 
 
 
 
 
Table 2. Trial emulation table summarizing the main protocol elements of the PALOMA-2 trial and the planned emulation. 
Protocol 
component 
PALOMA-2 RCT 
Emulation 
Comments 
Eligibility criteria 
• Women aged ≥18 years with 
diagnosis of breast cancer 
• Female aged ≥18 years at treatment initiation with a diagnosis of breast 
cancer 
  
  
• Histologically or cytologically 
confirmed adenocarcinoma of the 
breast 
• Diagnosis of breast adenocarcinoma via histology/pathology records 
  
  
• Locoregionally recurrent or 
metastatic disease not amenable to 
curative resection/radiotherapy 
• Evidence of metastatic or recurrent disease 
  
  
• ER-positive status 
• Documentation of ER-positive (or -missing) status 
• If ER is missing and a patient received the 
exposures of interest, then ER is likely to be 
positive given the alignment with the 
indication for the exposures of interest 

6 
 
  
• HER2-negative status 
• Documentation of HER2-negative (or -missing) status 
• If HER2 is missing and a patient received 
the exposures of interest, then HER2 is 
likely to be negative given the alignment 
with the indication for the exposures of 
interest 
  
• Postmenopausal status 
• N/A 
• Although not directly captured in RWD, it 
is likely to be fulfilled given the alignment 
with the indication for the exposures of 
interest 
  
• No prior systemic therapy for 
advanced/metastatic ER+ disease 
• No systemic anti-cancer therapy* following initial record indicating 
metastatic disease and prior to index date 
  
  
• Measurable disease per RECIST 
v1.1 or bone-only disease 
confirmed by imaging 
• N/A 
• It is reasonable to assume that all 
patients in RWD had evidence of disease if 
they received treatment; additionally OS 
primary endpoint does not require 
measurable disease 
  
• WHO/ECOG performance status 0 
or 1 
• WHO/ECOG performance status of 0 or 1 within 90 days of index date 
  
  
• No symptomatic, life-threatening 
visceral disease (e.g., massive 
effusions, >50% liver involvement) 
• N/A 
• This is a safety criterion for the trial and 
less relevant to real-world treatment setting, 
given that patients received treatments of 
interest 
  
• No active or symptomatic CNS 
metastases 
• Record of CNS metastases prior to or on index date 
  
  
• No prior treatment with CDK4/6 
inhibitors 
• Exclude patients with prior CDK4/6 inhibitor exposure 
  
  
• No recurrence during or within 12 
months of adjuvant non-steroidal AI 
(anastrozole or letrozole) 
• Treatment with letrozole or anastrozole ≤12 Months prior to date of 
metastatic diagnosis 
  
  
• No other malignancy within 3 
years 
• No prior record of non-breast malignancy within 3 years 
  
Treatment 
strategies 
Palbociclib + letrozole vs. placebo + 
letrozole 
Patients initiating palbociclib + letrozole vs. letrozole alone 
Use treatment start dates to define 
exposure 
Assignment 
procedures 
Randomized 2:1 to palbociclib + 
letrozole or placebo + letrozole 
Propensity score–based matching or weighting to emulate randomization 
Balance baseline covariates to reduce 
confounding 
Follow-up period 
Time from randomization to death 
or censoring 
Time from treatment initiation to death or censoring 
  
Outcome 
Primary: Progression-free survival 
(PFS) per investigator assessment 
Secondary: Overall survival (OS) 
Primary: OS  
Secondary:  Time-To-Next-Treatment (TTNT) 
Lack of good measurement of progression, 
so inferred by initiation of next treatment as 
a secondary endpoint rather than primary 

7 
 
Causal contrast 
Intent-to-treat effect 
Effect of initiating palbociclib + letrozole versus letrozole 
Analogous to ITT; emulates initiation rather 
than adherence 
 
*Includes the following: alpelisib, capecitabine, carboplatin, cisplatin, cyclophosphamide, docetaxel, doxorubicin, epirubicin, eribulin, exemestane, fluorouracil, gemcitabine, goserelin, 
ixabepilone, methotrexate, olaparib, paclitaxel, tamoxifen, talazoparib, toremifene, and vinorelbine. 
 
6.  Research methods 
6.1.  Data sources 
6.1.1.  Context and rationale for data sources 
The overall ENCORE project uses data from a total four different oncology-specific electronic health records (EHR)-derived data sources: ConcertAI, COTA, Flatiron 
Health, McKesson/Ontada. For ENCORE, not all databases are available for each cancer indication and the names of the databases will henceforth be blinded and 
referred to as ENCORE DataBase (EDB) 1, 2, 3 and 4 (the numbering does not coincide with the above order of mention of the databases). 
For this trial emulation, breast cancer-specific data are available for EDB1, EDB3 and EDB4. The fitness-for-purpose of the data for the given trial emulation were 
assessed and considered for the final selection of the databases. 
Reason for selection: All considered databases draw from a comprehensive national sample of patients with cancer in the US with detailed EHR-derived information 
on the information necessary to study medication effectiveness in oncology. 
Strengths of data source(s): Size and detailed clinical information on oncology-specific variables and outcomes (validated composite all-cause mortality sourced from 
different data sources7,8). 
Limitations of data source(s): General limitations across all data sources include missing data, potential lack of data continuity, heterogeneous data provenance, 
quality/heterogeneous ascertainment of mortality endpoint data  and the variability in how line of treatment is captured and curated (a more comprehensive 
discussion of the data sources and approaches is provided in section 7. After a comprehensive assessment of all data sources regarding their fitness for the purpose 
of emulating the PALOMA-2 trial. For these reasons, only EDB1 and EDB3 will be used for the main analysis. However, EDB4 will be considered as part of a sensitivity 
analysis in which all databases are individually analyzed and treatment effect estimates are pooled using a meta-analytic fixed effects model (see sensitivity analysis 
#11 in Table 8). 
 
 
 

8 
 
Data source provenance/curation: In brief, all databases provide EHR-derived oncology-specific patient-level information which are either derived directly (e.g., 
through structured data fields and dropdown menu selections) from EHR and/or undergo semi-automated abstraction processes from unstructured reports. The 
detailed data provenance, abstraction processes and implemented business rules to curate and prioritize certain variables may vary by database and can be found in 
legacy publications by the data partners. 
Table 3. Metadata about data sources and software. 
 
EDB1 
EDB3 
EDB4 
Data Source(s): 
EHR-derived 
EHR-derived 
EHR-derived 
Study Period: 
Patient identification period: 01/01/2011-
04/30/2024 with follow-up information 
through data cut-off date on 04/30/2024 
Follow-up information through June 
2023 (there is no specific time period 
restrictions for patient eligibility) 
Patient identification period: 10/01/2018-
09/30/2023 with follow-up information through 
data cut-off date on 09/30/2023. 
Eligible Cohort Entry Period: 
Anytime at start of study drug initiation 
Anytime at start of study drug 
initiation 
Anytime at start of study drug initiation 
Data Version (or date of last update): 
Delivery: Jul 11, 2024 
Delivery: Jun 16, 2023 
Delivery: Oct 24, 2023 
Updated (demographics): Feb 29, 2024 
Data sampling/extraction criteria: 
Patients are sampled if they have a 
confirmed diagnosis of metastatic breast 
cancer via abstraction on or after 1 Jan 
2011, and at least 2 EHR visits on or after 
1 Jan 2011. Both ICD-9 (174.x) and ICD-10 
(C50.x) codes are used for the initial 
selection, and advanced diagnosis are 
then confirmed via abstraction (since ICD 
codes do not specify advanced diseases). 
 
This database identifies patients for 
curation using a structured ICD-10 
diagnosis code (ICD-10 C50*), 
corresponding to the indication of 
interest, along with at least the year 
of diagnosis. Once this initial 
screening list is generated, patients 
are randomly selected for further 
review. Curation begins with 
confirmation of the diagnosis and 
diagnosis date, primarily based on 
pathology reports and other 
unstructured data sources. All 
patients must be over 18 years of 
age at the time of their first 
diagnosis. Certain breast cancer 
cases are excluded from curation. 
Specifically, in-situ breast cancers 
such as ductal carcinoma in situ 
(DCIS) or lobular carcinoma in situ 
(LCIS) without an invasive component 
are not eligible. However, DCIS with 
microinvasion or DCIS associated 
with Paget’s disease qualifies for 
curation. Additionally, in-situ breast 
cancers that later progress to 
invasive carcinoma are eligible; in 
Breast cancer patients with an office visit in the 
reporting period will be included in the report 
with full patient history. Patients are sampled if 
they were diagnosed with breast cancer and 
with a documented visit date, within the defined 
reporting period, to one of the network facilities 
and were at least 20 years of age at the time of 
first diagnosis. Patients who were on a clinical 
trial at any point in their treatment history are 
excluded. 

9 
 
these cases, the diagnosis date 
should reflect the date of the original 
in-situ diagnosis, not the later 
invasive diagnosis. 
Patients are ultimately selected for 
inclusion in data products through 
quality control processes that assess 
consistency and potential conflicts in 
their records. These evaluations may 
rely on structured data alone or a 
combination of structured and 
curated data, depending on the 
specific data product. Importantly, 
CAI does not exclude patients based 
on data completeness, in order to 
prevent the introduction of selection 
bias. 
Type(s) of data: 
EHR-derived 
EHR-derived 
EHR-derived 
Data linkage1: 
Mortality/date of death is a composite 
endpoint of structured and unstructured 
data from the EHR, obituary data, and the 
social security death index 
Mortality/date of death is a 
composite endpoint derived from 
structured EHR data, manual 
curation, and third-party sources 
including obituary data and the Social 
Security Death Index. De-identified 
tokens link patients across datasets 
using hashed PII. Curated data is 
prioritized, followed by EHR and then 
third-party sources. Reported death 
dates are shifted to the nearest 
Sunday within four days to enhance 
privacy. Curated death information 
follows a source hierarchy: death 
certificate, obituary, or provider-
reported date. 
Mortality/date of death is a composite endpoint 
of structured EHR data and linked commercial 
data from clearinghouse providers hosted by 
Datavant. 
Conversion to CDM2: 
No 
Yes 
No 
Software for data management: 
R 4.3.2 
R 4.3.2 
R 4.3.2 
 
1 Mortality/date of death is a composite endpoint that is often derived from various linked sources including social security death 
index/ Social Security Administration death master file, obituary data and EHR records 
2 CDM = Common Data Model 
 

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6.2.  Data management 
Data is stored on secure Mass General Brigham corporate provisioned and backed up servers physically located in our Mass General Brigham corporate data centers. 
Mass General Brigham corporate data centers are designed to insure availability of the affiliated hospitals’ and research applications and IT systems in the event of a 
disaster. The Division follows Mass General Brigham workstation requirements which include: encryption at rest, up-to-date malware protection including antivirus, 
spyware detection and removal tools, Crowdstrike End Point protection installed, devices enrolled in enterprise Mobile Device Management (MDM) solution as 
appropriate, any laptop/computer used for business purposes must not be shared with family, friends, or other unauthorized individuals, and compliance with 
enterprise Password Requirements. Only authorized personnel have read-only access to raw data files. 
Cleaned and analysis-ready datasets, i.e., +/- imputed one-row-per-patient tables with all required exposure, outcome and covariate variables, are stored in separate 
sub-directories dedicated for the specific emulated trial. 
6.3.  Quality control 
Upon delivery, data quality procedures included checks on delivered tables and variables, per table checks, descriptives on most important measures such as 
demographic and stage distributions by sex at time of initial diagnosis, regimen/exposure frequency counts and time-trends and overall survival benchmarks against 
literature and general cancer registry statistics. The R code to reproduce the quality assessments is deposited on the Mass General Brigham-provisioned GitLab 
server https://gitlab.partners.org/drugepi/encore/quality (repository is only accessible within the Mass General Brigham network and additionally only to authorized 
study personnel). 
6.4.  Study design 
Research design (e.g. cohort, case-control, etc.): Cohort study 
Rationale for study design choice: Resembles the principles of the (target) trial emulation framework.9 
6.5.  Study design diagram 
 Figure 1 depicts study design and variable measurement considerations for the emulation of the PALOMA-2 trial. The selection of key confounders/prognostic factors 
is driven by expert knowledge and additionally based on covariates included in the real-world prognostic score (ROPRO) which is a published and validated pan-tumor 
and cancer-specific prognostic score framework for overall survival.10-14 
 

11 
 
 Figure 1. Study design illustration for PALOMA-2 trial emulation. 
  
 
     

12 
 
6.6.  Setting 
6.6.1.  Context and rationale for definition of time 0 (and other primary time anchors) for entry to the study population  
Time 0 in this database study is defined as the date a patient initiated palbociclib plus letrozole (exposure) or letrozole alone (comparator) as part of their 1L systemic 
antineoplastic treatment for advanced/metastatic adenocarcinoma of the breast. This aims to emulate the date of randomization and cohort entry in the RCT (the 
time from randomization to first dose is not reported in the clinicaltrials.gov study report or the trial articles). 
6.6.2.  Context and rationale for study inclusion criteria 
Study inclusion criteria were defined to emulate all key inclusion criteria for the trial that were deemed both clinically relevant and measurable in EHR data. See Excel 
appendix table 1 (Table1_I_E) for a one-by-one evaluation. A summary of the operational definitions of the inclusion criteria that were applied for each database can 
be found in the Excel appendix Table 2. A flowchart of the study cohort assembly is provided in Appendix 3.  
6.6.3.  Context and rationale for study exclusion criteria 
Study exclusion criteria were defined to emulate all key exclusion criteria for the trial that were deemed both clinically relevant and measurable in EHR data. See Excel 
appendix table 1 (Table1_I_E) for a one-by-one evaluation. A summary of the operational definitions of the exclusion criteria that were applied for each database can 
be found in the Excel appendix Table 2. A flowchart of the study cohort assembly is provided in Appendix 3. 
6.6.4.  Context and rationale for exposure(s) of interest 
The exposure and comparator were defined to emulate the agents compared for the trial, i.e., initiation of palbociclib plus letrozole versus letrozole alone in a 1L 
metastatic setting.  
• 
EDB1: Exposure is derived using a manually curated line of therapy (LOT) table provided by the data partner that programmatically categorizes treatment 
regimens into a coherent line of treatment. That is, each patient is represented with one row per curated line of therapy with corresponding information on 
line number, regimens as well start and end dates.  Based on this table, patients are identified who received palbociclib-plus-letrozole or letrozole-alone 
treatment regimen by their generic names (string match) in 1L. The LOT implicitly only considers regimens that were given as part of a metastatic disease 
setting. More details and annotated code to identify initiators can be found in the ‘Derive cohort EDB1’ Quarto report (access within MGB network only). 
• 
EDB3: Exposure is derived using a manually curated line of therapy (LOT) table provided by the data partner that programmatically categorizes treatment 
regimens into a coherent line of treatment. That is, each patient is represented with one row per curated line of therapy AND drug name with 
corresponding information online number, regimens as well start and end dates.  Based on this table, patients are identified who received only palbociclib 
and letrozole within the first line of therapy, and letrozole alone within the first line of therapy by their generic names (string match). The LOT implicitly only 
considers regimens that were given as part of a metastatic disease setting. More details and annotated code to identify initiators can be found in the 
‘Derive cohort EDB3’ Quarto report’ (access within MGB network only). 
• 
EDB4: For the EDB4 database, the following logic is applied.  

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o Identify patients with evidence of a metastasis from the diagnosis table in which the earliest date associated with evidence of metastasis is 
captured as a structured field (metastasis date). 
o Identify all potential antineoplastic drugs typically used in advanced/metastatic breast cancer (see list below*). Only these are considered. 
o Identify patients who received any of the PALOMA-2 drugs as the first antineoplastic treatment on or after the metastasis date. 
o Identify and exclude patients who received palbociclib before the metastasis date. 
 
*Antineoplastic drugs considered: abemaciclib, alpelisib, anastrozole, atezolizumab, bevacizumab, capecitabine, carboplatin, cisplatin, cyclophosphamide, 
docetaxel, doxorubicin, entrectinib, epirubicin, eribulin mesylate, etoposide, everolimus, exemestane, fluorouracil (5-FU), fulvestrant, gemcitabine, goserelin, 
ixabepilone, larotrectinib, lapatinib, letrozole, margetuximab, methotrexate, mitomycin, mitoxantrone, nab-paclitaxel, neratinib, olaparib, paclitaxel, palbociclib, PARP 
inhibitor therapy, pembrolizumab, pertuzumab, ribociclib, sacituzumab govitecan, talazoparib, tamoxifen, toremifene, trastuzumab, trastuzumab deruxtecan, 
trastuzumab emtansine (T-DM1), tucatinib, vinblastine, vincristine, and vinorelbine. 
6.6.5.  Context and rationale for outcome(s) of interest 
The primary outcome for the database study was defined to emulate the primary outcome for the trial, time from index to death due to any reason (OS). 
Operational definitions: 
• 
EDB1: Time in [days, months and years] from index date to death due to any reason. The date of death is de-identified to month-level granularity or (rarely) 
to year-level granularity and the date of death is therefore imputed to the 15th of a month or mid-year/July 2 of the year of death, respectively. In If there is 
no indication that a patient died during the study period, the patient is censored. The censoring date is defined as the last visit or treatment encounter or 
data cut-off date, whichever occurred earlier. The OS endpoint is operationalized using a parameterized R function edb1_get_os() and more details can be 
found in the attached pdf documentation. 
• 
EDB3: Time in [days, months and years] from index date to death due to any reason. The date of death is de-identified to month-level granularity and the 
day of death is therefore imputed to the 15th of a month. Patients without evidence of death are censored at the earlier of the last recorded activity date or 
the data cut-off date. Activity dates are defined as documented in Table 4. Death dates are compiled from Electronic Medical Records (EMR), manual 
curation, and third-party death data, linked via de-identified tokens generated from hashed personally identifiable information (PII). Sources of death dates 
are prioritized as follows: (1) manual curation, (2) EMR, and (3) third-party sources. For privacy reasons, the final reported death date is shifted to the 
nearest Sunday within four days of the actual date. Manually curated death dates follow a prioritization hierarchy of source documentation: death 
certificate first, followed by scanned obituaries or death announcements, and lastly exact dates reported by providers without other supporting 
documentation. The OS endpoint calculation uses a parameterized R function (edb3_get_os()), with detailed documentation provided in the attached PDF. 
• 
EDB4: Time in [days, months and years] from index date to death due to any reason. The date of death is de-identified to month-level granularity and the 
day of death is therefore imputed to the 15th of a month. If there is no indication that a patient died during the study period, the patient is censored. The 
censoring date is defined as the last date of vital signs recorded as proof that the patient was alive at that time (de-identified to week-level granularity) or 

14 
 
data cut-off date, whichever occurred earlier. The OS endpoint is operationalized using a parameterized R function edb4_get_os() and more details can be 
found in the attached pdf documentation. 
 
Table 4. Relevant clinical activities considered to derive last activity date for censoring. 
Table / clinical activity considered 
Dates considered 
Adverse events 
Event date 
Therapy (cellular, systemic, radiation, 
surgery) 
Start and end dates or declined intervention date, surgery date, 
assessed resection dates 
Palliative care referral 
Referral date 
Visits 
Contact/visit date 
Vitals 
Assessed date 
Labs 
Lab date 
Biomarkers 
Specimen collection date 
Patient observation period 
Start and end dates 
Demographics 
Date of most recent contact with provider, date patient was diagnosed 
with a second primary malignancy 
Performance assessments 
Documented date, reported date 
Secondary diagnoses 
Diagnosis date 
Progression, histology, lymphovascular 
invasion, metastatic sites, pancoast 
tumor, perineural invasion 
Assessed date 
Stage/TNM 
Assessed date 
Smoking 
Assessed date 
 

15 
 
6.6.6.  Context and rationale for follow up  
Only intention-to-treat (ITT) analyses will be conducted. Although cross-over from the exposure to the comparator can be expected which biases the exposure 
treatment effect more towards the null, this also applies to the RCT. According to Slamon et al., although cross-over was not permitted during the study, a total of 399 
(89.9%) patients in the palbociclib plus letrozole arm and 217 (97.7%) in the placebo plus letrozole arm discontinued treatment. 47 (11.8%) and 58 (26.7%) patients 
received a CDK4/6 inhibitor(s) poststudy, respectively; of these, the majority received palbociclib (74.5% in the palbociclib plus letrozole arm and 89.7% in the 
placebo plus letrozole arm, which would constitute a crossover).6 
An as-treated analysis is not considered since in the context of oncology, reasons for discontinuation usually are due to toxicity, death or progression/non-response to 
the current treatment, all of which are highly correlated with the outcome under study which would hence lead to bias due to informative censoring.  
 
6.6.7.  Context and rationale for covariates 
We identified a series of covariates that are strong prognostic factors for the outcome and auxiliary covariates which may be useful to impute missing data. Such 
covariates comprise demographics, covariates indicating disease-severity, cancer-specific covariates as well as pathological and genetic factors. In addition, selected 
labs and vitals are considered since they were shown to carry a high amount of prognostic information as described in Becker, Weberpals, et al.10 For these variables, 
additional plausibility checks and transformations are carried out. In detail, labs and vitals are individually checked if they cross a certain biologically implausible 
threshold (e.g., a heart rate of 0) in which cases the values are set missing and imputed in a next step. These thresholds were compiled by experienced practicing 
physicians and medical oncologists and are listed in appendix Table 9 and Table 10. 
Note that not all covariates are available across all databases used for this trial emulation. In the analytical stage, the most comprehensive model will be fit for each 
database individually. 
Table 5. Operational definitions of key covariates used for trial emulation. 
Characteristic 
Harmonized analysis 
variable name 
R function to derive 
covariate (see pdf in 
appendix) 
Details1 
Variable encoding 
Assessment window 
Age at initial 
diagnosis 
dem_age_initial_diagnosis 
edbx_get_demographics() 
Age measured at initial diagnosis 
of eligible primary tumor 
Nominal (<60, 60-69, 70-79, 
80+) 
[-inf;0] at initial diagnosis 
of primary cancer 
Age at index date  
dem_age_index 
edbx_get_demographics() 
Age measured at index date 
Binary (<60, 65+); modelled 
continuously in ROPRO10 
[0;0] 
Sex 
dem_sex 
edbx_get_demographics() 
Sex  
Binary (Male, Female) 
[0;0] 
Year of index date  
c_year_index 
De novo derived from 
dt_index 
Calendar year in which patient 
initiated study treatment  
Nominal (<2018, 2018+) 
[0;0] 
Family history 
dem_family_history 
edbx_get_demographics() 
Family history of cancer 
Logical (TRUE, FALSE) 
[0;0] (no specific date is 
associated) 
Race 
dem_race 
edbx_get_demographics() 
Race categorized as in the 
original RCT 
Binary (Asian vs non-Asian) 
[0;0] 

16 
 
Characteristic 
Harmonized analysis 
variable name 
R function to derive 
covariate (see pdf in 
appendix) 
Details1 
Variable encoding 
Assessment window 
Ethnicity 
dem_ethnicity 
edbx_get_demographics() 
Ethnicity 
Hispanic, Non-Hispanic 
[0;0] 
Region 
dem_region 
edbx_get_demographics() 
US region patient receives 
care in; if given on a state 
level, region is manually 
mapped (see 
Table 11) 
Nominal (Northeast, South, 
West, Midwest) 
[0;0] 
Practice type 
dem_practice 
edbx_get_demographics() 
Setting patient is receiving care at 
Nominal (academic, 
community, academic & 
community) 
[-inf;0] 
Socio-economic 
status 
dem_ses 
edbx_get_demographics() 
Socioeconomic status (SES) index 
based on residence area of 
patient 
Nominal (from ‘1 - Lowest 
SES’ through ‘5 - Highest 
SES) 
[-inf;0] 
Smoking 
c_smoking_history 
edbx_get_demographics() 
History of current or former (= 
TRUE) or never (= FALSE) 
smoking on or anytime before 
index date; if there are multiple 
records per patient, any evidence 
of former/current smoking is 
prioritized 
Binary logical (TRUE, FALSE) 
[-inf;0] 
PR status 
c_pr_biomarker_status 
edbx_get_biomarker() 
Evidence of any PR mutation 
present. If patient has multiple 
measurements, any evidence of a 
mutation is prioritized. In case of 
ties, the closest measurement 
relative to index date is selected 
Binary (negative, positive) 
[-90;0] 
ECOG 
c_ecog 
edbx_get_ecog() 
ECOG performance status 
measured closest to index date 
within assessment window. In 
case of ties, the lower ECOG 
value is selected 
Nominal (0, 1, 2, 3, 4); 
modelled as ordinal numeric 
in ROPRO10; due to I/E 
criteria ECOG is modelled as 
a binary (0, 1) covariate 
[-90;0] 
Stage 
c_stage_initial_dx 
edbx_get_diagnosis_solid() 
AJCC summary group stage at 
initial diagnosis 
Ordinal numeric (from 0 to IV 
with sub-categories, e.g., 
IA1)10 
[-inf;0] at initial diagnosis 
of primary cancer 
De novo metastatic 
status 
c_de_novo_mets_dx 
edbx_get_diagnosis_solid() 
Evidence of presence of one or 
multiple metastases at/before 
initial diagnosis 
Binary logical (TRUE, FALSE) 
[-inf;0] at initial diagnosis 
of primary cancer 

17 
 
Characteristic 
Harmonized analysis 
variable name 
R function to derive 
covariate (see pdf in 
appendix) 
Details1 
Variable encoding 
Assessment window 
Evidence of 
metastases between 
initial diagnosis and 
index date 
c_met_pre_index 
edbx_get_diagnosis_solid() 
Evidence of any metastasis 
between initial diagnosis and 
index date (includes the initial 
diagnosis and index date) 
Binary logical (TRUE, FALSE) 
[-initial dx;0] 
Number of metastatic 
sites 
c_number_met_sites 
edbx_get_diagnosis_solid() 
Number of metastatic sites for a 
given patient before/on index 
date 
Integer 
[-inf;0] 
Time between initial 
diagnosis to index 
date 
c_time_dx_to_index 
edbx_get_diagnosis_solid() 
Time in days between initial 
diagnosis to index date 
Continuous 
[-initial dx;0] 
Time between earliest 
evidence of a 
metastatic and 
index date 
c_time_met_dx_to_index 
edbx_get_diagnosis_solid() 
Time in days between earliest 
evidence of a metastatic and 
index date 
Continuous 
[-inf;0] 
Histology 
(adenocarcinoma) 
c_histology_match 
edbx_get_histology() 
Evidence of adenocarcinoma 
histology (non-squamous cell for 
EDB1) 
Binary logical (TRUE, FALSE) 
[-inf;0] 
Albumin 
c_albumin_g_l_cont 
edbx_get_labs() 
Closest albumin measurement (in 
serum/plasma) relative to index 
date in g/L. In case of ties, the 
lower is selected 
continuous 
[-90;0] 
Alkaline phosphatase 
(ALP) 2 
c_alp_u_l_cont 
edbx_get_labs() 
Closest alkaline phosphatase 
measurement (in serum/plasma) 
relative to index date in U/L. In 
case of ties, the lower is selected 
continuous 
[-90;0] 
Alanine 
aminotransferase 
(ALT) 2 
c_alt_u_l_cont 
edbx_get_labs() 
Closest alanine transaminase 
measurement (in serum/plasma) 
relative to index date in U/L. In 
case of ties, the lower is selected 
continuous 
[-90;0] 
Aspartate 
aminotransferase 
(AST) 
c_ast_u_l_cont 
edbx_get_labs() 
Closest aspartate 
aminotransferase measurement 
(in serum/plasma) relative to 
index date in U/L. In case of ties, 
the lower is selected. Used to 
compute AST-ALT ratio 
continuous 
[-90;0] 
AST/ALT ratio 
c_ast_alt_ratio_cont 
edbx_get_labs() 
AST/ALT ratio calculated from 
c_ast_u_l_cont/c_alt_u_l_cont 
continuous 
[-90;0] 
Bilirubin2 
c_bilirubin_mg_dl_cont 
edbx_get_labs() 
Closest bilirubin measurement (in 
serum/plasma) relative to index 
date in mg/dL. In case of ties, the 
lower is selected 
continuous 
[-90;0] 

18 
 
Characteristic 
Harmonized analysis 
variable name 
R function to derive 
covariate (see pdf in 
appendix) 
Details1 
Variable encoding 
Assessment window 
Calcium2 
c_calcium_mg_dl_cont 
edbx_get_labs() 
Closest calcium measurement (in 
serum/plasma) relative to index 
date in mg/dL. In case of ties, the 
lower is selected 
continuous 
[-90;0] 
Chloride 
c_chloride_mmol_l_cont 
edbx_get_labs() 
Closest chloride measurement (in 
serum/plasma) relative to index 
date in mmol/L. In case of ties, 
the lower is selected 
continuous 
[-90;0] 
Eosinophils/100 
leukocytes2 
c_eosinophils_leukocytes_r
atio_cont 
edbx_get_labs() 
Eosinophils/100 leukocytes in 
blood. In case of ties, the lower 
continuous 
[-90;0] 
Glucose2 
c_glucose_mg_dl_cont 
edbx_get_labs() 
Closest glucose measurement (in 
serum/plasma) relative to index 
date in mmol/L. In case of ties, 
the lower is selected 
continuous 
[-90;0] 
Granulocyte/leukocyt
e ratio 2 
c_granulocytes_leukocytes_
ratio 
edbx_get_labs() 
Closest granulocyte/leukocyte 
ratio measurement (in blood) 
relative to index date. In case of 
ties, the lower is selected. Used 
to compute 
granulocyte/lymphocyte ratio 
 
continuous 
[-90;0] 
Hemoglobin 
c_hemoglobin_g_dl_cont 
edbx_get_labs() 
Closest hemoglobin 
measurement (in blood) relative 
to index date in g/L. In case of 
ties, the lower is selected 
continuous 
[-90;0] 
Lactate 
dehydrogenase 
(LDH)3 
c_ldh_u_l_cont 
edbx_get_labs() 
Closest LDH measurement (in 
serum or plasma) relative to 
index date in U/L. In case of ties, 
the lower is selected 
continuous 
[-90;0] 
Lymphocytes 
c_lymphocyte_10_9_l_cont 
edbx_get_labs() 
Closest lymphocytes 
measurement (in blood) relative 
to index date in 109/L. In case of 
ties, the lower is selected. Used 
to compute 
neutrophil/lymphocyte ratio 
continuous 
[-90;0] 
Lymphocyte/leukocyt
e ratio2 
c_lymphocyte_leukocyte_ra
tio_cont 
edbx_get_labs() 
Closest lymphocyte/leukocyte 
ratio measurement (in blood) 
relative to index date. In case of 
ties, the lower is selected. Used 
to compute 
neutrophil/lymphocyte ratio 
continuous 
[-90;0] 

19 
 
Characteristic 
Harmonized analysis 
variable name 
R function to derive 
covariate (see pdf in 
appendix) 
Details1 
Variable encoding 
Assessment window 
Monocytes2 
c_monocytes_10_9_l_cont 
edbx_get_labs() 
Closest monocytes measurement 
(in blood) relative to index date in 
109/L. In case of ties, the lower is 
selected. 
continuous 
[-90;0] 
Neutrophils 
c_neutrophil_10_9_l_cont 
edbx_get_labs() 
Closest neutrophils measurement 
(in blood) relative to index date in 
109/L. In case of ties, the lower is 
selected. Used to compute 
neutrophil/lymphocyte (NLR) ratio 
continuous 
[-90;0] 
Neutrophil/lymphocyt
e ratio2 
c_neutrophil_lymphocyte_r
atio_cont 
edbx_get_labs() 
Neutrophil/lymphocyte (NLR) 
ratio calculated from 
c_neutrophil_10_9_l_cont/ 
c_lymphocyte_10_9_l_cont 
continuous 
[-90;0] 
Platelets 
c_platelets_10_9_l_cont 
edbx_get_labs() 
Closest platelets measurement 
(in blood) relative to index date in 
109/L. In case of ties, the lower is 
selected 
continuous 
[-90;0] 
Protein 
c_protein_g_l_cont 
edbx_get_labs() 
Closest protein measurement (in 
serum/plasma) relative to index 
date in g/L. In case of ties, the 
lower is selected 
continuous 
[-90;0] 
Urea nitrogen2 
c_urea_nitrogen_mg_dl_co
nt 
edbx_get_labs() 
Closest urea nitrogen 
measurement (in serum/plasma) 
relative to index date in mg/L. In 
case of ties, the lower is selected 
continuous 
[-90;0] 
Systolic blood 
pressure2 
c_sbp_cont 
edbx_get_vitals() 
Closest systolic blood pressure (in 
mmHg) measurement. In case of 
ties, the lower is selected 
continuous 
[-90;0] 
Body mass index 
(BMI)2 
c_bmi_cont 
edbx_get_vitals() 
Closest BMI measurement (in 
kg/m2) relative to index date. In 
case of ties, the lower is selected. 
continuous 
[-90;0] 
Heart rate2 
c_hr_cont 
edbx_get_vitals() 
Closest heart rate measurement 
(in bpm) relative to index date. In 
case of ties, the lower is selected 
continuous 
[-90;0] 
Oxygen saturation 
c_oxygen_cont 
edbx_get_vitals() 
Closest heart rate measurement 
(in bpm) relative to index date. In 
case of ties, the lower is selected 
continuous 
[-90;0] 
1 x stands for the pseudonymized number of the respective database, i.e., EDB1, EDB3 or EDB4 
2 For calculation of ROPRO prognostic score10, this variable is log transformed. 
3 For calculation of ROPRO prognostic score10, this variable is log-log transformed. 

!+
6.7. Data analysis
6.7.1. Context and rationale for analysis plan
To emulate the PALOMA-2 trial, the following analytical workflow will be used (Figure 2). First, an analytical cohort with covariates on key eligibility criteria and 
prognostic factors will be derived across all databases. To ensure reproducibility and consistency throughout the entire ENCORE project, the internally developed 
encore.io R package streamlines this process using the functions referenced in Table 5. Operational definitions of key covariates used for trial emulation. Table 5
(code documentation see appendix).15 The analytical cohort will be derived by first identifying a metastatic breast cancer inception cohort of initiators of palbociclib 
plus letrozole or letrozole alone in the first-line setting as described in section 6.6.4. Next, key eligibility criteria will be applied in which patients with missing values 
are considered eligible in the respective attrition steps to allow thorough missing data investigations.
Figure 2. Illustration of principled analytical workflow.
Once a full analytic cohort is built, principled missing data investigations will be employed to empirically assess assumptions on potentially underlying missingness 
mechanisms according to Rubin’s classification of missing data (i.e., missing completely at random [MCAR], missing at random [MAR] and missing not at random 
[MNAR]).16 To that end, we will adopt a principled process on missing data that was developed as part of a FDA Sentinel Innovation Center causal inference 
workstream that empirically evaluates different aspects across partially observed covariates based on three group diagnostics (Table 6).17,18 In brief, the first group 
diagnostics computes distributions and absolute standardized mean differences (ASMD) between patients with and without an observed value for a given partially 
observed covariates. If missingness can be explained by observed covariates such as in MAR mechanisms, patient characteristics will significantly differ which will (in 
m
Derive Analytic Cohort
Describe 
Missingness
Multiple 
Imputation
If missing data 
evaluation results in 
conclusion that data 
can be imputed, create 
m imputed datasets 
(with m = ! % of 
observed missing 
values)
Imputed 
datasets
Dataset m1
Dataset m2
Dataset m3
Dataset m…
PS matching
m
Dataset m1
Dataset m2
Dataset m3
Dataset m…
MatchThem
HR m1
HR m2
HR m3
HR m…
Outcome 
Models
Pre-specified primary, 
secondary and 
sensitivity analyses 
within each imputed 
propensity score 
weighted dataset
Reporting
Rubin’s rule

21 
 
analogy to propensity scores) be indicated by ASMDs > 0.1. In addition, Hoteling’s19 and Little’s20 tests additionally provide formal hypothesis tests for such 
comparisons in which high test statistics and a rejection of the null hypothesis would provide evidence for differences in the distribution of patient characteristics and 
suggest the underlying mechanism is not MCAR or MNAR. Group 2 diagnostics assess the ability to predict missingness based on observed covariates by fitting a 
classification model to predict the missingness indicator of the partially observed covariate. To that end, we will fit a random forest (RF) classification model using 
observed covariates with a 70/30 train-test split of the complete cohort. A sufficiently high area under the receiver operating characteristic curve (AUC) metric of the 
test dataset may demonstrate that missingness can be predicted well and could point towards MAR as a likely mechanism as opposed to an AUC~0.5 which would 
suggest MCAR or MNAR. Group 3 diagnostics evaluates the association between the missingness indicator of the partially observed covariates and the outcome (OS). 
If the missingness of a confounder cannot be explained or approximated by observed covariates and a difference in the outcome is observed depending on the 
missingness indicator (e.g., HRmissingness indicator ≠ 1), this may be indicative of an underlying MNAR mechanism. These empirical diagnostics will be implemented through 
the smdi R package21 and be further enhanced by clinical expert knowledge. 
 
Table 6. Diagnostics to empirically differentiate and characterize missing data mechanisms. 
 
Group 1 Diagnostics 
Group 2 Diagnostics 
Group 3 Diagnostics 
Diagnostic metric 
Absolute standardized 
mean difference (ASMD) 
P-value  
Hoteling19 Little20  
Area under the receiver 
operating curve 
(AUC) 
Log HR 
(missingness indicator) 
Purpose 
Comparison of distributions between patients with 
vs. without observed value of the partially observed 
covariate. 
Assessing the ability to 
predict missingness based 
on observed covariates. 
Check whether 
missingness of a 
covariate is associated 
with the outcome 
(differential 
missingness). 
Example value 
ASMD = 0.1 
p-value < 0.001 
AUC = 0.5 
log HR = 0.1 (0.05 to 
0.2) 
Interpretation 
<0.1a: no imbalances in 
observed patient 
characteristics; 
missingness may be 
likely completely at 
random or not at random 
(~MCAR, ~MNAR). 
>0.1a: imbalances in 
observed patient 
characteristics; 
missingness may be 
likely at random (~MAR). 
High test statistics and 
low p-values indicate 
differences in baseline 
covariate distributions 
and null hypothesis 
would be rejected 
(~MAR). 
AUC values ~ 0.5 indicate 
completely random or not at 
random prediction (~MCAR, 
~MNAR). 
Values meaningfully above 
0.5 indicate stronger 
relationships between 
covariates and missingness 
(~MAR). 
 
No association in either 
univariate or adjusted 
model and no meaningful 
difference in the log HR 
after full adjustment 
(~MCAR). 
Association in univariate 
but not fully adjusted 
model (~MAR). 
Meaningful difference in 
the log HR also after full 
adjustment (~MNAR). 

22 
 
Abbreviations: ASMD = Median absolute standardized mean difference across all covariates, AUC = Area under the curve, CI = Confidence 
interval, MAR = Missing at random mechanism in which the missingness probability depends on observed covariates, MCAR = Missing 
completely at random mechanism in which each patients has the same missingness probability, MNAR(unmeasured) = Missing not at random 
mechanism in which the missingness can only be explained by a covariate which is not observed in the underlying dataset, MNAR(value) = 
Missing not at random mechanism in which the missingness just depends on the actual value of the partially observed confounder of interest 
itself. 
a Analogous to propensity score-based balance measures.22 
 
 
While the MAR assumption is a strong assumption to hold across all considered covariates, it was shown that especially in the context of partially observed covariate 
data (as opposed to missing exposure and outcome data), only mechanisms in which a covariate causes its own missingness leads to critical bias (MNAR).23,24 In such 
situations, multiple imputation can have significant advantages over a complete case analysis since additional information (auxiliary covariates and missing indicator 
variables) can be included in imputation algorithms which can make the MAR assumption holding more plausible and increase efficiency in treatment effect 
estimates since all patients and critical covariates can be retained and variances can be realistically estimated, considering both the general sampling error and the 
error introduced by missing data.25,26 Hence, multiple imputation with flexible, non-parametric random forest imputation algorithms27  (mice R package28) will be used 
for this trial emulation. The number of imputed datasets (m) will be determined for each database separately based on the average proportion missingness observed 
in the analytic cohort and results from the above-referenced missing data investigations will inform the choice of appropriate sensitivity analyses. 
 
 

23 
 
Figure 3. Covariate balance across imputed datasets (simulated example). 
 

24 
 
To estimate the treatment effects for palbociclib plus letrozole using propensity score matching across imputed datasets we will apply the “within” approach using the 
“MatchThem” R package.29,30 That is, propensity score matching and the estimation of the treatment effect are performed in each imputed dataset separately and 
resulting treatment effect estimates are combined using Rubin’s rule. In this study, this will be implemented by matching eligible patients on their propensity to 
initiate palbociclib plus letrozole using a 1:1 nearest neighbor matching algorithm without replacement and a caliper of 1% of the standard deviation of the propensity 
score. The resulting covariates balance will be assessed by computing and visualizing ASMDs before and after matching across datasets. As compared to a single 
dataset matching approach, this can lead to a range of ASMDs per covariate due to random variation across imputed datasets for which an example (using simulated 
data) is illustrated in Figure 3. If sufficient balance can be established, a Cox proportional hazards regression model will be fit to estimate the marginal average 
treatment effect in the matched population. Since in most databases there are more palbociclib-plus-letrozole patients than patients in the control arm, the estimand 
will rather correspond to the ATC than the ATT. Confidence intervals will be estimated using cluster-robust standard errors.31 As a secondary endpoint, we will 
additionally estimate the median OS survival time difference between the two exposure groups using the Kaplan-Meier method. It should be noted that due to 
administrative and de-identification purposes, the date of death is often only available at the month- or year-granularity level, in which case the date of death will be 
imputed to the 15th of a month or July 2nd of a year, respectively (depending on the database). In rare cases, this can lead to negative/implausible follow-up times if 
the date of death is very close to the index date. These patients will be excluded from the analysis. 
The final hazard ratio and median OS survival time difference estimates for each database will then be combined using Rubin’s rule.28,32 To arrive at a single estimate 
across databases, the final estimates will be summarized through a meta-analytic fixed effects model.33 A summary of the analytic approach is summarized in Table 7 
and an example workflow with simulated data and annotated code can be found here. 
 
 

25 
 
 
A. Primary analysis 
Table 7. Primary, secondary, and subgroup analysis specification 
Hypothesis: Initiation of palbociclib plus letrozole decreases the hazard of all-cause mortality as compared to initiation of letrozole 
alone 
Exposure contrast: Initiation of palbociclib plus letrozole vs letrozole alone 
Primary outcome: Time to all-cause mortality (OS)  
Secondary outcome: Time-to-next-treatment (TTNT) defined as a composite outcome of all-cause mortality or adding or switching to a second-
line treatment.34,35 
Databases used: EDB1, EDB3, EDB4 
Time period: From 2015 (approval year for palbociclib) - 2022 (publication of OS outcome for competitor ribociclib)36 
Analytic software:  R 4.3.2. Version control of code and R packages will be established through git and Posit package manager, respectively. 
All packages are frozen to their most recent version as of April 24, 2024. 
Model(s): 
(provide details or code)  
See example code here. The annotated code for the trial emulation will be hosted at https://drugepi.gitlab-
pages.partners.org/encore/paloma2-nct-01740427/ (access only through MGB network for authorized personnel) 
Confounding adjustment method   
Name method and provide relevant details, e.g. bivariate, multivariable, propensity score matching (specify matching algorithm 
ratio and caliper), propensity score weighting (specify weight formula, trimming, truncation), propensity score stratification 
(specify strata definition), other. 
1:1 propensity score nearest neighbor matching without replacement and a caliper of 1% of propensity score standard 
deviation 
Missing data methods   Name method and provide relevant details, e.g. missing indicators, complete case, last value carried forward, multiple 
imputation (specify model/variables), other. 
   Multiple imputation by chained equations using a random forest imputation model across all covariate types. The number 
of imputed datasets will be determined by the average proportion of missing values across all partially observed 
covariates. Imputation models will include all variables of the substantiative model, i.e., exposure, outcome, 
confounders/prognostic factors and additional auxiliary covariates. 
Subgroup Analyses 
List all subgroups 
 In subgroup analysis, multiple imputation, propensity score matching and balance assessment will be conducted within 
each subgroup separately. The treatment effect will be estimated for each stratum separately (stratum-specific effects).  
1. Age (<65, ≥65) 
2. Region (North America, Europe, Asia/Pacific) 
3. ECOG (0, 1) 
4. Disease-free interval (De-novo metastatic, ≤12 months, >12 months) 
According to Slamon et al,6 the subgroup analyses are considered exploratory in nature and performed with no formal 
statistical testing. 
 
 

26 
 
 
Table 8. Sensitivity 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 #1 
Caliper matching on ROPRO prognostic 
score instead of propensity score 
Matching patients on validated 
prognostic score may be more 
beneficial to control for 
(unmeasured) confounding 
Matches patients on 
validated prognostic score 
that incorporates weights of 
key prognostic factors 
Limited experience on how to 
optimally use prognostic scores 
and should be seen as an 
experimental sensitivity analysis 
Sensitivity #2 
ATO weighting instead of matching 
Weights that resemble the 
average treatment effect in the 
overlap population (ATO) create 
a clinical equipoise population 
which is comparable to an RCT 
ATO weighting usually results 
in excellent balance and 
clinical equipoise 
Estimates the average 
treatment effect among the 
overlap patients which may not 
be comparable to target 
population anymore 
Sensitivity #3 
SMR/ATT weighting instead of matching. 
Here symmetric trimming (i.e., setting all 
weights lower/higher than that at a given 
quantile to the weight at the quantile) of 
extreme weights may be considered with 
the quantiles chosen based on weight 
distribution and resulting balancing 
performance. 
SMR weighting retains all 
patients and resembles the 
same estimand as matching 
ATT weighting retains all 
patients 
Patients with extreme weights 
after trimming may bias the 
analysis 
Sensitivity #4 
Censoring date is changed to 3 months 
before data cut-off date 
For all databases, information 
on mortality comes from 
different data sources which 
are updated asynchronously. 
To account for the potential lag 
of updated mortality 
information (ghost-time bias37), 
the censoring for patients 
without mortality event in the 
whole patient identification 
period will be moved to last 
sign of patients being 
alive/visit or 3 months before 
data cut-off date, whichever 
occurred earlier.38 
Approach implements a more 
conservative censoring rule 
Approach addresses ghost-time 
bias by censoring patients 
without a recorded death event 
earlier  
Sensitivity #5 
Delta imputation models for MNAR 
(tipping point analysis)  
Primary multiple imputation 
analysis assumes MAR which 
Estimates impact of 
deviations from MAR 
assumption on final 
Delta parameters must be 
assumed and results are 
complex to interpret in 

27 
 
may not hold for every 
covariate 
treatment effect estimates 
for key covariates 
multivariate missingness 
settings; just most important 
covariates or those with highest 
suspicion of being MNAR will be 
evaluated 
Sensitivity #6 
Re-weighting of strong risk factors 
and/or treatment effect modifiers 
distribution to match that of PALOMA-2 
In the presence of effect 
modification, treatment effect 
estimates may be different if 
the distribution of strong risk 
factors/effect modifiers is 
different in the emulated 
cohort versus the trial cohort 
Re-weighting adjusts for 
differences in distributions of 
key risk factors and/or 
treatment effect modifiers 
(see subgroup analysis in 
Table 7) 
Re-weighting risk 
factors/potential effect 
modifiers to match the PALOMA-
2 trial and simultaneously 
balancing them across 
treatment groups may be 
challenging due to differences 
in measurement 
Sensitivity #7 
Including patients who have had at least 
1 visit 90 days prior to treatment 
initiation 
EHR are often lacking data 
continuity, and this analysis 
uses the requirement of 1 visit 
as a proxy for continuous 
observation periods 
Considers aspect of data 
continuity 
There may be patients who are 
put on treatment immediately in 
which case they are falsely 
excluded 
Sensitivity #8 
Using all available calendar time 
This analysis explores potential 
confounding related to 
calendar time, evolving clinical 
practice patterns, and access 
to therapies. 
Aims to assess the extent of 
confounding introduced by 
changes in time, treatment 
practices, and access to 
therapies. 
We expect the results to be 
more confounded due to 
calendar time. 
Sensitivity #9 
Missingness is handled by restricting to 
patients with complete observations on a 
subset of the most important 
confounders (“complete cases”). 
Instead of imputing data, this 
sensitivity analysis restricts the 
analysis cohort to patients with 
complete observations on key 
confounders 
Data will not be imputed and 
missingness is assumed to 
be missing completely at 
random 
The restriction to complete 
cases will significantly decrease 
sample size. To limit the 
attrition of patients with partially 
observed covariates, it won’t be 
possible to use all covariates 
used in the main analysis 
propensity score model, but only 
consider key covariates with 
overall low proportions of 
missingness (age, sex, etc.) 
Sensitivity #10 
Exclusion of patients with >1 year 
between metastasis diagnosis and index 
date 
This sensitivity analysis varies 
the inclusion criteria by 
excluding patients whose index 
date (treatment start) is >1 
year after their metastatic 
diagnosis date which is 
Helps ensure that treatment 
reflects initial first-line 
treatment of metastatic 
disease, not late-line therapy. 
Exclusion of further patients 
results in a smaller cohort size 
which may reduce statistical 
power. 
 

28 
 
clinically unrealistic or 
implausible for a first-line 
treatment 
 
6.8.  Study size and feasibility 
See appendix 4 (first feasibility matching on age, sex, comorbidity score) and appendix 5 (second feasibility matching on all predefined propensity score variables). 
Propensity score distribution plots with c-statistics are available in appendix 6. Table 1 showing prevalence of baseline characteristics and standardized differences in 
distribution are available in appendix 7. 
7.  Limitation of the methods 
• 
Missingness in prognostic factors is a major challenge which is addressed in this emulation by multiple imputation using a non-parametric imputation 
algorithm. Multiple imputation usually assumes that missingness can be explained by observed characteristics, which may be empirically evaluated using 
principled missingness diagnostics, but the true underlying missingness mechanisms are usually unknown. Nevertheless, multiple imputation makes use of 
additional information (auxiliary covariates) which can render the underlying missingness assumptions more plausible. In addition, assumptions for alternative 
missing data approaches like complete case analysis or the “missing indicator approach” come with even stronger assumptions and additionally have the 
limitation of significantly reduced sample sizes, especially when comprehensively adjusting for known confounders and prognostic factors. 
• 
Data continuity is a major challenge in EHR databases since “guaranteed” observable periods (such as continuous enrolment periods in administrative claims 
data) do not exist which may lead to measurement error in key covariates and exposure misclassification. Sensitivity analysis #7 tries to address this requiring 
patients to have had at least one visit before the index date which increases the likelihood that a patients was not only diagnosed at the respective center but 
is also regularly seen. 
• 
Balancing patients on calendar year is not possible since calendar year shows instrumental variable-like behaviours (see Figure 12), i.e., it perfectly predicts 
treatment assignment and does not have any association with the outcome other than through the exposure. This assumption is formally untestable but 
clinically reasonable since there has not been any other significant change in treatment paradigms for PALOMA-2--like postmenopausal, ER-positive, HER2-
negative metastatic breast cancer populations other than introduction of palbociclib plus letrozole. The improvements of radiation of brain metastases may be 
the only exception, but it is expected that this may be of negligible significance for the scope of this emulation. 
8.  Protection of human subjects 
This study has been approved by the Brigham and Women’s Hospital Institutional Review Board. 
 

29 
 
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31 
 
10.  Appendices 
10.1.  CONSORT diagrams 
The following CONSORT attrition diagrams depict the process to select eligible PALOMA-2-like populations in EDB1, EDB3 and EDB4 for the main analysis, 
respectively. 
Figure 4. CONSORT attrition to select eligible PALOMA-2-like populations in EDB1. 
 

32 
 
Figure 5. CONSORT attrition to select eligible PALOMA-2-like populations in EDB3. 
 
 
 

33 
 
Figure 6. CONSORT attrition to select eligible PALOMA-2-like populations in EDB4. 
 
 

34 
 
10.2.  Covariate balance figures 
The following figures illustrate the balance of key covariates included in propensity score models among eligible PALOMA-2-like populations in EDB1, EDB3 and 
EDB4, respectively. 
Figure 7. EDB1 covariate balance of covariates included in propensity score model before and after matching. 
 
 

35 
 
Figure 8. EDB3 covariate balance of covariates included in propensity score model before and after matching. 
 
 

36 
 
Figure 9. EDB4 covariate balance of covariates included in propensity score model before and after matching. 
 

37 
 
10.3.  Sample size/power calculations 
Power estimations are computed based on the average number of observed events across imputed and matched datasets according to the methodology described by 
Schoenfeld.39 The OS result was null in the trial, therefore, we are not expecting to observe a strong effect in the emulation. Because our sample sizes are larger than 
the number of trial participants (2887 in EDB1, 1281 in EDB3, and 1929 in EDB4 vs 666 in the trial), we expect our confidence intervals to be narrower. 
Figure 10a. Number of events needed to achieve x% power for overall-survival outcome 
 
 
 

38 
 
Figure 11b. Number of events needed to achieve x% power for progression-free-survival / time-to-next-treatment outcome 
 
 
10.4.  Additional Figures and Tables 
Table 9. Lab measurement plausibility thresholds. 
Lab and standardized unit 
Lower plausibility threshold 
Upper plausibility threshold 

39 
 
c_albumin_g_l 
10 
200 
c_alp_u_l 
1 
2000 
c_alt_u_l 
1 
90000 
c_ast_u_l 
1 
90000 
c_bilirubin_mg_dl 
0.1 
80 
c_calcium_mg_dl 
0.1 
20 
c_chloride_mmol_l 
0.1 
200 
c_eosinophils_leukocytes_ratio 
0 
100 
c_glucose_mg_dl 
0.1 
2000 
c_granulocytes_leukocytes_ratio 
0 
100 
c_hemoglobin_g_dl 
0.1 
20 
c_ldh_u_l 
0.1 
Inf 
c_lymphocyte_10_9_l 
0 
1e+06 
c_lymphocyte_leukocyte_ratio 
0 
100 
c_monocytes_10_9_l 
0 
1e+06 
c_neutrophil_10_9_l 
0 
1e+06 
c_platelets_10_9_l 
0 
5000 
c_protein_g_l 
1 
300 
c_urea_nitrogen_mg_dl 
0.1 
250 
 
 
 

40 
 
Table 10. Vital sign measurement plausibility thresholds. 
Vital sign 
Lower plausibility threshold 
Upper plausibility threshold 
c_sbp 
50 
250 
c_dbp 
30 
150 
c_bmi 
10 
80 
c_bsa 
0.5 
3.5 
c_height 
0.5 
3 
c_oxygen 
50 
100 
c_pain 
0 
10 
c_hr 
20 
250 
c_resp 
5 
50 
c_temp 
86 
113 
c_weight 
20 
300 
 
 
 

41 
 
Table 11. Mapping from State to Region. 
State 
Region 
CT 
Northeast 
ME 
Northeast 
MA 
Northeast 
NH 
Northeast 
RI 
Northeast 
VT 
Northeast 
DE 
Northeast 
NJ 
Northeast 
NY 
Northeast 
PA 
Northeast 
IL 
Midwest 
IN 
Midwest 
MI 
Midwest 
OH 
Midwest 
WI 
Midwest 
IA 
Midwest 
KS 
Midwest 
MN 
Midwest 
MO 
Midwest 
NE 
Midwest 
ND 
Midwest 

42 
 
SD 
Midwest 
FL 
South 
GA 
South 
MD 
South 
NC 
South 
SC 
South 
VA 
South 
DC 
South 
WV 
South 
AL 
South 
KY 
South 
MS 
South 
TN 
South 
AR 
South 
LA 
South 
OK 
South 
TX 
South 
AZ 
West 
CO 
West 
ID 
West 
MT 
West 
NV 
West 
NM 
West 

43 
 
UT 
West 
WY 
West 
AK 
West 
CA 
West 
HI 
West 
OR 
West 
WA 
West 
 
 
 

44 
 
Figure 12. Treatment initiation trends by calendar year and treatment in EDB1. 
 

45 
 
Figure 13. Treatment initiation trends by calendar year and treatment in EDB3. 
 

46 
 
Figure 14. Treatment initiation trends by calendar year and treatment in EDB4.