CARE Initiative Study: Real-world Emulation of the PALOMA-2 Comparative Effectiveness Trial of Palbociclib and Letrozole vs. Placebo and Letrozole for the First-line Treatment of ER+/HER2- Advanced Breast Cancer
Conditions: Metastatic Breast Cancer
Sponsor: Aetion, Inc.

1. Title Page
Title
CARE Initiative Study: Real-world emulation of the PALOMA-2 comparative effectiveness trial
of palbociclib and letrozole vs. placebo and letrozole for the first-line treatment of ER+/HER2-
advanced breast cancer
Research Question and Objectives
This study seeks to advance understanding of under what conditions real-world evidence
studies can provide reliable conclusions about drug effectiveness. The objective is to
emulate the PALOMA-2 randomized controlled trial of palbociclib and letrozole as first-line
treatment in postmenopausal adult female patients with ER+/HER2- advanced breast
cancer using real-world electronic health record data.
Protocol version
v1.1
Last update date
August 13, 2024
Contributors
Paige Sheridan, PhD
Aetion, Inc
5 Penn Plaza
7
th Floor
New York, NY 10001
Natalie Levy, PhD
Aetion, Inc
5 Penn Plaza
7
th Floor
New York, NY 10001
Nileesa Gautam, MSc
Aetion, Inc
50 Congress St.
Suite 1025
Boston, MA 02109
Adina Estrin, MS

Aetion, Inc
5 Penn Plaza
7
th Floor
New York, NY 10001
Monica Iyer
Aetion, Inc
5 Penn Plaza
7
th Floor
New York, NY 10001
Sarah McDonald
Aetion, Inc
5 Penn Plaza
7
th Floor
New York, NY 10001
Ann Madsen, PhD
Aetion, Inc
5 Penn Plaza
7
th Floor
New York, NY 10001
Ulka Campbell, PhD
Aetion, Inc
5 Penn Plaza
7
th Floor
New York, NY 10001
Sponsor
This work was collectively sponsored by Abbvie, Amgen, AstraZeneca, Bayer, Gilead, Janssen,
and Pfizer.
Conflict of interest
PS, NL, NG, AE, MI, SM, AM, and UC are shareholders and employees of Aetion, Inc.
2

Table of contents
1. Title Page
1
2. Abstract
5
3. Amendments and updates
5
4. Milestones
6
5. Rationale and background
6
6. Research question and objectives
7
7. Research methods
8
7.1 Study design
8
7.2 Study design diagram
9
Figure 1. PALOMA-2 randomized controlled trial study design
10
Figure 2. Real-world emulation study design
11
7.3 Setting
11
7.3.1 Context and rationale for definition of Time 0 (and other primary time anchors) for entry to the study population
11
7.3.2 Context and rationale for study inclusion criteria
12
7.3.3 Context and rationale for study exclusion criteria
16
7.4 Variables
23
7.4.1 Context and rationale for exposures of interest
23
7.4.2 Context and rationale for outcome of interest
23
7.4.3 Context and rationale for follow-up
24
7.4.4 Context and rationale for covariates (confounding variables and effect modifiers, e.g., risk factors, comorbidities, comedications) 25
7.5 Data analysis
28
7.5.1 Context and rationale for analysis plan
28
7.5.2 Context and rationale for sensitivity analyses
31
7.6 Data source(s)
34
7.6.1 Context and rationale for data source(s)
34
7.7 Data management
35
7.8 Quality control
36
7.9 Study size and feasibility
37
3

8. Strengths and Limitations
38
9. Protection of Human Subjects
39
10. Reporting of Adverse Events
39
11. References
39
12. Appendices
43
Appendix A: List of abbreviations
43
Appendix B: Preliminary feasibility assessment
45
Appendix C: Code lists
51
4

2. Abstract
Background
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 (RWD) in clinical and regulatory decision-making.
1 Using randomized controlled trials
(RCT) as a benchmark for causal effect estimates, a series of RCT emulations will be conducted across varying trials, real world data sources,
and study design elements to better understand under what conditions non-interventional studies, using data generated during routine clinical
care, can provide reliable conclusions about drug effectiveness.
Research Question and Objectives
In this study, real-world electronic health record (EHR) data will be used to emulate the Palbociclib: Ongoing Trials in the Management of Breast
Cancer (PALOMA-2) efficacy trial of palbociclib as first-line therapy in patients with estrogen receptor-positive (ER+)/human epidermal growth
factor
receptor
2-negative
(HER2-) advanced breast cancer.
2 Similarly to the PALOMA-2 trial, this study will compare real-world
progression-free survival (rwPFS) between patients who initiate palbociclib and letrozole and those treated with letrozole alone.
Research Methods
The inclusion and exclusion criteria applied in the PALOMA-2 trial will be operationalized in a real-world EHR data source, as closely as is feasible,
to create an observational cohort similar to the trial study population. Patients who initiate first-line treatment with palbociclib and letrozole
within 15 days of one another in the metastatic setting will be considered exposed. Patients who initiate letrozole only with no evidence of
palbociclib within 15 days of letrozole initiation will comprise the comparator group. Inverse probability of treatment weighting (IPTW) will be
used to control for measured hypothesized confounders. Kaplan-Meier methods and Cox proportional hazards models will be used to compare
median rwPFS and hazards of progression and death between the exposure and comparator groups.
3. Amendments and updates
Version date
Version number
Section of protocol
Amendment or update
Reason
August 13, 2024
1.1
Tables 4, 5, 9
Amendments to some variable
assessment windows
To improve emulation of trial
criteria and measures
5

4. Milestones
Table 1. Milestones
Milestone
Date
Initial feasibility assessment
March 21, 2024
Additional data explorations
April 8, 2024
Draft 1 of protocol complete
May 30, 2024
Final protocol shared with steering committee
June 28, 2024
Amended protocol shared with steering committee
August 13, 2024
5. Rationale and background
The potential of non-interventional studies using real-world data (RWD) — healthcare data generated during routine clinical practice — to
produce evidence about the effectiveness and safety of biomedical products is increasingly recognized by clinical and regulatory decision
makers.
3,4 This is reflected by the growing use of RWD to support regulatory approvals.
5 Real-world evidence (RWE) studies complement
randomized controlled trials (RCTs) by generating new hypotheses, producing results more quickly and at a lower cost, including broader
patient populations, reflecting clinical care patterns, and assessing longer-term outcomes.
1,6 These advantages of RWE studies are of particular
value in the field of oncology due to high unmet medical need, poor patient outcomes for several cancer types, a rapidly evolving treatment
landscape, and the need to generate additional confirmatory evidence following accelerated regulatory approval.
7
At the same time, causal inference from non-randomized studies leveraging RWD may be hindered by threats to internal validity. Due to a lack
of randomization, RWE studies may suffer from unmeasured or inadequately controlled confounding. Key variables may be missing or
misclassified in data generated from clinical practice, which may introduce information bias and limit direct comparisons with clinical trials.
Therefore, successful application of RWD to support clinical and regulatory decision-making requires a thorough understanding of the
circumstances under which RWD can generate valid evidence about treatment effectiveness.
The Coalition to Advance Real-World Evidence through Randomized Controlled Trial Emulation (CARE) Initiative aims to contribute to this
understanding by building an empirical evidence base for the generation of RWD-based evidence of treatment effectiveness.
1 To do this,
electronic health record (EHR) data collected during routine healthcare practice will be used to emulate the primary outcomes of completed
RCTs for oncology therapies. The RCT results will provide a benchmark causal effect estimate against which the findings of non-randomized
emulations can be compared. No standard metric has been proposed to quantify agreement between emulation and RCT results and ​previous
6

work has used a variety of measures.
8 The CARE emulations will focus on qualitative agreement — whether findings from a non-interventional
study and RCT are in the same direction and are of similar magnitude. The choice to use a metric that is not anchored to statistical significance
reflects conclusions from the CARE pilot study about specific challenges in oncology emulations (e.g., small real-world sample sizes) and the
non-inferential goals of this work.
9,10 Through this effort, the CARE Initiative seeks to identify under what conditions non-interventional studies
using data generated during routine clinical care can provide reliable conclusions about drug effectiveness.
6. Research question and objectives
In this emulation of the Palbociclib: Ongoing Trials in the Management of Breast Cancer (PALOMA-2) trial, real-world EHR data will be used to
estimate the effectiveness of initiating first-line treatment with palbociclib and letrozole versus letrozole alone among patients with estrogen
receptor-positive (ER+)/human epidermal growth factor receptor 2-negative (HER2-) advanced breast cancer.
2
Table 2. Primary research question and objective
Objective:
The objective of this non-interventional study is to estimate the effectiveness of initiating first-line
palbociclib and letrozole versus letrozole alone in a real-world emulation of the PALOMA-2 RCT.
Hypothesis:
Patients with ER+/HER2- metastatic breast cancer treated with palbociclib and letrozole will have
improved real-world progression-free survival (rwPFS) compared with patients treated with letrozole
alone.
Population:
Adult (≥18 years of age), postmenopausal female patients with ER+/HER2- metastatic breast cancer
Exposure:
Palbociclib and letrozole as first-line treatment for metastatic breast cancer
Comparator:
Letrozole as first-line treatment for metastatic breast cancer
Outcome:
rwPFS, defined as time from study treatment initiation to disease progression or death
Setting:
Clinical data sourced from oncology practices in the United States (U.S.)
Main measure of effect:
Hazard ratio for rwPFS in the intent-to-treat population
7

7. Research methods
7.1 Study design
7.1.1 Overview of key design elements of the PALOMA-2 trial
Study design: The PALOMA-2 trial (NCT01740427)
11 was an international, randomized, double-blind, placebo-controlled Phase III clinical trial
comparing the efficacy and safety of palbociclib and letrozole versus placebo and letrozole for the first-line treatment of ER+/HER2- advanced
breast cancer. Figure 1 displays the study design diagram for the PALOMA-2 trial.
Population: The trial study population included postmenopausal females, 18 years of age or older with histologically or cytologically confirmed
ER+/HER2- adenocarcinoma of the breast and evidence of locoregionally recurrent or metastatic disease. Eligible patients had received no prior
systemic therapy for advanced disease; did not have disease recurrence during or within 12 months of neoadjuvant or adjuvant treatment with
a non-steroidal aromatase inhibitor; had adequate organ function; had an Eastern Cooperative Oncology Group (ECOG) performance status of
0 to 2; and had measurable disease according to the Response Evaluation Criteria in Solid Tumours (RECIST) v.1.1.
12 Patients with visceral
metastasis who were at risk of short-term, life-threatening complications were excluded. Full inclusion and exclusion criteria are listed in Table 4
and Table 5.
Endpoints: The primary trial endpoint was progression-free survival (PFS), defined as the time from the date of randomization to the date of the
first documentation of objective progression of disease as evaluated by study investigators according to RECIST v.1.1, or death due to any cause.
Follow-up for PFS continued until the first of: documented disease progression; death; initiation of a new anti-cancer treatment; discontinuation
from overall study participation due to symptomatic deterioration, unacceptable toxicity, withdrawal of consent; or loss to follow-up.
Comparator patients were not able to initiate palbociclib.
Analysis: Efficacy was assessed in the intent-to-treat population defined by randomized treatment assignment. Median PFS and corresponding
95% confidence intervals were estimated after a maximum follow-up of 33 months using the Kaplan-Meier method and compared by
treatment status using a log-rank test. The hazard ratio for progression was calculated using a Cox proportional-hazards model.
7.1.2 Overview of key design elements of the real-world emulation study
Study design: This new user, non-randomized active comparator cohort study will compare rwPFS between patients with records indicating
initiation of palbociclib and letrozole versus letrozole alone following qualifying metastatic disease diagnosis in the EHR data source. The data
originate from oncology practices in the U.S. and include both structured and curated data elements abstracted from unstructured sources
including provider notes, pathology, and imaging reports (Section 7.6.1).
Population: The study population will include postmenopausal females, 18 years of age or older with a diagnosis of ER+/HER2- metastatic
adenocarcinoma of the breast recorded in the EHR. Patients will be selected to reflect the PALOMA-2 trial eligibility criteria, as feasible in the RWD
source, to create a trial-similar population. Eligible patients will include those with no record of previously receiving systemic anti-cancer
therapy for advanced disease; without neoadjuvant or adjuvant treatment with a non-steroidal aromatase inhibitor in the 12 months prior to
8

metastatic diagnosis; without evidence of inadequate organ function; and without evidence of an ECOG performance status >2. Real-world
operationalization of all trial inclusion and exclusion criteria are listed in Table 4 and Table 5. Study exposure groups will be ascertained within a
15-day time window (‘exposure ascertainment window’), beginning on the day of the first record of a study drug. Patients initiating treatment for
metastatic disease with palbociclib and letrozole within the exposure ascertainment window will be classified as exposed. Patients initiating
treatment with letrozole and with no evidence of palbociclib within the exposure ascertainment window will be classified as comparator
patients.
Endpoints: The study endpoint, rwPFS, will be based on curated progression information and date of death available in the data source. Patients
will be followed until the first of: progression or death; initiation of a new anti-cancer therapy; the administrative end of the study period (Day
1004, or 33 months, to conform to the maximum follow-up time at which PFS was evaluated in the PALOMA-2 trial); the end of data; or loss to
follow-up (the last date prior to a period of >90 days without curated EHR activity and without death).
Analysis: Inverse probability of treatment weighting (IPTW) will be used to adjust for measured baseline confounders. Median rwPFS and
corresponding 95% confidence intervals will be estimated using the Kaplan-Meier method and compared between exposure groups using a
log-rank test. The hazard ratio for progression will be calculated using a Cox proportional-hazards model.
Rationale for study design choice: The choice of study design, population, endpoint, and analysis plan are intended to emulate the PALOMA-2
trial design as closely as possible, including creating a trial-similar real-world population, controlling for confounding in the absence of
randomization, and estimating the intent-to-treat effect as was done in the trial.
7.2 Study design diagram
The figures below display the study designs for the PALOMA-2 RCT (Figure 1) and this real-world emulation study (Figure 2). For simplicity,
inclusion and exclusion criteria displayed correspond to those highlighted in the trial publication.
2 Full trial eligibility criteria are listed in Sections
7.3.2 and 7.3.3.
9

Figure 1. PALOMA-2 randomized controlled trial study design
10

Figure 2. Real-world emulation study design
7.3 Setting
7.3.1 Context and rationale for definition of Time 0 (and other primary time anchors) for entry to the study population
Figure 2 displays the study design diagram for the primary analysis. Candidate exposed and comparator patients will be identified as those
initiating treatment for metastatic disease with palbociclib and/or letrozole. Study exposure group will be ascertained within a 15-day time
11

window (i.e., 'exposure ascertainment window'), beginning on the day of the first record of a study drug (palbociclib or letrozole) (Day -15), and
ending 15 days later on Day -1. Patients experiencing an outcome or censoring event during this window will be excluded. Additional information
on the exposure ascertainment window is provided in Section 7.4.1. Follow-up for progression and death will begin on Day 0 ('Time 0'). To avoid
immortal time bias, Time 0 will occur after the exposure ascertainment window for all patients, irrespective of exposure status. The operational
definition of Time 0 is provided in Table 3. The date of first study drug initiation will be restricted to dates from February 1, 2010 to June 20, 2023 to
reflect current treatment paradigms at the time of the PALOMA-2 trial, and to allow a minimum available follow-up time of one month prior to
the end of data for all patients, respectively. It should be noted that the first line of metastatic treatment can be initiated up to 30 days before
the documented date of metastatic diagnosis per the data vendor’s line of therapy definitions.
Table 3. Operational definition of Time 0
Study population name(s)
Time Anchor Description
Type of
entry
Washout window
Incident with respect to…
Patients initiating first-line
treatment for metastatic
ER+/HER2- breast cancer
Time 0 will be defined as the day after the end
of the 15-day exposure ascertainment
window. Exposure ascertainment is described
in the variables section of this protocol.
Incident
[Metastatic diagnosis date,
Day -16]
Metastatic diagnosis
7.3.2 Context and rationale for study inclusion criteria
Operational definitions for the study inclusion criteria are presented in Table 4. These inclusion criteria correspond to those applied in the
PALOMA-2 RCT.
As indicated below, PALOMA-2 inclusion criteria that are not relevant in a real-world clinical setting (e.g., willingness and ability to provide tumor
tissues) or are not captured in routine oncology care (e.g., measurable disease) will not be applied.
Table 4. Operational Definitions of Inclusion Criteria
Trial Criterion
Real-world operationalization
a
Assessment
window
Rationale for real-world
operationalization
(where applicable)
Validity concerns and how they
will be addressed
(where applicable)
Adult women (≥18 years of
age)
Recorded female sex and age at
study drug initiation ≥18 years.
[Day -15]
N/A
N/A
12

Trial Criterion
Real-world operationalization
a
Assessment
window
Rationale for real-world
operationalization
(where applicable)
Validity concerns and how they
will be addressed
(where applicable)
Proven diagnosis of
adenocarcinoma of the
breast
Breast cancer diagnosis.
Histology not indicative of
non-adenocarcinoma
histologies.
[Start of data,
Day -15]
Specific histology results cannot
be identified for all patients in the
dataset. As a result, patients with
non-adenocarcinoma histology
will be excluded.
Patients with non-specific
histology results who do not have
adenocarcinoma and who would
have been ineligible for the trial
may be included. This may affect
comparability between the study
and trial populations. The specific
histology values for all included
patients will be reported
descriptively.
Evidence of locoregionally
recurrent or metastatic
disease not amenable to
resection or radiation
therapy with curative intent
and for whom
chemotherapy is not
clinically indicated
Staging information indicative of
metastatic disease.
[Initial
diagnosis date,
Day -15]
Locoregionally recurrent disease
cannot be identified in the
dataset.
Patients initiating study treatment
will be assumed to be ineligible for
curative therapy or
chemotherapy.
Patients with advanced disease
who would have been eligible for
the trial may be excluded. While
patients with advanced disease
constituted <3% of the trial
population, this may affect
comparability between the study
and trial populations.
Documentation of
histologically or
cytologically confirmed
diagnosis of ER+ breast
cancer based on local
laboratory results.
ER biomarker test with a result
interpretation of 'Positive.' For
patients with multiple ESR1 tests,
the entry closest in time to study
drug initiation will be used.
[Start of data,
Day -15]
N/A
N/A
Previously untreated with
any systemic anti-cancer
therapy for their
locoregionally recurrent or
metastatic ER+ disease
No first-line regimen for
metastatic disease prior to study
drug initiation.
[Metastatic
diagnosis date,
Day -16]
N/A
N/A
13

Trial Criterion
Real-world operationalization
a
Assessment
window
Rationale for real-world
operationalization
(where applicable)
Validity concerns and how they
will be addressed
(where applicable)
Postmenopausal women,
defined as women with prior
bilateral surgical
oophorectomy or medically
confirmed
post-menopausal status
Surgery indicative of bilateral
surgical oophorectomy or
recorded post-menopausal
status.
Age >50 years if both of the
above are missing.
[Start of data,
Day -15]
Age >50 years will be used as a
proxy for post-menopausal status
for patients with missing data. This
reflects the average age of
menopause for females in the
U.S.
13
Patients >50 years of age but who
are not postmenopausal and who
would have been ineligible for the
trial may be included. This may
affect comparability between the
study and trial populations.
Measurable disease as
defined per RECIST v.1.1
This criterion cannot be
operationalized.
N/A
RECIST assessments are not
performed in routine oncology
care.
Patients with non-measurable
disease who would have been
ineligible for the trial may be
included. This may result in longer
estimates of rwPFS than were
observed in the trial due to
difficulties in objectively
measuring progression for these
patients.
ECOG performance status
0-2
ECOG performance status 0-2 or
missing
Karnofsky performance status
≥50 or missing
For patients with multiple
performance status records, the
entry closest in time to study
drug initiation will be used.
[Day -105,
Day -15]
Performance status will be
evaluated in the 90 days before
study drug initiation to balance
missingness and misclassification,
given infrequent real-world
assessments.
Patients with missing
performance status records who
would have been ineligible for the
trial may be included. This may
affect comparability between the
study and trial populations.
Sensitivity analyses restricting to
patients with known values will be
performed, sample size
permitting.
14

Trial Criterion
Real-world operationalization
a
Assessment
window
Rationale for real-world
operationalization
(where applicable)
Validity concerns and how they
will be addressed
(where applicable)
Adequate organ and
marrow function
No lab results indicating
inadequate organ or marrow
function as defined in the
PALOMA-2 trial protocol.
[Day -45,
Day -15]
Lab tests specified in the trial are
not performed for all patients in
routine oncology care. As a result,
patients with lab results indicative
of inadequate organ or marrow
function will be excluded.
Lab results will be evaluated in the
30 days before study drug
initiation to balance missingness
and misclassification.
Patients with inadequate organ
and marrow function and
therefore potentially reduced
survival who would have been
ineligible for the trial may be
included. We do not expect this to
affect a large number of patients
as physicians are unlikely to start
treatment for patients with poor
organ or marrow function.
Resolution of all acute toxic
effects of prior anti-cancer
therapy or surgical
procedures to National
Cancer Institute (NCI)
Common Terminology
Criteria for Adverse Events
(CTCAE) version 4.0 Grade ≤1
This criterion cannot be
operationalized.
N/A
Acute toxic effects and their
resolution cannot be identified in
the data source.
Patients with ongoing acute toxic
effects who would have been
ineligible for the trial may be
included. We do not expect this to
affect a large number of patients
as physicians are unlikely to start
a new treatment for patients with
ongoing acute toxic effects.
Willingness and ability to
comply with scheduled
visits, treatment plan,
laboratory tests, and other
study procedures
This criterion is not relevant in a
real-world clinical setting.
N/A
N/A
N/A
Willingness and ability to
provide tumor tissues
This criterion is not relevant in a
real-world clinical setting.
N/A
N/A
N/A
Evidence of a personally
signed and dated informed
consent document
This criterion is not relevant in a
real-world clinical setting.
N/A
N/A
N/A
N/A = not applicable
a See Appendix C for code list.
15

7.3.3 Context and rationale for study exclusion criteria
Operational definitions for the study exclusion criteria are presented in Table 5. These exclusion criteria correspond to those applied in the
PALOMA-2 RCT.
As indicated below, PALOMA-2 exclusion criteria that are not relevant outside of an RCT and in a real-world clinical setting (e.g., investigational
site staff, their relatives, or Pfizer employees) will not be applied.
An additional exclusion criteria that was not relevant for the PALOMA-2 trial but is necessary for emulation using RWD is the exclusion of patients
with evidence of a censoring event or death during the exposure ascertainment window (described in Section 7.4.1). Patients with evidence of a
progression event during the exposure ascertainment window will not be excluded as these are likely latent progression events that preceded
study drug initiation.
Table 5. Operational Definitions of Exclusion Criteria
Trial Criterion
Real-world operationalization
a
Assessment
window
Rationale for real-world
operationalization
(where applicable)
Validity concerns and how they
will be addressed
(where applicable)
HER2-positive tumor
V-erb-b2 avian erythroblastic
leukemia viral oncogene homolog 2
(ERBB2) biomarker test with a result
description of 'Positive'.
For patients with multiple ERBB2
tests, the entry closest in time to
study drug initiation will be used.
[Start of data,
Day -15]
N/A
N/A
Patients with advanced,
symptomatic, visceral
spread, that are at risk of
life-threatening
complications in the short
term
This criterion cannot be
operationalized.
N/A
A patient’s short-term risk of
life-threatening complications
cannot be identified in the data
source.
Patients at acute risk of
life-threatening complications
who would have been ineligible
for the trial may be included. We
do not expect this to affect a large
number of patients as physicians
are unlikely to start a new
treatment for patients at high risk
of complications.
16

Trial Criterion
Real-world operationalization
a
Assessment
window
Rationale for real-world
operationalization
(where applicable)
Validity concerns and how they
will be addressed
(where applicable)
Known active, uncontrolled,
or symptomatic central
nervous system
metastases, carcinomatous
meningitis, or
leptomeningeal disease as
indicated by clinical
symptoms, cerebral edema,
and/or progressive growth.
Patients with a history of
central nervous system
(CNS) metastases or cord
compression are eligible if
they have been definitively
treated with local therapy
(e.g., radiotherapy,
stereotactic surgery) and
are clinically stable off
anticonvulsants and
steroids for at least 4 weeks
before randomization
Diagnosis of brain, CNS, and/or
spinal cord metastases.
[Start of data,
Day -15]
Active, uncontrolled, or
symptomatic CNS metastases
and diagnoses of carcinomatous
meningitis and leptomeningeal
disease cannot be identified in
the data source.
Definitive treatment with local
therapy and clinical stability
without anticonvulsants and
steroids cannot be identified in
the data source.
Patients with a history of CNS
metastases who would have been
eligible for the trial (those who
received definitive treatment and
were clinically stable at study
drug initiation) may be excluded.
This may affect comparability
between the study and trial
populations.
Prior neoadjuvant or
adjuvant treatment with a
non-steroidal aromatase
inhibitor (i.e., anastrozole or
letrozole) with disease
recurrence while on or
within 12 months of
completing treatment
Neoadjuvant or adjuvant treatment
with anastrozole or letrozole with
time between the last date of
neoadjuvant or adjuvant therapy
and metastatic diagnosis ≤12
months.
[Metastatic
diagnosis date
-365 days,
Metastatic
diagnosis date
– 1 day]
N/A
N/A
Prior treatment with any
cyclin-dependent kinase
4/6 (CDK4/6) inhibitor
Treatment with ribociclib,
abemaciclib, or palbociclib.
[Start of data,
Day -16]
N/A
N/A
17

Trial Criterion
Real-world operationalization
a
Assessment
window
Rationale for real-world
operationalization
(where applicable)
Validity concerns and how they
will be addressed
(where applicable)
Patients treated within the
last 7 days prior to
randomization with:
Food or drugs that are
known to be Cytochrome
P450 3A4 (CYP3A4)
inhibitors; drugs that are
known to be CYP3A4
inducers; drugs that are
known to prolong the QT
interval
Treatment with a CYP3A4 inhibitor
or inducer or drugs known to
prolong the QT interval, as specified
in the PALOMA-2 trial protocol.
Consumption of foods known to be
CYP3A4 inhibitors (grapefruit,
grapefruit juice) and use of St.
John’s Wort cannot be
operationalized.
[Day -45,
Day -15]
To account for real-world
frequency of medication capture
in the EHR, this exclusion criteria
will be assessed in the 30 days
prior to study drug initiation.
Food consumption and
supplement use cannot be
identified in the data source.
Patients with less recent exposure
to these treatments who would
have been eligible for the trial
may be excluded. Conversely,
patients with grapefruit or St.
John’s Wort exposure who would
have been ineligible for the trial
may be included. This may affect
comparability between the study
and trial populations.
Major surgery,
chemotherapy,
radiotherapy, any
investigational agents, or
other anti-cancer therapy
within 2 weeks before
randomization. Patients who
received prior radiotherapy
to ≥25% of bone marrow are
not eligible independent of
when it was received
First-line regimen for metastatic
disease prior to study drug
initiation.
Anti-cancer therapy other than
study treatment in the same
first-line therapy or neoadjuvant or
adjuvant therapy.
Major cancer-related surgery.
Radiotherapy to ≥25% of bone
marrow cannot be operationalized.
[Day -29,
Day -16]
Non-cancer surgeries cannot be
identified in the data source.
Extent of radiotherapy cannot be
identified in the data source.
Patients with recent major
non-cancer surgery or
radiotherapy to ≥25% of bone
marrow who would have been
ineligible for the trial may be
included. This may affect
comparability between the study
and trial populations.
Diagnosis of any other
malignancy within 3 years
prior to randomization,
except for adequately
treated basal cell or
squamous cell skin cancer,
or carcinoma in situ of the
cervix
Diagnosis of a second primary
malignancy.
[Day -1110,
Day -15]
Information on adequate
treatment for basal cell or
squamous cell skin cancer or
carcinoma in situ of the cervix
cannot be identified in the data
source.
Patients with adequately
controlled basal cell or squamous
cell skin cancer or carcinoma in
situ of the cervix who would have
been eligible for the trial may be
excluded. This may affect
comparability between the study
and trial populations.
18

Trial Criterion
Real-world operationalization
a
Assessment
window
Rationale for real-world
operationalization
(where applicable)
Validity concerns and how they
will be addressed
(where applicable)
Heart-rate corrected QT
interval (QTc) >480 msec
(based on the mean value
of the triplicate
electrocardiograms
[ECGs]), family or personal
history of long or short QT
syndrome, Brugada
syndrome or known history
of QTc prolongation, or
Torsade de Pointes
QTc >480 msec and family history
of long or short QT syndrome
cannot be operationalized.
Diagnosis of long or short QT
syndrome, Brugada syndrome, QTc
prolongation, or Torsade de Pointes.
[Start of data,
Day -15]
QTc measurements and family
history of long or short QT
syndrome cannot be identified in
the data source.
Patients with QTc >480 msec or
family history of long or short QT
syndrome who would have been
ineligible for the trial may be
included. This may affect
comparability between the study
and trial populations.
Uncontrolled electrolyte
disorders that can
compound the effects of a
QTc-prolonging drug (e.g.,
hypocalcemia,
hypokalemia,
hypomagnesemia)
Diagnosis of hypocalcemia,
hypokalemia, or hypomagnesemia.
[Day -105,
Day -15]
Diagnosis of electrolyte disorders
will be evaluated in the 90 days
before study drug initiation to
reflect acute diagnoses and
balance missingness and
misclassification.
Controlled disease status cannot
be identified in the data source.
Patients with controlled disease
who would have been eligible for
the trial may be excluded. This
may affect comparability
between the study and trial
populations.
19

Trial Criterion
Real-world operationalization
a
Assessment
window
Rationale for real-world
operationalization
(where applicable)
Validity concerns and how they
will be addressed
(where applicable)
Any of the following within 6
months of randomization:
myocardial infarction,
severe/unstable angina,
ongoing cardiac
dysrhythmias of grade ≥2,
atrial fibrillation of any
grade, coronary/peripheral
artery bypass graft,
symptomatic congestive
heart failure,
cerebrovascular accident
including transient ischemic
attack, or symptomatic
pulmonary embolism
Diagnosis of myocardial infarction,
angina, ongoing cardiac
dysrhythmias, atrial fibrillation,
congestive heart failure, cerebral
infarction, transient ischemic
attack, or pulmonary embolism.
Coronary/peripheral artery bypass
graft cannot be operationalized.
[Day -195,
Day -15]
Non-cancer surgeries cannot be
identified in the data source.
Severe/unstable angina cannot
be identified in the data source.
Patients who have undergone
artery bypass graft and who
would have been ineligible for the
trial may be included. Patients
with stable angina who would
have been eligible for the trial
may be excluded. This may affect
comparability between the study
and trial populations.
Active inflammatory bowel
disease or chronic diarrhea,
short bowel syndrome, or
any upper gastrointestinal
surgery including gastric
resection
Diagnosis of inflammatory bowel
disease, chronic diarrhea, or short
bowel syndrome.
Upper gastrointestinal surgery
cannot be operationalized.
Active
inflammatory
bowel disease
or chronic
diarrhea:
[Day -105,
Day -15]
Short bowel
syndrome:
[Start of data,
Day -15]
Diagnosis of inflammatory bowel
disease and chronic diarrhea will
be evaluated in the 90 days
before study drug initiation to
reflect active disease and
balance missingness and
misclassification.
Diagnosis of short bowel
syndrome will be evaluated from
the start of data to reflect the
chronic nature of this condition.
Non-cancer surgeries cannot be
identified in the data source.
Patients who have undergone
upper gastrointestinal surgery
and who would have been
ineligible for the trial may be
included. This may affect
comparability between the study
and trial populations.
20

Trial Criterion
Real-world operationalization
a
Assessment
window
Rationale for real-world
operationalization
(where applicable)
Validity concerns and how they
will be addressed
(where applicable)
Known hypersensitivity to
letrozole, or any of its
excipients, or to any
palbociclib (PD-0332991)/
placebo excipients
This criterion cannot be
operationalized.
N/A
Hypersensitivity cannot be
identified in the data source.
Patients with known
hypersensitivity to letrozole or
palbociclib who would have been
ineligible for the trial may be
included. We do not expect this to
affect a large number of patients
as physicians are unlikely to
prescribe these treatments to
patients with a known
hypersensitivity.
Known human
immunodeficiency virus
infection
Diagnosis of human
immunodeficiency virus infection.
[Start of data,
Day -15]
N/A
N/A
Other severe acute or
chronic medical or
psychiatric conditions or
laboratory abnormality that
may increase the risk
associated with study
participation or
investigational product
administration or may
interfere with the
interpretation of study
results and, in the judgment
of the investigator, would
make the patient
inappropriate for entry into
this study
This criterion is not relevant in a
real-world clinical setting.
N/A
N/A
N/A
21

Trial Criterion
Real-world operationalization
a
Assessment
window
Rationale for real-world
operationalization
(where applicable)
Validity concerns and how they
will be addressed
(where applicable)
Patients who are
investigational site staff
members or relatives of
those site staff members or
patients who are Pfizer
employees directly involved
in the conduct of the trial
This criterion is not relevant in a
real-world clinical setting.
N/A
N/A
N/A
Participation in other studies
within 4 weeks before
randomization
This criterion cannot be
operationalized.
N/A
Dates of clinical trial participation
cannot be identified in the data
source.
Patients who have recently
participated in a clinical trial and
who may have initiated treatment
for metastatic disease in that
setting who would have been
ineligible for the trial may be
included. This may affect
comparability between the study
and trial populations, and may
bias comparative effect estimates
if trial participation is related to
the study treatment received.
Recent or active suicidal
ideation or behavior
This criterion cannot be
operationalized.
N/A
Suicidal ideation or behavior
cannot be identified in the data
source.
Patients with recent or active
suicidal ideation or behavior who
would have been ineligible for the
trial may be included. We do not
expect this to affect a large
number of patients given the low
prevalence of suicide mortality
among patients with advanced
breast cancer (<1%).
14,15
N/A = not applicable
a See Appendix C for code list.
22

7.4 Variables
7.4.1 Context and rationale for exposures of interest
Operational definitions for the two treatment strategies that will be compared are presented in Table 6. Exposure will be defined based on
treatment initiated during the 15-day exposure ascertainment window. While patients in the PALOMA-2 trial received study treatments on the
same day, a 15-day window was selected to allow for potential delays in data entry and treatment administration variability that may occur in
routine practice. In a feasibility assessment of 944 patients in the data source who initiated both letrozole and palbociclib as first-line therapy,
93.3% started both medications within 15 days. A shorter exposure window 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. As discussed above, patients who
experience a censoring event or death during the exposure ascertainment window will be excluded from the study to align the start of follow-up
for both exposure groups.
Table 6. Operational Definitions of Exposures
Group
name(s)
Details
Washout window
Assessment
Window
Incident with
respect to…
Source of
algorithm
Validity concerns and how
they will be addressed
Exposed
Patients initiating
treatment with
palbociclib and letrozole
within 15 days of one
another, in the metastatic
setting
N/A, treatment in
first-line metastatic
setting
[Metastatic
diagnosis date,
Day -1]
Metastatic
Diagnosis
Curated regimen
definition; clinical
experts
Real-world exposure group
definitions allow for more
flexibility in treatment timing
than in the trial. The potential
impact of this on results will be
explored through a sensitivity
analysis shortening the
duration of the exposure
ascertainment window
(Table 11).
Comparator
Patients initiating
treatment with letrozole
with no evidence of
palbociclib treatment
within 15 days, in the
metastatic setting
N/A, treatment in
first-line metastatic
setting
[Metastatic
diagnosis date,
Day -1]
Metastatic
Diagnosis
Curated regimen
definition; clinical
experts
N/A = not applicable
7.4.2 Context and rationale for outcome of interest
The operational definition of the outcome of interest, rwPFS, is presented in Table 7. This outcome corresponds to the primary outcome of the
PALOMA-2 trial.
23

Table 7. Operational Definitions of Outcome
Outcome
name
Details
Primary outcome
Type of outcome
Washout window
Source of
algorithm
Validity concerns and how
they will be addressed
rwPFS
Time from Day 0 to
disease progression or
death
Yes
Time-to-event
N/A
Progression events
are curated in the
data. All tumor
progression events
after initial cancer
diagnosis are
captured.
Real-world progression is not
evaluated at fixed intervals as
was done in the trial. The
frequency and timing of
real-world progression
assessment results by
exposure group will be
reported to contextualize
study findings.
N/A = not applicable
7.4.3 Context and rationale for follow-up
Follow-up will begin on Day 0, and will continue until the earliest of:
1.
Date of documented progression or death (Table 7);
2.
Initiation of any new anti-cancer therapy;
3.
The administrative end of the study period (Day 1004, or 33 months, of follow-up), to align with the maximum follow-up time at which PFS
was evaluated in the PALOMA-2 trial;
4.
End of the data (July 20, 2023);
5.
Loss to follow-up: The last date of curated EHR activity prior to a period of >90 days without curated EHR activity and without death.
Operational definitions for the study censoring criteria are presented in Table 8. These censoring criteria correspond to those applied in the
PALOMA-2 RCT where applicable.
Table 8. Operational Definitions of Censoring Criteria
Trial Criterion
Real-world operationalization
Rationale for real-world
operationalization
(where applicable)
Validity concerns and how they will
be addressed
(where applicable)
Initiation of a new anti-cancer
treatment
Treatment with any new anti-cancer
therapy.
N/A
N/A
24

Trial Criterion
Real-world operationalization
Rationale for real-world
operationalization
(where applicable)
Validity concerns and how they will
be addressed
(where applicable)
End of study
Administrative end of study (Day
1004, or 33 months) or July 20, 2023.
Align with maximum time in
PALOMA-2 or end of available data in
real-world data source.
N/A
Withdrawal of consent
This criterion is not relevant in a
real-world clinical setting.
N/A
N/A
Loss to follow-up
A period of >90 days without curated
EHR activity and without death.
Curated activity in the real-world
data source indicates points at
which progression can be recorded.
Metastatic breast cancer patients
likely have contact with the
healthcare system at least every 90
days for lab work, prescription refills,
outpatient visits, or scans. Periods
greater than 90 days may indicate
loss to follow-up during which
censoring or outcome events cannot
be captured.
Patients who use the health care
system less frequently will be
censored. A sensitivity analysis will
be conducted expanding the period
without curated activity to >180 days
(Table 11).
≥2 missed disease assessments
This criterion will not be
operationalized.
Real-world progression is not
evaluated at fixed intervals as was
done in the trial. Patients with gaps in
activity of greater than 90 days will
be censored under the loss to
follow-up definition.
The frequency of real-world
progression assessments will be
reported to contextualize study
findings.
N/A = not applicable
7.4.4 Context and rationale for covariates (confounding variables and effect modifiers, e.g., risk factors, comorbidities,
comedications)
Operational definitions for the study covariates are presented in Table 9. Covariates were chosen based on the primary trial publication and the
research team’s substantive knowledge of potential confounders. For time-varying characteristics, the value closest in time prior to study drug
initiation will be used.
25

Table 9. Operational Definitions of Covariates
Characteristic
Details/Levels
a
Type of variable
Assessment window
Patient Demographic Characteristics
Age
Age at study drug initiation
18 to <65 yrs, ≥65 yrs
Continuous, Categorical
[Day -15]
Race
American Indian or Alaska Native, Asian, Native
Hawaiian or other Pacific Islander, Black or African
American, White, other or unknown race
Categorical
[Start of data, Day -15]
Ethnicity
Hispanic or Latino, not Hispanic or Latino, unknown
Categorical
[Start of data, Day -15]
Region
Northeast, south, midwest, west, missing
Categorical
[Start of data, Day -15]
Clinical Characteristics
Oncology clinic site type
Academic, community, missing
Categorical
[Start of data, Day -15]
Performance status
ECOG performance status 0, 1, 2, or Karnofsky
performance status 50, 60, 70, 80, 90, 100, or
missing.
Categorical
[Day -105, Day -15]
Smoking status at initial
diagnosis
Current smoker, former smoker, non-smoker,
missing
Categorical
[Start of data, Initial diagnosis date]
Number of outpatient
visits in past year
N/A
Continuous
[Day -380, Day -15]
Disease Characteristics and Treatment History
Year of study treatment
initiation
N/A
Categorical
[Day -15]
Prior therapy
Aromatase inhibitor, antiestrogen
Dichotomous
[Start of data, Day -15]
Disease stage at initial
diagnosis
Disease stage I-IV
Categorical
[Initial diagnosis date - 30, Initial diagnosis date
+ 90]
26

Characteristic
Details/Levels
a
Type of variable
Assessment window
Time between
metastatic diagnosis
and treatment initiation
N/A
Continuous
[Metastatic diagnosis date, Day -15]
Time interval between
initial diagnosis and
metastatic diagnosis
(approximate
disease-free interval)
N/A
Continuous
[Initial diagnosis date, Metastatic diagnosis
date]
Prior neoadjuvant or
adjuvant chemotherapy
Yes, no
Dichotomous
[Start of data, Day -16]
Prior neoadjuvant or
adjuvant hormone
therapy
Yes, no
Dichotomous
[Start of data, Day -16]
Surgery in the curative
setting
Yes, no
Dichotomous
[Start of data, Day -16]
Radiation in the curative
setting
Yes, no
Dichotomous
[Start of data, Day -16]
Recurrence type
Recurrent, de novo metastatic, missing
Categorical
[Initial diagnosis date - 30, Initial diagnosis date
+ 90]
Metastatic disease site
Visceral (lung, including pleura, liver), non-visceral
(all others), bone only
Categorical
[Start of data, Day -15]
Number of metastatic
disease sites
1, 2, ≥3
Continuous, Categorical
[Start of data, Day -15]
N/A = not applicable
a See Appendix C for code list.
27

7.5 Data analysis
7.5.1 Context and rationale for analysis plan
Analytic Population
Primary analyses will be conducted in the real-world study population based on the first study treatment initiated. This approach is intended to
emulate the intent-to-treat analysis conducted in the PALOMA-2 trial, where patients were analyzed based on their randomized treatment
assignment. As crossover between treatment arms was not allowed in the PALOMA-2 trial, intent-to-treat and per-protocol trial analyses would
produce equivalent results. Conversely, real-world patients initially prescribed letrozole could initiate palbociclib after the end of the exposure
ascertainment window. To investigate the potential for crossover between comparison groups prior to disease progression in the real-world
population, descriptive analyses of all first-line treatments initiated and time between treatments will be conducted.
IPTW will be used to approximate full conditional exchangeability between the comparison groups and facilitate estimation of the population
average treatment effect.
16–18 Propensity scores (PS) reflecting the conditional probability of initiating treatment with palbociclib and letrozole
will be calculated via multivariable logistic regression. Exposure to palbociclib and letrozole will be regressed on a priori identified potential
confounders (Table 9). Inverse probability of treatment weights will be calculated as the inverse of the propensity score (1/PS) for patients in the
exposed group and as the inverse of one minus the propensity score (1/1-PS) for comparator patients.
19 Patients are therefore weighted by the
inverse probability of initiating the treatment they actually started, conditional on the observed covariates included as independent variables in
the PS model. This approach aims to create a pseudo-population with full exchangeability on measured confounders within which the
treatment effect is estimated.
Confounder balance will be assessed by comparing the absolute standardized difference (ASD) in the distribution of categorical variable levels
and the mean of continuous variables between the weighted exposure groups.
16 If balance is not achieved (ASD > 0.1), alternative specifications
of the PS model, e.g., including variable transformations and interaction terms, will be explored. Confounders with insufficient balance may also
be included as covariates in the outcome models.
The distribution of baseline patient demographic and clinical characteristics will be compared between comparator patients who index before
2015 and on/after 2015 to understand if differences exist between those who received the comparator treatments before and after palbociclib
approval in this indication. Characteristics to be compared are described in Table 9. Differences will be assessed using t-tests, chi squared tests,
and accompanying 95% confidence intervals.
Descriptive Analyses
The distribution of patient baseline demographic and clinical characteristics will be compared between the unweighted and weighted
real-world populations and the PALOMA-2 trial population. Characteristics to be compared are described in Table 9. Differences will be
assessed using t-tests, chi squared tests, and accompanying 95% confidence intervals.
28

Reasons for exclusion and censoring will be presented in a Consolidated Standards of Reporting Trials (CONSORT) diagram and distributions of
missingness of inclusion/exclusion criteria and potential confounders will be calculated and compared by exposure group.
20
Treatment Effectiveness
Similar to the PALOMA-2 trial, comparative treatment effectiveness of palbociclib and letrozole versus letrozole alone will be estimated using
median rwPFS and by comparing the hazards of progression and death in the two exposure groups. Follow-up will be administratively censored
at 33 months to emulate maximum follow-up in the PALOMA-2 trial at the time that results were reported.
2 We will emulate subgroup analyses
performed in the trial, except where sample size is less than 10 in each exposure group. Post-hoc analyses to explore effect modification will be
conducted for characteristics where distributions differ between the trial and RW populations.
Details of the analytic approach are presented in Table 10.
Table 10. Primary and subgroup analysis specification
Hypothesis:
Median rwPFS is longer and the hazard of progression is lower among patients who initiated palbociclib and letrozole
versus letrozole alone.
Exposure contrast:
Patients initiating palbociclib and letrozole compared with patients initiating letrozole alone in the first line metastatic
setting.
Outcome:
rwPFS
Analytic software:
Aetion Substantiate Version 5.01 (or latest version)
Model(s):
Median rwPFS will be estimated using a Kaplan-Meier estimator, weighted by time-fixed inverse probability of treatment
weights. The weighted survival probability
for exposure group ‘A=a’ at time ‘t’ will be as follows:
𝑆𝑎(𝑡)
𝑆𝑎(𝑡) =
𝑡
∏1 −
𝑑𝑡𝑎
𝑟𝑡𝑎
where
denotes the weighted number of events and
denotes the weighted
𝑑𝑡𝑎=
𝑖=1
𝑁
∑𝑤𝑖𝑡• 𝑌𝑖𝑡∙𝐼(𝐴𝑖𝑡= 𝑎)
𝑟𝑡𝑎=
𝑖=1
𝑁
∑𝑤𝑖𝑡∙𝐼(𝐴𝑖𝑡= 𝑎)
risk set.
21–23 This is equivalent to calculating the Kaplan-Meier estimator in the IPTW weighted population.
22 A
non-parametric bootstrap will be used to derive 95% confidence intervals.
A Cox proportional hazards model will be used to estimate the hazard ratio at 33 months in the IPTW weighted
population as follows:
ℎ𝑡, 𝐿0
(
) = ℎ0 𝑡
( ) * 𝑒
β1𝐴0+β2
𝑇𝐿0
29

where
is the hazard of progression at discrete time interval ‘t’ conditional on the vector of potential confounders ‘
ℎ𝑡, 𝐿0
(
)
’ assessed at baseline;
is the baseline hazard at discrete time interval ‘t’; and
is an indicator for treatment
𝐿0
ℎ0 𝑡
( )
𝐴0
initiation, coded as '1' and '0' for the exposure and comparator group, respectively. Patients with an outcome or
censoring event on Day 0 will be assigned a follow-up time of 0.5 days. The proportional hazards assumption will be
checked using plots that display the scaled Schoenfeld residuals vs. time for each covariate; if violations are detected, a
time-dependent or stratified Cox proportional hazards model will be considered. The Efron method of handling ties will
be used.
Confounding adjustment method
Time-fixed inverse probability of treatment weights will be used to adjust for confounding. Individual-level weights will
be estimated by the following formula:
𝑊
𝐴=
1
𝑓(𝐴|𝐿0)
where
is the first study treatment that the patient initiated and
is a vector of baseline confounders.
𝐴
𝐿0
The quantity in the denominator
—the probability of exposure to treatment
given baseline confounders
—will
𝑓(𝐴|𝐿0)
𝐴
𝐿0
be estimated using a logistic regression model with
as the dependent variable and the vector
as the independent
𝐴
𝐿0
variables. The distribution of weights will be used to identify potential extreme weights. If extreme weights are identified,
weight truncation and/or stabilization will be considered.
All potential confounder variables will be considered for inclusion in the weight estimation (Table 9). 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. Thus, variables with high
missingness will be excluded from the final model. Additionally, categorical variables may be collapsed to ensure
convergence of the propensity score model.
Missing data methods
Data missingness was assessed as part of an initial feasibility assessment (Appendix B); therefore, key variables are
expected to have a high degree of completeness. If substantial missingness results in an insufficient sample size for the
complete analytic dataset, alternative variable specifications (e.g., changing the time frames over which variables are
assessed) or exclusion of variables may be considered.
Subgroup Analyses
Cox proportional hazards models will be assessed in the following subgroups to align with the trial, as feasible.
Measurable disease, international region, and histopathological classification were included as subgroups in the trial
but cannot be emulated in the RWD source.
1.
Age (18 to <65 yrs, ≥65 yrs)
2.
Race (White, Asian)
3.
Site of metastatic disease at baseline (visceral (lung, liver), non-visceral)
4.
Disease-free interval (<12 months, ≥12 months, de novo metastatic)
30

5.
Prior hormonal therapy (yes, no)
6.
Performance status (ECOG 0 or Karnofsky Score 100, ECOG 1-2 or Karnofsky Score 50-90)
7.
Bone-only disease at baseline (yes, no)
8.
Prior chemotherapy (yes, no)
9.
Most recent therapy (aromatase inhibitor, antiestrogen)
10.
Number of disease sites (1, ≥2)
7.5.2 Context and rationale for sensitivity analyses
Sensitivity analyses will be conducted to explore the potential impact of several key study design elements. Planned analyses and their
respective goals are presented in Table 11.
Table 11. Sensitivity analyses – rationale, strengths and limitations
Description
Primary Analysis
Sensitivity Analysis
Rationale
Strengths of the sensitivity
analysis compared to the
primary
Limitations of the
sensitivity analysis
compared to the primary
Contemporaneous
cohort
Study population
includes patients
with first study drug
initiation from
February 1, 2010 to
June 20, 2023.
The study population will
be restricted to patients
with first study drug
initiation from February 1,
2015 to June 20, 2023 to
coincide with Food and
Drug Administration (FDA)
accelerated approval of
palbociclib.
Removing historical
comparator patients
will decrease potential
confounding due to
changes in treatment
paradigms over time.
This analysis will provide
effect estimates using a
control group that more
closely resembles the
exposed group with respect
to treatment standards.
Restriction to
contemporaneous
comparator patients
may reduce the sample
size and introduce other
unmeasured sources of
confounding due to
differences between
patients who do and do
not initiate newly
available treatment.
31

Description
Primary Analysis
Sensitivity Analysis
Rationale
Strengths of the sensitivity
analysis compared to the
primary
Limitations of the
sensitivity analysis
compared to the primary
Limit eligible cohort
entry dates to six
months prior to the
end of the data
Eligible cohort entry
period limited to
dates at least one
month prior to the
end of the data.
The eligible cohort entry
period will be limited to
dates at least six months
before the end of the data
(February 1, 2010 to
February 20, 2023).
Patients identified
toward the end of the
available study period
may not have
adjudicated death
data, leading to
underestimates of
death.
Greater minimum follow-up
time and opportunity for
death to be identified.
The sample size will be
reduced relative to the
primary analysis.
Modified exposure
ascertainment
window (one day)
Exposure status is
ascertained over a
15-day window
following first study
drug initiation.
The exposure
ascertainment window
will be shortened to occur
over a single day.
Shortening the
exposure
ascertainment
window will exclude
patients who initiate
letrozole and
palbociclib greater
than 1 day apart from
the exposed group.
This analysis will more closely
align the treatment strategy
in the exposed group to that
of the trial.
Patients who initiate
palbociclib greater than 1
day after letrozole will be
eligible for inclusion in
the comparator group,
which may bias results
towards the null.
Modified exposure
ascertainment
window (30 days)
Exposure status is
ascertained over a
15-day window
following first study
drug initiation.
The exposure
ascertainment window
will be increased to occur
over 30 days.
Increasing the
exposure
ascertainment
window will include
patients who initiate
letrozole and
palbociclib greater
than 15 days apart in
the exposed group.
This analysis will increase
follow-up time for patients
who initiate study drugs
further apart in time and will
employ a more flexible
definition of the exposure
ascertainment period that
aligns with the data vendor
line of therapy definition.
Patients who initiate
palbociclib greater than
15 days after letrozole will
be eligible for inclusion in
the exposed group, which
may less accurately
reflect the treatment
strategy used in the trial.
32

Description
Primary Analysis
Sensitivity Analysis
Rationale
Strengths of the sensitivity
analysis compared to the
primary
Limitations of the
sensitivity analysis
compared to the primary
Loss to follow-up
censoring definition
Patients are
censored on the
last date prior to a
period of >90 days
without curated
EHR activity and
without death.
Censor patients on the
last date prior to a period
of >180 days without
curated EHR activity and
without death.
Lengthening the time
period without
curated EHR activity
will allow patients who
are using healthcare
less frequently to have
outcomes recorded
after a gap in 180 days
of activity.
Progression and death
events that occur after a gap
of 90 days without curated
EHR activity will be included.
Lengthening the time
period without curated
EHR activity will increase
the possibility of
unobserved events or
censoring reasons and
potentially overestimate
rwPFS.
ECOG performance
status assessment
window
ECOG performance
status for study
inclusion will be
assessed within 90
days from first
study treatment
initiation.
ECOG performance status
for study inclusion will be
assessed within 30 days
from first study treatment
initiation.
Shortening the
assessment window
for ECOG performance
status may more
accurately reflect
patients’ status at the
time of study
treatment initiation
and will provide
information on the
sensitivity of results to
this key inclusion
criteria.
Assessing ECOG status closer
in time to study treatment
initiation may create a study
population that is more
similar to the trial population.
A larger number of
patients may be missing
ECOG performance
status during the shorter
time period. Patients with
missing values will be
included in the analysis
in line with the primary
approach.
Complete case -
ECOG performance
status
Patients with
missing ECOG or
Karnofsky
performance
status in the 90
days prior to first
study treatment
initiation are
included.
Patients with missing
ECOG or Karnofsky
performance status in the
90 days prior to first study
treatment initiation will be
excluded.
Removing patients
with missing ECOG
performance status
may reduce
misclassification of
disease severity and
will enable descriptive
comparison of the
study population with
and without these
patients.
Excluding patients with
missing ECOG may create a
study population that is more
similar to the trial population.
The sample size will be
reduced.
33

7.6 Data source(s)
7.6.1 Context and rationale for data source(s)
The data source used in this study obtains clinical data from multiple partnerships across the U.S. These partnerships include data from
organizations that aggregate data from oncology practices. The data vendor receives the complete EHR from each practice, including
unstructured notes and scanned documents attached to the EHR. Structured and unstructured data are semi-normalized using an
Observational Medical Outcomes Partnership (OMOP)-based common data model to improve uniformity across original data sources. Data
curation (i.e., abstraction) of unstructured data from provider notes, pathology and imaging reports, and scanned documents is performed by
trained reviewers. Natural Language Processing (NLP) is used to identify relevant strings of data within the patient record, but all curated data
points are reviewed, interpreted, and documented by a human curation team. Patients are not filtered on the basis of data completeness to
allow for flexibility when selecting the analytic sample.
Reason for selection: The dataset was considered due to its focus on oncology EHR data and was further considered fit-for-purpose after a
detailed feasibility assessment that considered available sample size and completeness and quality of key inclusion criteria, exclusion criteria,
key confounders, and outcomes. (Appendix B). Linked exposure and outcome data will not be accessed prior to conducting final analyses.
Strengths of data source(s): The dataset includes individuals from all regions of the U.S. and both community and academic providers. Due to
the oncology focus of the data vendor, this dataset provides information on important diagnostic, prognostic, and clinical characteristics
among breast cancer patients. The data include several important curated fields, including ECOG, line of therapy, and progression, using a
broad range of clinical documentation (e.g., physician notes, pathology reports, etc.).
Limitations of data source(s): Algorithms used by the data vendor to derive treatment regimens and certain key variables such as those used to
define key inclusion and exclusion criteria, potential confounders, and mortality have not been validated. The data are also limited by the
accuracy of data collection in the original EHRs, the subjective nature of data abstraction, and, for some variables, the inability to determine
whether missing values indicate the true absence of a condition or missing data.
Data source provenance/curation: The data vendor programmatically tracks all inbound data throughout the data pipeline for data
provenance and quality control using internal identifiers. Additionally, the data curation process is operationalized to ensure consistency and
high inter-rater reliability. To reduce data entry errors following abstraction, data curation software is utilized. Quality control analyses on
various subsets (e.g., a random sample, a specific cohort, only curated patients) before data are finalized for external use.
34

Table 12. Metadata about data sources and software
Data Source(s):
[Redacted]
Study Period:
Start of all available data-July 20, 2023
Eligible Cohort Entry Period:
February 1, 2010-June 20, 2023
Data Version (or date of last update):
Q3 2023
Data sampling/extraction criteria:
Described above
Type(s) of data:
Clinical data sourced from oncology practices in the U.S.
Data linkage:
N/A
Conversion to Common Data Model:
Observational Medical Outcomes Partnership-based Common Data Model
Software for data management:
Aetion Substantiate Version 5.01 (or latest version)
N/A = not applicable
7.7 Data management
Raw data review
At Aetion, as part of the data ingestion process, raw data review is routinely conducted to understand contents of the data table(s), establish
relationships, and help inform the database connection specification. Scientific integrity checks are performed to understand if the contents of
the data shipment is consistent with the expected data as laid out in the applicable data usage agreement. Some of the key characteristics
explored in this process include:
●
Table structure (number of rows, columns, column names etc.)
●
Summary counts per table (i.e., non-missing counts, unique counts)
●
Variable distribution (e.g., min, mean, median, max for numeric variables; top frequencies for categorical variables)
●
Date range (min, max and distribution over a time period)
35

●
Missingness percentage of attributes
Database connection (DBC) process
Following receipt and review of the raw data, a data connector specification is drafted by a data scientist. The specification provides a map to
Engineering for transformation of raw data to the Aetion longitudinal patient timeline. It includes information such as:
●
Overall schema including tables (event types), rows (events), and columns (attributes); derivation of attributes to improve data flexibility
on Platform and rationale for any attributes or events that are dropped
●
Event dates that define how data will be reflected on the longitudinal patient timeline, and any minimal processing rules (e.g., drop an
event that does not include a start or end date)
●
Skeleton structure diagram that represents the logical view of the entire database, defining how the data tables are organized and
related in the longitudinal patient timeline and how the relations among them are associated
●
Information for user interface and labeling
●
Codes and definitions; typically used to substitute users’ having to look-up multiple resources to understand/process data
Validation of the DBC is completed to ensure that the implementation of DBC logic leads to transformed data output that connects to and
behaves within Aetion Substantiate exactly as intended. Raw data are never loaded as-is; rather, data are transformed (via the DBC) into a
longitudinal sequence of healthcare data points for each patient. DBC validation is required to confirm that this transformation was performed
correctly. This helps to ensure validity/accuracy of the connected data and its importance cannot be ignored. Validation is performed via
double programming, where two different people work independently from the same DBC specification and then compare their output. The DBC
is considered validated if the outputs are identical. If the outputs are not identical, then the source of the discrepancy is investigated and
resolved.
Following validation, the specification files are used to create an Aetion data dictionary for the dataset. In addition, throughout the data
connector spec / creation process, any issues or decisions that have to be made that are not otherwise specified in the Specification files (e.g.,
how missing dates are handled), are noted in the data dictionary.
7.8 Quality control
Prior to deployment on Aetion Substantiate, a manual test of certain platform features and dataset values is conducted to ensure they are
visible and testable on the front-end. This test is run following any deployment activity (such as a version update and/or data/shard update).
Checks include:
●
Baseline values for database information (dataset name, patient counts, earliest and latest event dates)
●
Database configuration (specified dataset values)
36

●
Measure, Cohort, and Analysis Generations to confirm this functionality using the dataset
●
Output from generated analysis output
●
Coding Systems, if applicable
The implementation of all variables, cohorts, and analytic plans will be individually checked by two analysts. Any discrepancies will be discussed
with the analysts and study lead to ensure alignment with the study design outlined in the protocol.
7.9 Study size and feasibility
The PALOMA-2 trial target sample size was 650 patients with 1:1 randomization. This sample size was based on 90% power to detect a hazard
ratio for progression of 0.69 at a one-sided alpha level of 0.025 (assuming 347 events).
2 A total of 666 patients were included in the trial.
Sample size requirements to detect a range of hazard ratios relevant to the PALOMA-2 trial are presented in Table 13. If the unweighted study
sample size falls below the lowest estimate, corresponding to the required sample size to detect the point estimate for the hazards of
progression or death observed in the trial with 80% power, implementation will pause. The study team and CARE Steering Committee will then
consider the value of continuing the study with potentially insufficient power, given the lower primacy of statistical significance in an emulation
setting.
Table 13. Sample size requirements
Power
Hazard ratio
for death
Ratio of exposed
to unexposed
Alpha
Prevalence of death
among the unexposed
a
Total sample size
required
b
Trial sample size calculation, point estimate
90%
0.69
1:1
5%
57.7%
530
Trial sample size calculation, point estimate
80%
0.69
1:1
5%
57.7%
396
Trial result, upper confidence limit
90%
0.72
1:1
5%
57.7%
675
Trial result, upper confidence limit
80%
0.72
1:1
5%
57.7%
505
Trial result, point estimate
90%
0.58
1:1
5%
57.7%
246
Trial result, point estimate
80%
0.58
1:1
5%
57.7%
184
a As reported in the trial.
b Calculated using the powerSurvEpi R package
24, based on the sample size formula for proportional hazards modeling derived by Latouche et al.
25
37

In feasibility analyses (Appendix B) among adult, postmenopausal females with metastatic breast cancer and no evidence of ECOG
performance status >2 in the data source, there were 944 individuals with evidence of initiating both palbociclib and letrozole, and 660
individuals who initiated letrozole only. This real-world study will include data for all individuals meeting study inclusion and exclusion criteria.
8. Strengths and Limitations
This emulation study is limited by several inherent differences between trial settings and non-interventional studies leveraging RWD.
In the absence of randomization, IPTW will be used to control for confounding. However, important confounders may be unavailable or
imperfectly measured in the RWD source, which may result in residual confounding. There may be residual confounding by indication arising
from causes of progression that also impact physicians’ treatment decisions. Several measures of disease severity (e.g., performance status,
stage at initial diagnosis, disease-free interval) will be used to generate treatment probabilities, but these may not sufficiently control for
confounding by indication. In particular, real-world patients who did not initiate palbociclib after approval may systematically differ from those
treated with palbociclib for reasons that are not captured in the health record. These patients may also be different from those enrolled in the
PALOMA-2 RCT, limiting successful emulation of the trial results.
Several trial design elements cannot be perfectly emulated or operationalized due to limitations of the data source. First, the trial included
international sites, while the RWD source is restricted to EHR data from U.S. oncology clinics. Second, while patients with locoregionally recurrent
disease were included in the trial, similar patients cannot be identified in the data source. These patients constituted a small proportion of the
original trial population (<3% in each arm), but restriction of the real-world population to patients with metastatic disease does limit the
comparability of the two populations. Third, some study inclusion and exclusion criteria and potential effect modifiers, such as measurable
disease, cannot be operationalized and others, such as performance status, lab values, and patient symptoms, may be missing in the RWD due
to infrequent real-world clinical assessment or inadequate capture in the EHR. Excluding patients who have missing values for these key
variables may result in systematic differences between the trial and real-world populations. A preliminary data feasibility assessment was
conducted prior to protocol finalization to ensure that key study eligibility criteria and potential confounders had a high degree of completeness
(Appendix B). Fourth, while trial treatments were administered on the same day, these treatment strategies must be approximated (within 15
days) due to differences in medication dosing schedules, insurance delays, and provider decision-making in routine clinical practice. Finally,
progression surveillance is conducted less frequently and regularly in real-world practice than was performed in the trial, which may affect
estimates of rwPFS.
At the same time, this study proposes to use a high-quality, RWD source specifically designed for conducting RWD analyses in oncology. The
data source includes curated, quality-controlled data elements (e.g., ECOG, progression) unique to oncology studies and necessary to this
emulation. The preliminary feasibility assessment indicated low missingness of key variables, the ability to create a trial similar population
through careful operationalization of trial characteristics, and a larger available sample size than was enrolled in the trial. Differences between
38

the trial and real-world emulation, including rates of study treatment discontinuation and crossover, will be transparently reported and
compared to contextualize final results.
9. Protection of Human Subjects
This study will use de-identified secondary data and therefore does not constitute research involving human subjects. Institutional review board
exemption will be requested.
10. Reporting of Adverse Events
Detection and reporting of adverse events do not apply as this study involves secondary use of real-world data from an existing data collection
infrastructure.
39

11. References
1.
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. 2023;113(6):1217-1222. doi:10.1002/cpt.2800
2.
Finn RS, Martin M, Rugo HS, et al. Palbociclib and Letrozole in Advanced Breast Cancer. N Engl J Med. 2016;375(20):1925-1936.
doi:10.1056/NEJMoa1607303
3.
National Institute for Health and Care Excellence. NICE Real-World Evidence Framework. National Institute for Health and Care Excellence;
2022. Accessed December 21, 2022.
https://www.nice.org.uk/corporate/ecd9/resources/nice-realworld-evidence-framework-pdf-1124020816837
4.
Center for Drug Evaluation and Research, Center for Biologics Evaluation, Oncology Center of Excellence. Considerations for the Use of
Real-World Data and Real-World Evidence to Support Regulatory Decision-Making for Drug and Biological Products. U.S. Food and Drug
Administration; 2023. Accessed December 21, 2023. https://www.fda.gov/media/171667/download
5.
Purpura CA, Garry EM, Honig N, Case A, Rassen JA. The Role of Real‐World Evidence in FDA‐Approved New Drug and Biologics License
Applications. Clin Pharmacol Ther. 2022;111(1):135-144. doi:10.1002/cpt.2474
6.
Visvanathan K, Levit LA, Raghavan D, et al. Untapped Potential of Observational Research to Inform Clinical Decision Making: American
Society of Clinical Oncology Research Statement. J Clin Oncol. 2017;35(16):1845-1854. doi:10.1200/JCO.2017.72.6414
7.
Castellanos EH, Wittmershaus BK, Chandwani S. Raising the Bar for Real-World Data in Oncology: Approaches to Quality Across Multiple
Dimensions. JCO Clin Cancer Inform. 2024;(8):e2300046. doi:10.1200/CCI.23.00046
8.
Franklin JM, Pawar A, Martin D, et al. Nonrandomized Real‐World Evidence to Support Regulatory Decision Making: Process for a Randomized
Trial Replication Project. Clin Pharmacol Ther. 2020;107(4):817-826. doi:10.1002/cpt.1633
9.
Merola D, Campbell UB, Lenis D, Schneeweiss S, Wang SV. Calibrating real-world evidence against randomized trials: A pilot to inform the
CARE Initiative. Manuscr Submitt Publ. Published online 2024.
10. Stang A, Poole C, Kuss O. The ongoing tyranny of statistical significance testing in biomedical research. Eur J Epidemiol. 2010;25(4):225-230.
doi:10.1007/s10654-010-9440-x
11.
Pfizer. A Randomized, Multicenter, Double-Blind Phase 3 Study of PD-0332991 (Oral CDK 4/6 Inhibitor) Plus Letrozole Versus Placebo Plus
Letrozole For The Treatment Of Postmenopausal Women With ER (+), HER2 (-) Breast Cancer Who Have Not Received Any Prior Systemic
40

Anti Cancer Treatment For Advanced Disease. clinicaltrials.gov; 2023. Accessed December 31, 2023.
https://clinicaltrials.gov/study/NCT01740427
12. Eisenhauer EA, Therasse P, Bogaerts J, et al. New response evaluation criteria in solid tumours: Revised RECIST guideline (version 1.1). Eur J
Cancer. 2009;45(2):228-247. doi:10.1016/j.ejca.2008.10.026
13. Menopause - Symptoms and causes. Mayo Clinic. Accessed May 22, 2024.
https://www.mayoclinic.org/diseases-conditions/menopause/symptoms-causes/syc-20353397
14. Gaitanidis A, Alevizakos M, Pitiakoudis M, Wiggins D. Trends in incidence and associated risk factors of suicide mortality among breast cancer
patients. Psychooncology. 2018;27(5):1450-1456. doi:10.1002/pon.4570
15. Zaorsky NG, Zhang Y, Tuanquin L, Bluethmann SM, Park HS, Chinchilli VM. Suicide among cancer patients. Nat Commun. 2019;10(1):207.
doi:10.1038/s41467-018-08170-1
16. Austin PC, Stuart EA. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score
to estimate causal treatment effects in observational studies. Stat Med. 2015;34(28):3661-3679. doi:10.1002/sim.6607
17. Hernán MA, Robins JM. Causal Inference: What If. Chapman & Hall/CRC; 2020.
18. Austin PC. The use of propensity score methods with survival or time-to-event outcomes: reporting measures of effect similar to those used
in randomized experiments. Stat Med. 2014;33(7):1242-1258. doi:10.1002/sim.5984
19. Austin PC. An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies. Multivar Behav Res.
2011;46(3):399-424. doi:10.1080/00273171.2011.568786
20. Schulz KF, Altman DG, Moher D. CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials. Published online
2010.
21. Xie J, Liu C. Adjusted Kaplan–Meier estimator and log‐rank test with inverse probability of treatment weighting for survival data. Stat Med.
2005;24(20):3089-3110. doi:10.1002/sim.2174
22. 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
23. Cole SR, Hernán MA. Adjusted survival curves with inverse probability weights. Comput Methods Programs Biomed. 2004;75(1):45-49.
doi:10.1016/j.cmpb.2003.10.004
41

24. Qui W, Chavarro J, Lazarus R, Rosner B, Ma J. powerSurvEpi: Power and Sample Size Calculation for Survival Analysis of Epidemiological
Studies. Published online 2021. https://CRAN.R-project.org/package=powerSurvEpi
25. Latouche A, Porcher R, Chevret S. Sample size formula for proportional hazards modelling of competing risks. Stat Med.
2004;23(21):3263-3274. doi:10.1002/sim.1915
26. Huang Y, Alsabbagh MhdW. Estimates of population-based incidence of malignant arrhythmias associated with medication use—a
narrative review. Fundam Clin Pharmacol. 2020;34(4):418-432. doi:10.1111/fcp.12578
27. Blanch B, Sweeting J, Semsarian C, Ingles J. Routinely collected health data to study inherited heart disease: a systematic review
(2000–2016). Open Heart. 2017;4(2):e000686. doi:10.1136/openhrt-2017-000686
28. Murthi M, Shaka H, El-amir Z, et al. Association of hypocalcemia with in-hospital mortality and complications in patients with acute
pulmonary embolism: results from the 2017 Nationwide Inpatient Sample. BMC Pulm Med. 2021;21(1):410. doi:10.1186/s12890-021-01784-0
29. Basnet S, Dhital R, Tharu B, Ghimire S, Poudel DR, Donato A. Influence of abnormal potassium levels on mortality among hospitalized heart
failure patients in the US: data from National Inpatient Sample. J Community Hosp Intern Med Perspect. 2019;9(2):103-107.
doi:10.1080/20009666.2019.1593778
30. Zipursky J, Macdonald EM, Hollands S, et al. Proton Pump Inhibitors and Hospitalization with Hypomagnesemia: A Population-Based
Case-Control Study. PLOS Med. 2014;11(9):e1001736. doi:10.1371/journal.pmed.1001736
31. Elixhauser A, Steiner C, Palmer L. Clinical Classifications Software (CCS). U.S. Agency for Healthcare Research and Quality. Published 2018.
Accessed May 29, 2024. https://hcup-us.ahrq.gov/tools_software.jsp
32. Tanaka Y, Shah NS, Passman R, Greenland P, Lloyd‐Jones DM, Khan SS. Trends in Cardiovascular Mortality Related to Atrial Fibrillation in the
United States, 2011 to 2018. J Am Heart Assoc. 2021;10(15):e020163. doi:10.1161/JAHA.120.020163
33. Tischer TS, Schneider R, Lauschke J, et al. Prevalence of Atrial Fibrillation in Patients with High CHADS 2 ‐ and CHA 2 DS 2 VAS c ‐Scores:
Anticoagulate or Monitor High‐Risk Patients? Pacing Clin Electrophysiol. 2014;37(12):1651-1657. doi:10.1111/pace.12470
34. Andrade S, Harrold L, Tjia J, Gurwitz J. Mini-Sentinel Systematic Evaluation of Health Outcome of Interest Definitions for Studies Using
Administrative Data: Cerebrovascular Accident/Transient Ischemic Attack Report. Published online 2011.
https://www.sentinelinitiative.org/sites/default/files/surveillance-tools/validations-literature/Mini-Sentinel-HOI-Evidence-Review-CVA-TIA-
Report_0.pdf
42

35. Keller K, Hobohm L, Münzel T, Ostad MA. Impact of symptomatic atherosclerosis in patients with pulmonary embolism. Int J Cardiol.
2019;278:225-231. doi:10.1016/j.ijcard.2018.12.019
36. Krogager ML, Søgaard P, Torp‐Pedersen C, Bøggild H, Gislason G, Kragholm K. Impact of Plasma Potassium Normalization on Short‐Term
Mortality in Patients With Hypertension and Hyperkalemia. J Am Heart Assoc. Published online December 15, 2020. doi:10.1161/JAHA.120.017087
37. Glasheen WP, Cordier T, Gumpina R, Haugh G, Davis J, Renda A. Charlson Comorbidity Index: ICD-9 Update and ICD-10 Translation. Am Health
Drug Benefits. 2019;12(4):188-197.
43

12. Appendices
Appendix A: List of abbreviations
Abbreviation
Definition
ASD
Absolute standardized difference
CARE
Coalition to Advance Real-World Evidence through Randomized Controlled Trial Emulation
CDK4/6
Cyclin-dependent kinase 4/6
CNS
Central nervous system
CONSORT
Consolidated Standards of Reporting Trials
CTCAE
Common Terminology Criteria for Adverse Events
CYP3A4
Cytochrome P450 3A4
DBC
Database connection
ECG
Electrocardiogram
ECOG
Eastern Cooperative Oncology Group
EHR
Electronic health record
ER+
Estrogen receptor-positive
ERBB2
V-erb-b2 avian erythroblastic leukemia viral oncogene homolog 2
FDA
Food and Drug Administration
HER2-
Human epidermal growth factor receptor 2-negative
IPTW
Inverse probability of treatment weighting
NCI
National Cancer Institute
NLP
Natural Language Processing
OMOP
Observational Medical Outcomes Partnership
44

Abbreviation
Definition
PD-0332991
Palbociclib
PFS
Progression-free survival
PS
Propensity score
QTc
Heart-rate corrected QT interval
RCT
Randomized controlled trials
RECIST
Response Evaluation Criteria in Solid Tumours
RWD
Real-world data
RWE
Real-world evidence
rwPFS
Real-world progression-free survival
TdP
Torsade de Pointes
U.S.
United States
45

Appendix B: Preliminary feasibility assessment
STEP 1: STATE RESEARCH AIM, QUESTION, AND OBJECTIVES
STEP 1a: Overarching research aim
To emulate the PALOMA-2 randomized controlled trial of letrozole + palbociclib as first-line treatment in postmenopausal women with estrogen
receptor-positive/human epidermal growth factor receptor 2-negative advanced breast cancer using real-world data.
STEP 1b: Trial research question
Among adult postmenopausal women with estrogen receptor-positive/human epidermal growth factor receptor 2-negative advanced breast cancer, does
initial treatment with letrozole + palbociclib compared with treatment with letrozole alone result in longer progression-free survival?
STEP 1c: Trial primary objective(s)
Among adult postmenopausal women with estrogen receptor-positive/human epidermal growth factor receptor 2-negative advanced breast cancer,
compare progression-free survival for patients treated with palbociclib and letrozole and patients treated with letrozole alone.
DESIGN ELEMENTS
STEP 2:
DESCRIBE ORIGINAL
CLINICAL TRIAL
STEP 3:
DESCRIBE REAL-WORLD DATA STUDY EMULATION OF
ORIGINAL CLINICAL TRIAL
3a. Minimal criteria for
valid operationalization in
real-world data source
3b. Criteria ranking with
regard to uniqueness
and importance
Data Source
OVERALL RATING
5
GENERAL
Sample size
Trial sample size
1.5x trial sample size
2
Must Have
Sample size among adult patients with metastatic
breast cancer treated with first-line letrozole or
letrozole + palbociclib
3
Treated
444
666
1,148
5
Comparator
222
333
1,086
5
Length and
frequency of
follow-up¹
Median reported
follow-up: 23 months
(range not reported)
Sufficient time coverage in
dataset to identify outcome
after receipt of treatment
Must Have
Earliest metastatic diagnosis date:
Q3 1980
End of data cut: Q3 2023
Not
Applicable
4
VARIABLE-RELATED
46

Variable
Trial criterion
Minimal criteria for valid
operationalization in any
real-world data source
based on routine clinical
care
Criteria ranking with
regard to uniqueness
and importance
Operationalization and coverage in
data source
Rating
Treatment
125 mg of palbociclib per
day, administered orally
in 4-week cycles (3
weeks of treatment
followed by 1 week off);
2.5 mg of letrozole per
day, administered orally
(continuous treatment)
Date of palbociclib and
letrozole treatment
Must Have
Date of palbociclib and letrozole
treatment is available. Lines of
therapy are available.
5
Comparator
Placebo once per day,
administered orally in
4-week cycles (3 weeks
of treatment followed by 1
week off); 2.5 mg of
letrozole per day,
administered orally
(continuous treatment)
Date of letrozole treatment
Must Have
Date of letrozole treatment is
available. Lines of therapy are
available.
5
Inclusion Criterion 1
Women, 18 years of age
or older
Year of birth; sex
Must Have
Year of birth and sex are available.
5
Inclusion Criterion 2
Estrogen
receptor-positive (ER+),
human epidermal growth
factor receptor
2-negative (HER2-)
advanced breast cancer
Diagnosis of breast cancer;
date of
advanced/metastatic
diagnosis; date and result
of biomarker tests
Must Have
Breast cancer diagnosis, date of
metastatic diagnosis, and dates and
results of biomarker tests are
available. Date of advanced
diagnosis is not available.
4
47

Variable
Trial criterion
Minimal criteria for valid
operationalization in any
real-world data source
based on routine clinical
care
Criteria ranking with
regard to uniqueness
and importance
Operationalization and coverage in
data source
Rating
Inclusion Criterion 3
No prior systemic therapy
for advanced disease
Names/types and dates of
antineoplastic treatment;
date of
advanced/metastatic
diagnosis
Must Have
Dates of antineoplastic treatment are
available. Lines of therapy are
available.
5
Inclusion Criterion 4
Postmenopausal status:
Prior bilateral
oophorectomy,
spontaneous cessation of
menses for 12
consecutive months or
more, or
follicle-stimulating
hormone and estradiol
levels in postmenopausal
ranges without an
alternative cause
Diagnosis of menopause or
related symptoms; curated
post-menopausal status;
date of last menstrual
period
Nice to Have
Post-menopausal status is available.
5
Inclusion Criterion 5
Adequate organ function
Dates and results of
laboratory tests for
hematologic, hepatic, and
renal function.
Nice to Have
Laboratory test results and dates are
available.
5
Inclusion Criterion 6
ECOG performance
status of 0 to 2
ECOG performance status
result
Must Have
ECOG or Karnofsky performance
status is available.
5
Inclusion Criterion 7
Measurable disease
according to RECIST v1.1,
or lesions only in the bone
RECIST assessment is not
performed in routine care
Not Applicable
RECIST is not used to assess
progression or response in a
real-world setting. Progression will be
assessed with available real-world
information (see below).
Not
Applicable
48

Variable
Trial criterion
Minimal criteria for valid
operationalization in any
real-world data source
based on routine clinical
care
Criteria ranking with
regard to uniqueness
and importance
Operationalization and coverage in
data source
Rating
Exclusion Criterion 1
Prior adjuvant or
neoadjuvant treatment
with a nonsteroidal
aromatase inhibitor was
allowed unless disease
had recurred while the
patient was receiving the
therapy or within 12
months after completing
therapy
Date of surgery with
curative intent; dates of
antineoplastic treatment
Nice to Have
Dates of (neo)adjuvant and adjuvant
treatment are available, but
completeness could not be
ascertained.
4
Exclusion Criterion 2
Advanced, symptomatic,
visceral spread (i.e.,
spread to the viscera or
main organs of the body)
who were at risk for
short-term,
life-threatening
complications
Life threatening
complication in the short
term is a qualitative
assessment made by a
physician and is not
routinely available in
medical records.
Not Applicable
Receipt of a study treatment will be
assumed to indicate the clinician
determined patient is not at risk of
life-threatening complication in the
short term. Per National
Comprehensive Cancer Network
(NCCN) guidelines, study treatments
are not approved for palliative intent,
but may be used in the real-world
setting.
Not
Applicable
Primary Outcome 1
(Definition &
Ascertainment)
Progression-free survival
Date of death; curated
progression variable;
imaging results; dates of
healthcare interactions
Must Have
Curated progression information is
available. Date of death is available.
Date of last activity is available.
4
Confounding
Variable 1
Not applicable in a
randomized setting
Age
Must Have
Year of birth is available.
5
Confounding
Variable 2
Not applicable in a
randomized setting
Sex
Must Have
Sex is available.
5
Confounding
Variable 3
Not applicable in a
randomized setting
Race/ethnicity
Must Have
Race/ethnicity is available.
5
49

Variable
Trial criterion
Minimal criteria for valid
operationalization in any
real-world data source
based on routine clinical
care
Criteria ranking with
regard to uniqueness
and importance
Operationalization and coverage in
data source
Rating
Confounding
Variable 4
Not applicable in a
randomized setting
Performance status
Must Have
ECOG or Karnofsky performance
status is available.
5
Confounding
Variable 5
Not applicable in a
randomized setting
Smoking status
Must Have
Smoking status is available.
5
Confounding
Variable 6
Not applicable in a
randomized setting
Progression/disease
free-interval (Time from
initial diagnosis to
advanced/metastatic
diagnosis)
Must Have
Date of initial and metastatic
diagnosis are available. Date of
advanced diagnosis is not available.
4
Confounding
Variable 7
Not applicable in a
randomized setting
Year of study treatment
initiation
Must Have
Year of study treatment initiation is
available.
5
Confounding
Variable 8
Not applicable in a
randomized setting
Number and/or location(s)
of metastatic sites
Must Have
Metastatic site location is available.
Number of metastatic sites can be
determined.
5
Abbreviations: ECOG = Eastern Cooperative Oncology Group; ER+ = estrogen receptor positive; HER2- = human epidermal growth factor receptor 2-negative; NCCN = National Comprehensive Cancer
Network; Q1/Q2/Q3/Q4 = quarter of year (Q1: January - March, Q2: April - June, Q3: July - September, Q4: October - December); RECIST = Response Evaluation Criteria in Solid Tumors.
Footnotes:
1.
Follow-up time is stated as reported in the trial publication. Maximum available observation time is reported for the real-world data source. These are not directly comparable.
2.
The minimum sample size for feasibility analyses was selected to account for expected attrition when all eligibility criteria are applied.
3.
Sample sizes were calculated only among patients with metastatic breast cancer because curated dates of advanced breast cancer diagnosis are not available and line of therapy is defined
relative to metastatic diagnosis in these data sources.
4.
The final study period would be defined in the study protocol based on the date of treatment approval and relevant updates to treatment guidelines.
Keys for Ranking
Scoring for Data Sources by Data Elements
Scoring
Description
1
Data Requirements are not met
2
50

3
Some data requirements are met
4
5
All or nearly all data requirements are met
51

Appendix C: Code lists
Design Element
Variable
Code Type
Code
Inclusion
Non-adenocarcinoma histology
Diagnosis name
Intraductal carcinoma, non infiltrating, NOS
Infiltrating duct mixed with other types of carcinoma (C50)
Inflammatory carcinoma (C50)
Comedocarcinoma, NOS (C50)
Cribriform carcinoma, NOS
Cribriform carcinoma in situ (C50)
Papillary carcinoma, NOS
Intraductal micropapillary carcinoma (C50)
Comedocarcinoma, non infiltrating (C50)
Paget disease, mammary (C50)
Infiltrating lobular mixed with other types of carcinoma (C50)
Neuroendocrine carcinoma, NOS
Encapsulated papillary carcinoma with invasion (C50)
Inclusion
Bilateral surgical oophorectomy
Surgery name
Bilateral salpingo-oophorectomy with omentectomy, total abdominal
hysterectomy and radical dissection for debulking
Bilateral salpingo-oophorectomy with omentectomy, total abdominal
hysterectomy and radical dissection for debulking; with pelvic
lymphadenectomy and limited para-aortic lymphadenectomy
Bilateral salpingo-oophorectomy with total omentectomy, total
abdominal hysterectomy for malignancy
Total abdominal hysterectomy with bilateral salpingo-oophorectomy
Resection (initial) of ovarian, tubal or primary peritoneal malignancy
with bilateral salpingo-oophorectomy and omentectomy; with
radical dissection for debulking (ie, radical excision or destruction,
intra-abdominal or retroperitoneal tumors)
Resection (initial) of ovarian, tubal or primary peritoneal malignancy
with bilateral salpingo-oophorectomy and omentectomy; with total
abdominal hysterectomy, pelvic and limited para-aortic
lymphadenectomy
Laparoscopic bilateral salpingo-oophorectomy
Inclusion
Absolute neutrophil count (ANC)
Lab test name
Neutrophils [#/volume] in Blood
Neutrophils [#/volume] in Blood by Automated count
Neutrophils [#/volume] in Blood by Manual count
Unit
x10(3)/mcL
cells/uL
10*9 cells/L
52

Design Element
Variable
Code Type
Code
10*9/L
Inclusion
Platelets
Lab test name
Platelets [#/volume] in Blood
Platelets [#/volume] in Blood by Automated count
Platelets [#/volume] in Blood by Estimate
Platelets [#/volume] in Blood by Manual count
Unit
10*9 cells/L
x10(3)/mcL
Inclusion
Hemoglobin
Lab test name
Hemoglobin [Mass/volume] in Blood
Hemoglobin [Mass/volume] in Blood by calculation
Hemoglobin [Mass/volume] in Arterial blood
Hemoglobin [Mass/volume] in Venous blood
Unit
g/dL
Inclusion
Creatinine clearance
Lab test name
Creatinine [Mass/volume] in Blood
Creatinine [Mass/volume] in Serum or Plasma
Unit
mg/dL
Inclusion
Serum total bilirubin
Lab test name
Bilirubin.total [Mass/volume] in Serum or Plasma
Bilirubin.total [Mass/volume] in Blood
Unit
mg/dL
Inclusion
Gilbert’s disease
ICD-10 code
E80.4
Inclusion
AST (SGOT)
Lab test name
Aspartate aminotransferase [Enzymatic activity/volume] in Serum or
Plasma
Aspartate aminotransferase [Enzymatic activity/volume] in Serum or
Plasma by With P-5'-P
Unit
U/L
IU/L
Inclusion
ALT (SGPT)
Lab test name
Alanine aminotransferase [Enzymatic activity/volume] in Blood
Alanine aminotransferase [Enzymatic activity/volume] in Serum or
Plasma by With P-5'-P
Alanine aminotransferase [Enzymatic activity/volume] in Serum or
Plasma by No addition of P-5'-P
Alanine aminotransferase [Enzymatic activity/volume] in Serum or
Plasma
Unit
U/L
IU/L
Inclusion
Alkaline phosphatase
Lab test name
Alkaline phosphatase [Enzymatic activity/volume] in Serum or Plasma
Alkaline phosphatase [Enzymatic activity/volume] in Blood
Unit
U/L
53

Design Element
Variable
Code Type
Code
IU/L
Exclusion
CYP3A4 inhibitors
Generic name
Amprenavir
Atazanavir
Boceprevir
Clarithromycin
Conivaptan
Delavirdine
Diltiazem
Erythromycin
Fosamprenavir
Indinavir
Itraconazole
Ketoconazole
Lopinavir
Mibefradil
Miconazole
Nefazodone
Nelfinavir
Posaconazole
Ritonavir
Saquinavir
Telaprevir
Telithromycin
Verapamil
Voriconazole
Exclusion
Major surgery
Surgery name
Modified radical mastectomy
Partial mastectomy
Partial mastectomy with axillary lymphadenectomy
Simple mastectomy
Prophylactic mastectomy
Excision of internal mammary lymph nodes
Excision of sentinel lymph node
Radical mastectomy
Subcutaneous mastectomy
Total mastectomy and axillary clearance
Hysterectomy
Ligation of fallopian tube
Simple mastectomy with axillary lymph node sampling
54

Design Element
Variable
Code Type
Code
Exclusion
Long or short QT syndrome
26
ICD-10 code
I45.8
Exclusion
Brugada syndrome
27
ICD-10 code
I49.8
Exclusion
Torsade de Pointes (TdP)
ICD-10 code
I47.21
Exclusion
Hypocalcemia
28
ICD-10 code
E83.51
Exclusion
Hypokalemia
29
ICD-10 code
E87.6
Exclusion
Hypomagnesemia
30
ICD-10 code
E83.42
Exclusion
Myocardial infarction
ICD-10 code
I21
I22
I25.2
Exclusion
Severe/unstable angina
ICD-10 code
I20.0
Exclusion
Cardiac dysrhythmias
31
ICD-10 code
I47.0
I47.1
I47.2
I47.9
I48.0
I48.1
I48.2
I48.3
I48.4
I48.91
I48.92
I49.1
I49.2
I49.3
I49.40
I49.49
I49.5
I49.8
I49.9
R00.0
R00.1
R00.2
Exclusion
Atrial fibrillation
32
ICD-10 code
I48
Exclusion
Symptomatic congestive heart
failure
33
ICD-10 code
I50.9
Exclusion
Cerebrovascular accident
34
ICD-10 code
I60
I61
55

Design Element
Variable
Code Type
Code
I62
I63
H34.1
G45
Exclusion
Symptomatic pulmonary
embolism
35
ICD-10 code
I26
Exclusion
Inflammatory bowel disease or
chronic diarrhea
36
ICD-10 code
K50
Exclusion
Inflammatory bowel disease or
chronic diarrhea
ICD-10 code
K51
Exclusion
Short bowel syndrome
ICD-10 code
K90.82
Exclusion
Human immunodeficiency virus
infection
37
SNOMED code
91947003
445945000
Covariate
Disease site: visceral
Diagnosis name
Metastatic malignant neoplasm to liver
Metastatic malignant neoplasm to left lung
Metastatic malignant neoplasm to pleura
Metastatic malignant neoplasm to lung
Metastatic malignant neoplasm to right lung
Metastatic malignant neoplasm to respiratory tract
Covariate
Prior adjuvant or neoadjuvant
chemotherapy
Therapy name
Capecitabine
Carboplatin
Cyclophosphamide
Docetaxel
Doxorubicin
Epirubicin
Etoposide
Fluorouracil
Gemcitabine
Methotrexate
Oxaliplatin
Paclitaxel
Pemetrexed
Covariate
Prior adjuvant hormone
Therapy name
Anastrozole
Exemestane
Fulvestrant
Goserelin
Letrozole
56

Design Element
Variable
Code Type
Code
Leuprolide
Megestrol
Tamoxifen
Toremifene
Triptorelin
57