Comparative Assessment of Overall Survival in Medicare Patients With HR+/HER2- Metastatic Breast Cancer Treated With Palbociclib in Combination With Aromatase Inhibitor (AI vs. AI Alone)
Conditions: Breast Cancer
Sponsor: Pfizer

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PFIZER CONFIDENTIAL
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Comparative assessment of overall 
survival in Medicare patients with 
HR+/HER2− metastatic breast cancer 
treated with palbociclib in combination 
with aromatase inhibitor (AI vs. AI alone)
This document contains confidential information belonging to Pfizer. Except as otherwise agreed to in writing, 
by accepting or reviewing this document, you agree to hold this information in confidence and not copy or 
disclose it to others (except where required by applicable law) or use it for unauthorized purposes. In the event 
of any actual or suspected breach of this obligation, Pfizer must be promptly notified.

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1
TABLE OF CONTENTS
1
TABLE OF CONTENTS............................................................................................3
2
LIST OF ABBREVIATIONS.......................................................................................5
3
RESPONSIBLE PARTIES..........................................................................................7
4
ABSTRACT............................................................................................................8
5
AMENDMENTS AND UPDATES..............................................................................8
6
MILESTONES........................................................................................................8
7
RATIONALE AND BACKGROUND ...........................................................................9
8
OBJECTIVES AND RESEARCH QUESTIONS ............................................................10
9
RESEARCH METHODS .........................................................................................10
9.1
Study Design...............................................................................................................10
9.2
Setting ........................................................................................................................ 12
9.2.1
Inclusion Criteria............................................................................................ 12
9.2.2
Exclusion Criteria........................................................................................... 13
9.3
Variables.....................................................................................................................13
9.4
Data Source ................................................................................................................19
9.5
Study Size....................................................................................................................20
9.6
Data Management......................................................................................................21
9.6.1
Documentation of SAS Programming............................................................ 21
9.6.2
Validation of SAS Programs...........................................................................21
9.6.3
Log Review.....................................................................................................22
9.6.4
Review of Data Listings and Tables of Summary Statistics ........................... 22
9.6.5
Validation Documentation ............................................................................22
9.6.6
Database Storage and Retention ..................................................................22
9.7
Data Analyses .............................................................................................................22
9.7.1
Analyses of the Primary Objective ................................................................ 23
9.7.2
Analyses for Exploratory Objectives.............................................................. 24
9.7.3
Sensitivity and Subgroup Analyses................................................................ 24
9.8
Quality Control ...........................................................................................................25
9.9
Strengths and Limitations of Research Methods .......................................................25
9.10
Other Aspects.............................................................................................................26
10
PROTECTION OF HUMAN SUBJECTS....................................................................26

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10.1
Patient Information....................................................................................................27
10.2
Patient Consent..........................................................................................................27
10.3
Patient Withdrawal ....................................................................................................27
10.4
Institutional Review Board/Independent Ethics Committee .....................................27
10.5
Ethical Conduct of the Study......................................................................................27
11
MANAGEMENT AND REPORTING OF ADVERSE EVENTS/ADVERSE REACTIONS .....27
12
PLANS FOR DISSEMINATING AND COMMUNICATING STUDY RESULTS.................28
13
REFERENCES ......................................................................................................29
14
LIST OF TABLES ..................................................................................................32
15
LIST OF FIGURES.................................................................................................32
ANNEX 1.
LIST OF STANDALONE DOCUMENTS........................................................33
ANNEX 2.
ADDITIONAL INFORMATION...................................................................33

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2
LIST OF ABBREVIATIONS
Abbreviation
Term
1L
first line
2L
second line
ACS
American Community Survey
AI
aromatase inhibitor
AJCC
American Joint Committee on Cancer
CDK4/6
cyclin-dependent kinase 4 and 6
CFR
Code of Federal Regulations
CI
confidence interval
CPH
Cox proportional hazards
EOD
Extent of Disease
ER
estrogen receptor
ET
endocrine therapy
HCPCS
Healthcare Common Procedure Coding System
HER2
human epidermal growth factor receptor 2
HER2–
human epidermal growth factor receptor 2–negative
HMO
health maintenance organization
HR+
hormone receptor–positive
IEC
independent ethics committee
IPTW
inverse probability of treatment weighting
IRB
institutional review board
LIS
low-income subsidy
mBC
metastatic breast cancer
N/A
not applicable
NAACCR
North American Association of Central Cancer Registries
NCI
National Cancer Institute
NCICI
National Cancer Institute Comorbidity Index
NHIA
NAACCR Hispanic Identification Algorithm
NOS
not otherwise specified
OQ
Office of Quality
OS
overall survival

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A b bre vi ati o n
Ter m
P R
pr o gester o ne rece pt or
P S
pr o pe nsit y sc ore
P S M
pr o pe nsit y sc ore matc hi n g
R P S F T
Ra n k Preser vi n g Str uct ural Fail ure Ti me
R TI -H S
R TI Healt h S ol uti o ns
S E E R
S ur veilla nce, E pi de mi ol o g y, a n d E n d Res ults
sI P T W
sta bilize d i n verse pr o ba bilit y of treat me nt wei g hti n g
S S A
S ocial Sec urit y A d mi nistrati o n
U S
U nite d States
 
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4
ABSTRACT
Not applicable.
5
AMENDMENTS AND UPDATES
Amendment 
number
Date
Protocol 
section(s) 
changed
Summary of 
amendment(s)
Reason
6
MILESTONES
Milestone
Planned date/anticipated timeline
Kickoff meeting
3 May 2023
Acquire study data
Quarter 3, 2023
Final study protocol
Quarter 3, 2023
Data analysis
Quarter 2, 2024
Prepare study report
Quarter 4, 2024

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7
RATIONALE AND BACKGROUND
Nearly 70% of all metastatic breast cancer (mBC) cases are of the hormone receptor–positive 
(HR+)/human epidermal growth factor receptor 2–negative (HER2−) subtype. Endocrine therapies 
(ETs), such as aromatase inhibitors (AIs), tamoxifen, and fulvestrant, have remained the mainstay of 
therapy for patients with HR+/HER2−mBC for nearly 2 decades; however, in recent years, several 
newer targeted therapies, particularly the class of cyclin-dependent kinase 4 and 6 (CDK4/6) 
inhibitors, have become available: palbociclib (February 2015), ribociclib (March 2017), and 
abemaciclib (September 2017). Phase 3 clinical studies have shown that CDK4/6 inhibitors used in 
combination with an AI in first line (1L) in HR+/HER2−mBC significantly increased progression-
free survival compared with AI monotherapy. However, evidence on the secondary endpoint of 
overall survival (OS) from the clinical studies is mixed across different CDK4/6 inhibitors. In the 
phase 3 trial PALOMA‐2, palbociclib + letrozole in the 1L setting demonstrated a numerical 
improvement but not a statistically significant OS increase, with median OS of 53.9 months for 
palbociclib + letrozole versus 51.2 months for letrozole alone (hazard ratio, 0.956; 95% confidence 
interval [CI], 0.78-1.18) (Finn et al., 2022). Likewise, in PALOMA-3, treatment with palbociclib +
fulvestrant, after progression or relapse during previous ET, yielded a clinically important but not 
statistically significant survival improvement over fulvestrant monotherapy (median OS, 39.7 vs. 
29.7 months; hazard ratio, 0.81; 95% CI, 0.64-1.03) (Turner et al., 2018). In a post hoc analysis of 
PALOMA trials, time to chemotherapy initiation was considerably longer for patients treated with
palbociclib + letrozole (in PALOMA-2) and palbociclib + fulvestrant (in PALOMA-3) versus the 
respective non-palbociclib control groups (Rugo et al., 2022b). Further, an exploratory analysis of 
PALOMA-3 trial patients showed that patients who had not been treated with a prior chemotherapy 
for advanced breast cancer had improved OS with the palbociclib combination therapy versus the 
control group (median OS, 39.7 vs. 29.5 months; hazard ratio, 0.75; 95% CI, 0.56-1.01) (Rugo et al., 
2021).
Data from at least 2 observational studies have shown possible OS benefits in relatively older patient 
populations. Results from P-Reality-X, a real-world evidence study conducted using data from the 
Flatiron electronic health record (median age, 70 years for palbociclib treatment group), also reported 
improved OS, specifically for patients receiving palbociclib + AI compared with AI alone. After 
stabilized inverse probability treatment weighting, median OS (95% CI) is significantly longer among 
palbociclib compared with AI recipients (49.1 [45.2-57.7] vs. 43.2 [37.6-48.0] months; hazard ratio, 
0.76 [95% CI, 0.65-0.87]; P < 0.0001) (Rugo et al., 2022a). In another study of older patients (median 
age, 74 years for both the treatment arms: palbociclib + letrozole and letrozole alone), the palbociclib 
group was shown to have significantly better OS than the letrozole alone group (hazard ratio, 0.55 
[95% CI, 0.42-0.72]; P < 0.001) (Rugo et al., 2023). In a recent observational study of an Medicare 
population of patients with diagnosis of de novo HR+/HER2−mBC using data from the Surveillance, 
Epidemiology, and End Results (SEER)–Medicare during an early period of CDK4/6 inhibitors use 
(2015-2017), Goyal et al. (2023) analyzed the effect of the CDK4/6 inhibitors (as a class) on OS and 
reported that combination therapy with ET and a CDK4/6 inhibitor is independently associated with 
improved OS compared with ET alone (hazard ratio, 0.59; 95% CI, 0.42-0.82). The majority (≥90%) 

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of patie nts i n t he C D K 4/ 6 i n hi bit or gr o u p ( me dia n a ge , 7 0 yea rs) i n t his st u d y were ta ki n g pal b ocicli b
(Tra pa ni a n d Ma yer, 2 0 2 3 ).
Beca use t he e vi de nce fr o m real-w orl d p o p ulati o ns are ofte n u n derre prese nte d i n cli nical trials (Le wis 
et al., 2 0 0 3 ; L oree et al., 2 0 1 9 ; M urt h y et al., 2 0 0 4 ; Talaric o et al., 2 0 0 4 ), a n d beca use 2 a d diti o nal 
years of S E E R -Me dicare
data ha ve n o w bec o me a vaila ble all o wi n g f or m ore sa m ple size a n d 
a d diti o nal f oll o w - u p we see k t o u n dersta n d c urre nt data o n p ote ntial s ur vi val be nefits a m o n g t he 
pri mar y c o h ort of patie nts treate d wi t h pal b ocicli b + AI c o m pare d wit h AI al o ne after de n o v o m B C
dia g n osis . A descri pti o n of t he S E E R -Me dicare data base is pr o vi de d i n Secti o n 9. 4 .
8
O BJ E C TI V E S A N D R E S E A R C H Q U E S TI O N S
F oll o wi n g t he w or k of G o yal et al. ( 2 0 2 3) , t his retr os pecti ve, n o ni nter ve nti o nal st u d y ai ms t o assess 
O S i n Me dicare
patie nts wit h H R +/ H E R 2 − de n o v o m B C w h o i nitiate d 1 L treat me nt wit h pal b ocicli b 
+ AI c o m bi nati o n t hera p y vers us AI al o ne . S pecificall y, t he ke y o bjecti ves of t his st u d y are as 
f oll o ws:
Pri m ar y o bjecti ve :

T o c o m pare O S of 1 L pal b ocicli b + AI vers us AI al o ne f or w o me n or me n wit h de n o v o 
H R +/ H E R 2 – m B C i n t he S E E R-Me dicare data base w h o are ≥6 5 years ol d
E x pl or at or y o bjecti ve s:

T o meas ure ti me t o c he m ot hera p y, if e n o u g h f oll o w -u p ti me is a vaila ble t o assess t his 
meas ure.
9
R E S E A R C H M E T H O D S
9. 1
St u d y D esi g n
T his st u d y will e nc o m pass a retr os pecti ve c o h ort a nal ysis usi n g cli nical a n d a d mi nistrati ve clai ms 
data fr o m t he li n ke d S E E R -Me dicare data base
( pr o vi di n g S E E R data are a vaila ble t hr o u g h 2 0 1 9 a n d 
li n ke d Me dicare clai ms are a vaila ble t hr o u g h 2 0 2 0) f or patie nts wit h dia g n osis of H R +/ H E R 2−m B C
at i nitial prese ntati o n. T he st u d y will assess t he rates of O S i n patie nts 6 5 yea rs or ol der wit h 
dia g n osis of de n o v o m B C fr o m 2 01 5- 2 0 1 9 w h o i nitiate d 1 L s yste mic t hera p y wit h pal b ocicli b + AI 
 
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9.2
Setting
9.2.1
Inclusion Criteria
The following inclusion criteria will be applied:
1.
De novo metastatic (stage IV) disease at initial breast cancer diagnosis during 2015-2019
(Howlader et al., 2018):
!
Incident breast cancer diagnosis will be identified using the SEER Site Recode 26000,
which includes cases classified as “breast” according to the International Classification 
of Diseases for Oncology, 3rd Edition codes: C50.0-C50.9, excluding histology codes 
9050-9055, 9140, and 9590-9992 (National Cancer Institute, 2023b).
!
Staging at diagnosis will be determined using criteria applicable during the specific 
period. Specifically, the American Joint Committee on Cancer (AJCC) staging 7th
edition variable will be used to classify diagnoses in year 2015, the derived SEER 
combined stage group variable will be used to classify diagnoses in years 2016-2017, and 
the derived Extent of Disease (EOD) stage group variable will be used to classify 
diagnoses in years 2018-2019.
2.
Age ≥65 years at diagnosis
3.
“Age” as the reason for Medicare entitlement
4.
Breast cancer recorded as the first cancer (coded as tumor sequence “1”) or the only cancer 
(coded as tumor sequence “0”) confirming no evidence of another, previous primary cancer 
diagnosis.
5.
HR+/HER2−molecular subtype at diagnosis, using the SEER breast subtype variable 
(defined according to estrogen receptor [ER]/progesterone receptor [PR] and HER2 status)
6.
Initiated 1L systemic therapy with a CDK4/6 inhibitor + AI or AI alone
!
Subsequently, and differing from Goyal et al. (2023), to assess the primary objective, the 
study cohort will be restricted to patients initiating 1L therapy with palbociclib + AI or 
AI alone within 6 months of mBC diagnosis:
o
It is assumed that most patients will initiate a systemic treatment within a few 
months of the diagnosis, and those who did not receive treatment within 6 months
may have meaningful unobserved characteristics that likely influenced their 
treatment decisions.

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7.
T o e ns ure c o m plete data f or i de ntif yi n g t he 1 L of t hera p y, patie nts will be re q uire d t o ha ve
c o nti n u o us e nr oll me nt i n Me dicare Parts A a n d B a n d ha ve prescri pti o n dr u g c o vera ge ( Part 
D) wit h n o H M O partici pati o n fr o m date of dia g n osis u ntil t he y i nitiate t he 1 L t hera p y , a n d 
t he y are all o we d u p t o 6 m o n t hs t o i nitiate first-li ne t hera p y.
8.
C o nti n u o u sl y e nr olle d i n Me dicare Parts A a n d B wit h n o H M O partici pati o n f or ≥6 m o n t hs 
bef ore t he i n de x date

Beca use H M Os are n ot o bli gate d t o pr o vi de i nf or mati o n a b o ut healt h care e nc o u nters 
(i.e., a d mi nistrati ve pr oce d ure a n d dia g n osis c o des) t o t he Me dicare s ys te m, patie nts w h o 
were e nr olle d i n a n H M O d uri n g st u d y peri o d ma y ha ve i nc o m plete data o n ser vice 
utilizati o n, w hic h ma y affect t he acc urac y of meas ure me nt f or c o m or bi d c o n diti o ns a n d 
recei pt of s yste mic t hera p y li nes.
9. 2. 2
E xcl usi o n Crit eri a
Patie nts will be e xcl u de d if t heir m B C dia g n osis was first rec or de d i n a deat h certificate or at t he ti me 
of a ut o ps y .
9. 3
Va ri a bl es
All rele va nt st u d y varia bles will be gat here d or calc ulate d fr o m ra w varia bles c o ntai ne d i n t he S E E R -
Me dicare data base. T he f oll o wi n g secti o ns list a n d defi ne t he a ntici pate d ke y a nal ysis varia bles t hat 
will be c ollecte d or create d fr o m t he st u d y data base. F or c o variates, cate g ories ma y be f urt her 
c om bi ne d if a p pr o priate f or statistical p ur p oses.
T a bl e 1.
St u d y  O ut c o m e s a n d Str atifi c ati o n V ari a bl e s w it h As s o ci at e d 
D efi niti o n s
V ari a ble n a me
T y pe
Defi niti o n/c ate g ories
O utc o me v ari a bles
O verall s ur vi val
O utc o me ( pri mar y
o bjecti ve )
Ti me i n m o nt hs fr o m t he st u d y i n de x date t o all-
ca use deat h
 
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V ari a ble n a me
T y pe
Defi niti o n/c ate g ories
Ti me t o 
c he m ot hera p y
O utc o me (e x pl orat or y 
o bjecti v e)
Ti me i n m o nt hs fr o m t he st u d y i n de x date t o first 
i nitiati o n of c he m ot hera p y i n a n y s u bse q ue nt 
t hera p y li nes
E x p os ure v ari a ble
1 L T reat me nt t y pe
E x p os ure ( pri mar y 
o bjecti ve )
Patie nts i nitiati n g 1 L t hera p y wit h o ne of t he 
f oll o wi n g re gi me ns (t o be i de ntifie d i n t he 
Prescri pti o n Dr u g E ve nt file usi n g t he ge neric 
dr u g na mes a n d H C P C S pr oce d ure c o des, as 
a p plica ble):

P al b ocicli b + AI

AI  al o ne
AI  will i ncl u de t he f oll o wi n g i n di vi d ual a ge nts:

A nastr oz ole ( H C P C S c o de: S 0 1 7 0)

Letr oz ole ( H C P C S c o de: N/ A)

E x e mesta ne ( H C P C S c o de: S 0 1 5 6)
T her a p y li ne defi niti o n: F or eac h patie nt, t he 
1 L t hera p y will be defi ne d as all a ge nts 
a d mi nistere d wit hi n a 6 0- da y peri o d after 
treat me nt i nitiati o n. T he 1 L t hera p y will start o n 
t he date t he first a ge nt will be a d mi nistere d ( or 
fille d) a n d will be c o nsi dere d t o ha ve e n de d u p o n 
occ urre nce of t he earliest of a n y of t he f oll o wi n g: 
treat me nt s witc h t o a ne w re gi me n or a ga p of 
m ore t ha n 9 0 da y s i n a d mi nistrati o n or refill of 
a n y of t he 1 L re gi me n dr u gs. T he start a n d e n d 
dates of t he sec o n d a n d s u bse q ue nt t hera p y li nes 
will be defi ne d i n a si milar fas hi o n. T he r ules 
 
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Variable name
Type
Definition/categories
defining “treatment switch to a new regimen” are 
presented in Annex 2.
Subsequent 
treatment
Exposure (exploratory 
objective)
All subsequent treatment lines for patients 
initiating 1L therapy with palbociclib + AI or AI 
alone, including a description of treatment switch 
from AI alone to palbociclib + AI.
Control variables or covariates
Tumor grade at 
diagnosis
Clinical characteristic
(assessed at mBC
diagnosis)
The tumor grade will be summarized as below:
!
Grade 1 (lowest grade; well 
differentiated)
!
Grade 2
!
Grade 3
!
Grade unknown
Metastatic site 
involvement at 
diagnosis 
Clinical characteristic
(assessed at mBC
diagnosis)
The SEER-defined metastatic site variables will 
be used to describe involvement of any of the 
following sites at diagnosis:
!
Bone
!
Brain
!
Liver
!
Lung
!
An additional variable will be created to 
describe presence of visceral metastases 
using the composite of liver and lung.
National Cancer 
Institute’s (NCI)
Comorbidity Index 
(NCICI) Score
Clinical characteristic
(assessed within the 
6 months period before 
index date)
The comorbidity burden during the 6-month 
baseline period will be assessed using the NCI’s 
comorbidity index, which is described in the 
Klabunde adaptation of the Charlson 
Comorbidity Index, version 2021 (Klabunde et 
al., 2007; National Cancer Institute, 2023a). 
NCICI scores will be reported as continuous and 
categorical variables:
!
Score 0
!
0 < Score ≤1
!
Score > 1
The specific comorbidities represented in the 
NCICI include the following:
!
Acute myocardial infarction

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Variable name
Type
Definition/categories
!
History of myocardial infarction
!
Congestive heart failure
!
Peripheral vascular disease
!
Cerebrovascular disease
!
Chronic obstructive pulmonary disease
!
Dementia
!
Paralysis (hemiplegia or paraplegia)
!
Diabetes without complications
!
Diabetes with complications
!
Renal disease
!
Mild liver disease
!
Moderate/severe liver disease
!
Peptic ulcer disease
!
Rheumatologic disease
!
AIDS
Age
Demographics
(assessed at mBC
diagnosis and at index 
date)
Age at diagnosis will be assessed as continuous 
and categorical variables (categories may be 
collapsed based on final sample size):
!
65-69 years
!
70-74 years
!
75-79 years
!
≥80 years
Year of diagnosis
Demographics
(assessed at mBC
diagnosis)
Year of de novo mBC diagnosis will be 
described as a categorical variable:
!
2015
!
2016
!
2017
!
2018
!
2019

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Variable name
Type
Definition/categories
Race
Demographics
(assessed at mBC
diagnosis)
Race will be defined using the SEER Race 
Recode variable and categorized as follows:
!
American Indian/Alaska native
!
Asian or Pacific Islander
!
Black
!
White
!
Other or unknown
These categories may be further combined for 
statistical purposes as below:
!
White
!
Non-White
Origin/ethnicity
Demographics
(assessed at mBC
diagnosis)
Ethnicity will be defined using the SEER Origin 
Recode variable defined by the NAACCR 
Hispanic Identification Algorithm (NHIA) and 
categorized as follows:
!
Hispanic (Spanish-Hispanic-Latino)
!
Non-Hispanic (non-Spanish-Hispanic-
Latino)
Race-ethnicity
Demographics
(assessed at mBC
diagnosis)
Race will be defined using the SEER Race 
Recode variable, and ethnicity will be defined 
using the SEER Origin Recode variable defined 
by the NAACCR Hispanic Identification 
Algorithm (NHIA) and categorized as follows:
!
American Indian/Alaska native/unknown
!
Hispanic
!
Non-Hispanic Asian or Pacific Islander
!
Non-Hispanic Black
!
Non-Hispanic White
Marital status
Demographics
(assessed at mBC
diagnosis)
Marital status will be categorized as follows:
!
Married
!
Not married
o
Single (never married)
o
Unmarried living with domestic 
partner
o
Divorced
o
Separated

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Variable name
Type
Definition/categories
o
Widowed
!
Unknown
These categories may be further combined for 
statistical purposes as below:
!
Single (never married)
!
Married/unmarried living with domestic 
partner
!
Divorced/separated/widowed
!
Unknown
Geographic region
Demographics
(assessed at mBC
diagnosis)
The geographic region of patients’ county of 
residence will be defined according to the rural-
urban continuum code (version year 2013) and 
categorized as follows:
!
Big metro: Counties in metropolitan 
areas ≥1 million population
!
Metro: Counties in metropolitan areas of 
250,000 to 1 million population
!
Urban: Counties in metropolitan areas 
of < 250,000 population
!
Less urban: Nonmetropolitan counties 
adjacent to a metropolitan area
!
Rural: Nonmetropolitan counties not 
adjacent to a metropolitan area
These categories may be further combined for 
statistical purposes as below:
!
Large urban (big metro and metro areas)
!
Small urban (urban and less urban areas)
!
Rural

A 5 4 8 1 1 8 2
N O N -I N T E R V E N TI O N A L S T U D Y P R O T O C O L
Fi n al Pr ot oc ol, Versi o n 1. 0 , 0 6 Se pte m ber 2 0 2 3
P FI Z E R C O N FI D E N TI A L
C T 2 4 -WI -G L 0 2 -R F 0 2 2. 0 N o n -I nterve nti o n al St u dy Pr ot oc ol Te m pl ate F or Sec o n d ary D at a C ollecti o n St u dy
0 1 -J u n-2 0 2 0 
Pa ge 1 9 of 3 6
V ari a ble n a me
T y pe
Defi niti o n/c ate g ories
H o use h ol d i nc o me
De m o gra p hics
(assesse d usi n g t he 
latest a vaila ble A C S 
data: 2 0 0 8 - 2 0 1 2)
T he me dia n h o use h ol d i nc o me base d o n patie nt s’
ce ns us tract of resi de nce will be defi ne d 
acc or di n g t o A merica n C o m m u nit y S ur ve y data 
( 2 0 0 8-2 0 1 2). Data will be cate g orize d usi n g 
q uartile distri b uti o n as f oll o ws:

Q uartile 1 ( e. g., < $ 5 5, 0 0 0)

Q uartile 2 ( e. g., $ 5 5, 0 0 0- $ 6 4, 9 9 9)

Q uartile 3 ( e. g., $ 6 5, 0 0 0- $ 7 4, 9 9 9)

Q uartile 4 ( e. g., $ 7 5, 0 0 0 +)

Missi n g/ u n k n o w n 
LI S c o vera ge
De m o gra p hics
(assesse d d uri n g 
6 m o n t hs bef ore i n de x 
date )
Patie nt’ s e nr oll me nt i n an LI S pla n d uri n g t he 6 -
m o nt h baseli ne peri o d will be assesse d a n d 
s u m marize d as a bi nar y varia ble:

A n y LI S c o vera ge

N o LI S c o vera ge
Me dicai d ( d ual ) 
eli gi bilit y
De m o gra p hics
(assesse d as a vaila ble i n 
t he data at m B C
dia g n osis or treat me nt )
Me dicare wit h Me dicai d ( d ual) eli gi bilit y stat us 
will be s u m marize d acc or di n g t o pri mar y pa yer/
i ns ura nce carrier at t he ti me of i nitial dia g n osis 
a n d/ or treat me nt at t he re p orti n g facilit y :

Me di cai d

N o Me dicai d
1 L = first li ne; 2 L = sec o n d li ne; A C S = A merica n C o m m u nit y S ur ve y; AI = ar o matase i n hi bit or; 
C D K 4/ 6 = c ycli n-de pe n de nt ki nase 4 a n d 6; H C P C S = Healt hcare C o m m o n Pr oce d ure C o di n g 
S yste m; LI S = l o w-i nc o me s u bsi d y; m B C = metastatic breast ca ncer; N/ A = n ot a p plica ble; 
N A A C C R = N ort h A merica n Ass ociati o n of Ce ntral Ca ncer Re gistries; N CI = Nati o nal Ca ncer 
I nstit ute; N CI CI = N CI C o m or bi dit y I n de x; N HI A = N A A C C R His pa nic I de ntificati o n Al g orit h m; 
; S E E R = S ur veilla nce, E pi de mi ol o g y, a n d E n d Res ults; 
.
9. 4
D a t a S o ur c e
T he S E E R -Me dic are
d ata base is c o m p ose d of 2 lar ge p o p ulati o n -base d s o urces of data t hat pr o vi de 
detaile d i nf or mati o n a b o ut Me dicare be neficiaries wit h ca ncer: ( 1) t he S E E R re gistr y data t hat c o ntai n 
i nf or mati o n a b o ut a n i nci de nt ca ncer dia g n osis a n d ( 2) li n ke d Me dicare data t hat c o ntai n l o n git u di nal 
i nf or mati o n o n Me dicare e nr oll me nt a n d s pecific healt hcare ser vices recei ve d i n differe nt setti n gs 
(e. g., h os pitals, p h ysicia n offices, o ut patie nt cli nics, n ursi n g h o mes) , as well as prescri pti o n dr u g 
utilizati o n. T he li n ka ge of t hese 2 data s o urces res ults i n a u ni q ue s o urce of i nf or mati o n t hat ca n be 
use d f or a n arra y of e pi de mi ol o gical researc h, i ncl u di n g healt h ser vices use a n d s ur vi val o utc o mes, 
a m o n g ol der patie nts wit h ca ncer i n t he U nite d States ( U S ) Me dicare p o p ulati o n.
 
C CI
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The SEER registries are part of the SEER Program, which is an epidemiologic surveillance system 
consisting of population-based tumor registries in certain states and metropolitan areas designed to 
track cancer incidence and survival in the US. The SEER registries collect information on nearly all 
(98%) newly diagnosed cancer cases, including breast cancer, in persons residing in SEER areas. 
SEER areas have been shown to be nationally representative (Nattinger et al., 1997) and the 21 
registries represented in the SEER data linkage available for this study (SEER-21) capture over one-
third (35%) of the total US population (Enewold et al., 2020). In contrast to the entire population of 
the US, the SEER Program encompasses a population that displays greater racial and ethnic diversity, 
along with a higher representation of individuals facing economic disadvantages (Enewold et al., 
2020).
The SEER registry data released as part of SEER-Medicare are included in a customized file known 
as the SEER-Medicare Cancer file. The information collected on each SEER-reported cancer 
diagnosis includes patient-level demographic characteristics, date of diagnosis, clinical data about the 
tumor (e.g., stage, grade, size, ER status, PR status, and HER2 status), and cause and date of death. 
Linkage with Medicare data provides a database of persons generally aged 65 years and older with 
detailed information on incident cancer linked with comprehensive longitudinal healthcare utilization 
data from Medicare fee-for-service. In SEER-Medicare, the Medicare date of death information is 
collected from multiple sources, including the Social Security Administration (SSA), Railroad 
Retirement Board, and Medicare Common Working File; the Medicare date of death information is 
known to be complete for the given enrollment year (e.g., 2020 enrollment data will capture deaths 
occurring in 2020). Because almost all (99%) Medicare deaths are validated (98% through SSA), the 
vital status information available in the linked SEER-Medicare data make it one of the most 
authoritative data sources to examine survival outcomes in Medicare beneficiaries with an incident of
cancer diagnosis in the US.
The linkage of SEER-Medicare data is managed by the NCI, in collaboration with the contributing 
SEER registries and the Centers for Medicare & Medicaid Services. Investigators must submit a 
detailed application to the NCI for obtaining access to the required study data, which must be used 
under the terms of a data use agreement. Based on the most recent data linkage by the NCI, released 
in December 2022, SEER data are linked with Medicare patients aged ≥65 years with an incident (de 
novo) cancer diagnosis from 1999-2019, with linked claims and survival data available through 2020. 
The reason for the 3-year gap between cancer diagnosis and integration into the SEER-Medicare 
database stems from a 2-year period required for complete case reporting to the SEER Program, 
coupled with one extra year to process the data linkage. Information contained within the linked 
Medicare claims data, including dates and types of treatment utilization (including prescription 
drugs), make it possible to retrospectively “follow” patients after the date of their initial diagnosis 
reported in the SEER registry. Additional details on the structure, content, and application of the 
SEER-Medicare data can be found in the literature (Enewold et al., 2020; Warren et al., 2002).
9.5
Study Size
Because the sample size available for analysis is dictated by eligible patients available in the study 
database, no formal sample size estimations were performed.

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9.6
Data Management
The analyses described in this proposal will be performed using a SAS statistical software application 
housed on a secure, large-capacity, high-performance Linux mainframe. Experienced programmers 
and analysts at RTI Health Solutions (RTI-HS) will perform all analyses. To ensure the integrity and 
quality of the study results, we will follow a programming validation life cycle process for all 
analyses. This includes quality-checking programs, logs, and output for accuracy according to 
relevant standard operating procedures. Because this secondary data analysis will use structured data 
only, no further human review of the raw data is planned.
To ensure the integrity and quality of study results, RTI-HS implements several practice standards for 
statistical programming, database management, and documentation for all projects involving 
databases analyses. The following 3 steps will be undertaken to achieve this high level of quality:
!
Documentation of SAS programming
!
Validation of SAS programs
!
Database storage and retention
9.6.1
Documentation of SAS Programming
To ensure smooth transitions of analytic methods and work among programmers, reviewers, and other 
project personnel, documentation of the following information will be created for each SAS program:
!
Project name
!
Program name
!
Program purpose
!
Program author
!
Date the program was completed
!
Descriptions of subsequent changes and/or enhancements, with name of programmer and 
date for each
This information will be incorporated into each program in the form of a header. In addition to 
documenting this information in a general program header, each program will include detailed comments 
throughout to describe the purpose and method of specific programming statements.
9.6.2
Validation of SAS Programs
RTI-HS employs a variety of programming validation methods, including log review, review of data 
listings, and independent programming (if needed to validate any complex coding), which will be 
used to ensure that our SAS programs function as intended. The validation methods described in this 
section are not exhaustive, and additional measures will be implemented as appropriate.

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9.6.3
Log Review
Programmers will review all SAS log files. This procedure is a widely accepted, basic level of 
program validation. The following issues must be addressed as part of a log review:
!
No errors should appear in a log file.
!
If warning messages or messages related to uninitialized variables are permitted in the 
log file, the programmer will document why they are permitted.
!
The programmer will account for the number of observations reported at each executed 
data step, especially when the number of observations increases or decreases.
!
The log file will contain all lines of the program as it was saved at the time of execution, 
and it will contain only those lines of code.
9.6.4
Review of Data Listings and Tables of Summary Statistics
Because an error-free log file does not necessarily demonstrate that a SAS program has functioned as 
intended, programmers will produce cell frequencies, means, and other summary statistics on specific 
data items to demonstrate that the program results are valid. Where appropriate, we will also have a 
separate analyst review these listings independent of the programmer.
9.6.5
Validation Documentation
For each SAS program used to produce final study outputs for presentation, RTI-HS will complete 
and store a formal SAS validation document.
9.6.6
Database Storage and Retention
RTI-HS will store in a secure location all copies of the original data files, as applicable. Data files 
stored on CD, DVD, or other media will be kept in a locked storage unit. Original data files will be 
transferred from media disks to dedicated project space on a Linux server. To ensure the integrity of 
the original files, they will be stored on our Linux server in a designated folder that cannot be 
overwritten. Data sets derived from the original files during the data cleaning process will be stored in 
a separate folder.
After project completion, all data sets (either raw or derived) used in this project will be retained for a 
period of at least 5 years, unless specific Pfizer protocols explicitly prohibit this. If any data cleaning 
activities or other analyses need to be repeated for any reason, this retention procedure will allow 
quick and efficient access to the data sets.
9.7
Data Analyses
All analyses, including computations and generation of tables, listings, and data for figures, will be 
conducted using SAS statistical software, version 9.4 or later (SAS Institute; Cary, North Carolina).

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Baseline patient demographics (e.g., age, race, marital status) and clinical characteristics 
(e.g., metastatic sites, NCICI) will be descriptively analyzed and reported. Means with standard 
deviations, medians with interquartile range, and minimum/maximum values will be provided for 
continuous variables when performing descriptive analysis of continuous data. Numbers and 
percentages will be provided for dichotomous and polychotomous variables when performing 
descriptive analysis of categorical data.
9.7.1
Analyses of the Primary Objective
For the primary objective, the association between primary independent variable (treatment type: 
palbociclib + AI or AI alone) and the outcome variable (OS) will be assessed using unadjusted 
Kaplan-Meier analysis and an appropriate multivariable method. The primary method of analysis will
be determined following an assessment of the balance in baseline characteristics between the 2
treatment groups. Specifically, we would explore the following methods: 1) multivariable Cox 
proportional hazards regression, 2) the inverse probability of treatment weighting (IPTW), and 3) 
propensity score matching (PSM). Both IPTW and PSM are propensity score (PS)-based methods
commonly used to achieve balance between treatment and control groups in retrospective cohort 
study designs. The baseline covariates or control variables that will be considered for adjustment in 
the multivariable Cox proportional hazards (CPH) regression and in logistic regression for deriving 
treatment probabilities (i.e., PSs for IPTW and PSM) are described earlier in Table 1. The 
multivariable CPH model will generate a hazard ratio, along with 95% CI, for each of the covariates 
included in the model, indicating higher or lower rate of death (relative to a reference category) at any 
given point in time following the index date. The final lists of variables to be used in the model will 
be discussed and determined during analysis development and will be recorded in the SAP prior to 
final analysis.
The general approach for an IPTW analysis involves calculating the inverse probability weights and 
then using these weights to estimate the treatment effect. As the first step, the estimated probability of 
treatment assignment will be obtained from a logistic regression model where the treatment 
assignment (treatment vs. control) will be regressed on a set of covariates or confounders. The 
treatment weights derived from the PS might exhibit instability due to the fact that patients receiving 
palbociclib + AI treatment could have PS closer to 0, while patients receiving AI monotherapy might 
have scores closer to 1. Consequently, extreme weights and inflated variance can occur. To mitigate 
this potential problem, we will calculate stabilized IPTW (sIPTW), as suggested by Pezzi et al. 
(2016), Xu et al. (2010), and Shiba and Kawahara (2021). By employing sIPTW, the point estimates 
of the treatment effect will remain unaffected, but it can lead to a more accurate estimation of the 
variance. Once the stabilized inverse probability weights are calculated for each individual, they will 
be used to estimate the average treatment effect by comparing the weighted outcomes between the 
treatment (palbociclib + AI) and control (AI alone) groups.
In the PSM technique, each subject in the palbociclib + AI cohort will be matched to a subject in the AI 
alone cohort using a 1-to-1 matching with no replacement, using the nearest neighbor matching 
algorithm with potential use of a caliper (no larger than 0.1 standard deviation).

A 5 4 8 1 1 8 2
N O N -I N T E R V E N TI O N A L S T U D Y P R O T O C O L
Fi n al Pr ot oc ol, Versi o n 1. 0 , 0 6 Se pte m ber 2 0 2 3
P FI Z E R C O N FI D E N TI A L
C T 2 4 -WI -G L 0 2 -R F 0 2 2. 0 N o n -I nterve nti o n al St u dy Pr ot oc ol Te m pl ate F or Sec o n d ary D at a C ollecti o n St u dy
0 1 -J u n-2 0 2 0 
Pa ge 2 4 of 3 6
C o variate bala nce will be e xa mi ne d bef ore a n y a dj ust me n t f or t he P S a n d after a dj usti n g f or t he P S 
(usi n g b ot h sI P T W a n d P S M tec h ni q ues). If n o si g nifica nt differe nces are o bser ve d a m o n g all 
baseli ne meas ures bet wee n t he treat me nt gr o u ps, t he treat me nt effect t hat is calc ulate d fr o m t he 2
met h o ds base d o n t he wei g hte d a n d matc he d p o p ulati o n ca n be c o nsi dere d t o be t he tr ue effect. 
S u bse q ue ntl y, de pe n di n g o n t he q ualit y of bala nce, t he e n d p oi nt ( O S) ma y be assesse d usi n g o ne or 
b ot h tec h ni q ues , i n a d diti o n t o t he m ulti varia ble C P H re gressi o n a nal ysis (A usti n, 2 0 1 4 ; B uc ha na n et 
al., 2 0 1 4 ).
F or t he assess me nt of O S, Ka pla n -Meier a nal ysis ma y als o be perf or me d usi n g t he sta bilize d wei g hts 
or a m o n g t he P S matc he d gr o u ps, a n d t he wei g hte d or P S -matc he d s ur vi val c ur ves ma y be dra w n. 
S u bse q ue ntl y, C o x re gressi o n a nal ysis ma y be perf or me d usi n g t he sta bilize d wei g hts or a m o n g t he 
P S -matc he d gr o u ps . P oi nt esti mate (i.e., hazar d rati o) will be deri ve d , as a p plica ble, al o n g wit h 
9 5 % CI a n d P val ue usi n g r o b ust varia nce esti mati o n . A n y c o variates wit h resi d ual i m bala nce ma y be 
a dj uste d f or i n t his ste p.
All patie nts will be f oll o we d fr o m t he start of t hera p y li ne t hr o u g h deat h or t he e n d of a vaila ble st u d y 
f oll o w-u p, w hic he ver is earliest. Secti o n 9. 7. 3 disc usses p ote ntial se nsiti vit y a nal yses; e. g., a dj usti n g 
f or s u bse q ue nt treat me nts recei ve d.
9. 7. 2
A n al ys es f or E x pl or at or y O bj ecti v es
E x pl orat or y st u d y meas ures 
 
 
 
9. 7. 3
S e nsiti vit y a n d S u b gr o u p A n al ys es
T o test f or t he r o b ust ness of t he res ults f or t he pri mar y o bjecti ve , se nsiti vit y a nal yses will be 
perf or me d, w hic h ma y i n v ol ve rea nal ysis of t he data usi n g a n alter nate met h o d ol o g y t o t hat e m pl o ye d 
i n t he pri mar y met h o d of a nal ysis. F or i nsta nce, after t he assess me nt of q ualit y of c o variate bala nce, if 
m ulti varia ble C o x re gressi o n is deter mi ne d t o be t he s uita ble met h o d f or pri mar y a nal ysis, P S -base d 
a nal ysis (I P T W or P S M or b ot h) ma y be perf or me d as se nsiti vit y a nal ysis t o test t he differe nce i n O S 
bet wee n t he treat me nt gr o u ps (refer t o Secti o n 9. 7. 1 f or details o n t he 3 met h o d s u n der 
c o nsi derati o n) . A n ot her se nsiti vit y a nal ysis ma y be perf or me d f or a s u bset of patie nts w h o i nitiate d 
t heir 1 L t hera p y wit hi n 3 m o n t hs of de n o v o m B C dia g n osis ( vs. wit hi n 6 m o n t hs of dia g n osis as 
pla n ne d f or t he mai n a nal ysis) . A d diti o nall y, a se ns iti vit y a nal ysis ma y be c o nsi dere d t o a dj ust f or 
s u bse q ue nt treat me nt wit h C D K 4/ 6 i n hi bit ors usi n g met h o ds s uc h as t he Ra n k Preser vi n g Str uct ural 
Fail ure Ti me ( R P S F T). F urt her, c o nsi deri n g t hat t he i nf or mati o n o n patie nts vital stat us is u p t o date 
a n d c o mplete as of t he e n d of st u d y peri o d ( Dec 2 0 2 0), t he defi niti o n of t he e n d of st u d y f oll o w -u p 
ma y be re visite d t o disre gar d t he criteria of c o nti n u o us e nr oll me nt i n Me dicare Parts A, B, a n d D a n d 
e nr oll me nt i n a n H M O; c o nse q ue ntl y, a se nsiti vit y a nal ysis ma y be perf or me d t o e xa mi ne if c ha n gi n g 
t his defi niti o n has a n y effect o n t he O S e n d p oi nt.
 
C CI

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Further, to help better explain the results and to generate additional contextual data, subgroup 
analyses may be performed as appropriate. As exploratory analyses, these subgroups may include age 
groups, metastatic sites, and other patient characteristics as appropriate (e.g., race/ethnicity, NCICI
score, dual Medicare/Medicaid eligibility), and the analyses will be conducted based on availability of 
the data and adequate sample sizes.
9.8
Quality Control
RTI-HS and its staff strive to meet the highest standards of professional performance and 
continuously improve our products and services. To ensure quality, we work with our clients to define 
requirements and clarify expectations, and we pledge that our products and services will comply with 
these requirements, meet or exceed client expectations, and deliver exceptional value.
The RTI-HS Office of Quality (OQ) is an independent unit that reports to the Vice President of 
RTI-HS and provides training on applicable regulations and guidelines, implements and maintains a 
series of standard operating procedures, and provides quality assurance monitoring for compliance 
with regulatory requirements.
RTI-HS will work closely with selected subcontractors, if applicable, to establish and ensure a 
complete integration of procedures for the project. The OQ will perform audits and assessments that 
involve various aspects of the project, including but not limited to education and training 
documentation. Audits are conducted by the OQ according to established criteria in standard 
operating procedures and other applicable procedures. The OQ reports quality assurance observations 
to the project director and facilitates corrective actions, if necessary.
9.9
Strengths and Limitations of Research Methods
This study is based on the analysis of the SEER-Medicare database—a database with a diverse array 
of demographic and clinical information on patients with incident diagnosis of cancer in the Medicare 
population in the US who are generally 65 years or older. The linkage of Medicare claims data with 
SEER registry data provides confirmatory data on cancer diagnoses along with precise dates, which in 
turn make it possible to attribute treatments as specific lines of therapy in alignment with their 
approved indications and guideline recommendations; this substantially minimizes the risk of 
misclassification—an inherent limitation in many other commonly used population-based 
administrative claims data sources in the US. The availability of up-to-date validated vital statistics 
data, as described in Section 9.4, further makes the database a unique and authoritative data resource 
for evaluation of survival outcomes in older patients with cancer, especially those represented in the 
US Medicare population.
There are certain limitations that are typically inherent in studies based on secondary data sources and 
are applicable to this current study as well:

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!
Patients are not randomized to treatment and control groups, and as such, the lack of 
random assignment may lead to selection bias. We plan to adjust for differences in 
baseline characteristics and risk factors that are observable in the database, but 
imbalances in many unobserved characteristics (e.g., performance status, laboratory test 
results, genetic mutations) may introduce unmeasured confounding.
!
The US-labelled indication for palbociclib includes patients who were initially diagnosed 
at an earlier stage but later progressed to have advanced or metastatic disease; however, 
this analysis will be restricted to patients with de novo metastatic disease given limitation 
of the data source. This could potentially limit the generalizability of study findings
beyond patients who are not diagnosed at the metastatic stage, but would be an 
appropriate approach in consideration of the fact that SEER does not collect information 
about progression or recurrences, and proxy methods to identify recurrences in the 
SEER-Medicare database are shown to have low sensitivity and specificity, especially 
among older patients (Hassett et al., 2014; Warren et al., 2016).
!
Patients on Medicare Advantage or HMO plans cannot be analyzed because Medicare 
data do not contain comprehensive claims records for these patients, who make up 32%
to 42% of the Medicare population, with higher uptake in certain geographic regions,
potentially affecting generalizability of our findings.
!
The planned survival analysis will be based on an intent-to-treat approach, regardless of 
changes to their exposure status during the follow-up, which may influence the survival
outcome.
!
Because the treatment regimens will be defined using a claims-based algorithm, it is 
reasonable to assume some risk of misclassification—even though the algorithm rules 
will be drawn from previously published methods.
The SEER-Medicare database used for this study, however, remains a unique—and the most 
authoritative—data source to examine survival outcomes in older patients with diagnosis of cancer at 
the US Medicare population level.
9.10 Other Aspects
Not Applicable.
10 PROTECTION OF HUMAN SUBJECTS
This study will be subjected to a project-level evaluation by the institutional review board (IRB) at 
RTI International, of which RTI-HS is a business unit. RTI International holds a Federal-Wide 
Assurance (#3331) from the Department of Health and Human Services’ Office for Human Research 
Protections that allows us to review and approve human subjects protocols through these IRB 

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committees. The IRB has been audited by the Food and Drug Administration and is fully compliant 
with applicable regulatory requirements. The committee reviews research studies to ensure adherence 
to appropriate regulations that govern human subjects research, including 45 CFR 46 and 21 CFR 50 
and 56, and to all applicable Helsinki Declarations and provisions of the International Council for 
Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. All studies involving 
human subjects undergo a continuing IRB review at least once per year. Depending on the level of 
risk and nature of the research, a study may be ruled as exempt from IRB review by an IRB chair or 
designated IRB member. Studies that are not exempt must be approved either by an IRB chair or 
designated IRB member (if the study qualifies for expedited review) or by a full IRB committee. 
Because this study involves use of existing, secondary data with no more than minimal risk to the 
patient, a waiver of informed consent will be requested.
10.1 Patient Information
Because the data used in this study are deidentified and anonymous, this study poses minimal risk to 
patients whose medical data are analyzed.
10.2 Patient Consent
Not applicable.
10.3 Patient Withdrawal
Not applicable.
10.4 Institutional Review Board/Independent Ethics Committee
There must be prospective approval of the study protocol, protocol amendments, and other relevant 
documents (e.g., informed consent forms, if applicable) from the relevant IRBs or independent ethics 
committees (IECs). All correspondence with the IRB or IEC must be retained. Copies of IRB or IEC 
approvals must be forwarded to Pfizer.
10.5 Ethical Conduct of the Study
Not applicable.
11 MANAGEMENT AND REPORTING OF ADVERSE EVENTS/ADVERSE 
REACTIONS
This study involves data that exist as structured data by the time of study start. In these data sources, 
individual patient data are not retrieved or validated, and it is not possible to link (i.e., identify a 
potential association between) a particular product and medical event for any individual. Thus, the 

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minimum criteria for reporting an adverse event (AE) (i.e., identifiable patient, identifiable reporter, a 
suspect product, and event) cannot be met.
12 PLANS FOR DISSEMINATING AND COMMUNICATING STUDY 
RESULTS
Upon study completion and finalization of the study report, the results of this noninterventional study
will be submitted for publication (abstract, manuscript, or both). Publications will comply with 
internal Pfizer standards and the recommendations of the International Committee of Medical Journal 
Editors.

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R u g o H S, Br ufs k y A, Li u X , Li B, Mc R o y L, C he n C, et al. Real-w orl d st u d y of o verall s ur vi val wit h 
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R u g o H S, Crist ofa nilli M, L oi bl S, Har bec k N, De Mic hele A, I wata H, et al. Pr o g n ostic Fact ors f or 
O verall S ur vi val i n Patie nts wit h H or m o ne Rece pt or -P ositi ve A d va nce d Breast Ca ncer: A nal yses 
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Rugo HS, Liu X, Li B, McRoy L, Layman RM, Brufsky A. Real-world comparative effectiveness of 
palbociclib plus letrozole versus letrozole in older patients with metastatic breast cancer. Breast. 
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Shiba K, Kawahara T. Using Propensity Scores for Causal Inference: Pitfalls and Tips. J Epidemiol. 
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Turner NC, Slamon DJ, Ro J, Bondarenko I, Im SA, Masuda N, et al. Overall Survival with 
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Warren JL, Mariotto A, Melbert D, Schrag D, Doria-Rose P, Penson D, et al. Sensitivity of Medicare 
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2016 Aug;54(8):e47-54. doi:http://dx.doi.org/10.1097/MLR.0000000000000058. 
Xu S, Ross C, Raebel MA, Shetterly S, Blanchette C, Smith D. Use of stabilized inverse propensity 
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14
LIST OF TABLES
Table 1.
Study Outcomes and Stratification Variables With Associated Definitions............... 13
15
LIST OF FIGURES
Figure 1.
Study Design Schematic (Depiction for a Hypothetical Patient)................................ 11

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ANNEX 1.
LIST OF STANDALONE DOCUMENTS
Not applicable.
ANNEX 2.
ADDITIONAL INFORMATION
Rules for Defining Change in Therapy Line
Condition
Rule
Switch to a completely new agent or combination of agents
Change in therapy line
Switching between CDK4/6 inhibitors (e.g., palbociclib to 
ribociclib or abemaciclib)
Change in therapy line
Therapy suspended for greater than 90 days
Change in therapy line
Addition of a new agent to an existing treatment regimen*
Change in therapy line
Removal of an agent from a combination regimen
Not a change in therapy line
Exception to switching within the same treatment class
Anastrozole to letrozole (or vice versa)
Not a change in therapy line
Anastrozole to exemestane (or vice versa)
Not a change in therapy line
Letrozole to exemestane (or vice versa)
Not a change in therapy line
1L = first line; CDK4/6 = cyclin-dependent kinase 4 and 6.
*A treatment regimen is defined as combination of all agents administered within a 30-day period 
from the date of initiation of the first agent, with the exception of CDK4/6 inhibitors, for which a 
60-day period will be allowed to account for insurance coverage delays. Addition of a new agent 
after 30 days (or 60 days for CDK4/6 inhibitors) will be considered a change in therapy line. A 
given therapy line will be considered to have ended upon occurrence of the earliest of any of the 
following: switch to a new treatment regimen or a gap of more than 90 days in administration or 
refill of any of the 1L regimen drugs.

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List of Systemic Therapy Agents
Therapy agents
HCPCS codes
CDK4/6 inhibitors
Palbociclib
N/A
Ribociclib
N/A
Abemaciclib
N/A
Aromatase inhibitors
Anastrozole
S0170
Letrozole
N/A
Exemestane
S0156
Other hormonal therapy
Fulvestrant
J9393, J9394, J9395, C9120
Tamoxifen
S0187
Toremifene
N/A
Kinase inhibitor
Everolimus
J7527, J8561
Specific chemotherapy agents
Doxorubicin (all forms)
C9415, J9000, J9001, J9002, Q2048, Q2049, Q2050
Epirubicin 
C1167, J9178, J9180
Eribulin
C9280, J9179
Gemcitabine
J9198, J9201
Capecitabine
J8520, J8521
Docetaxel 
J9170, J9171
Paclitaxel (all forms)
C9127, C9431, J9264, J9265, J9267, J9259
Cyclophosphamide 
C9420, C9087, C9421, J8530, J9070, J9071, J9080, J9090, J9091, 
J9092, J9093, J9094, J9095, J9096, J9097
Fluorouracil 
J9190
Cisplatin 
C9418, J9060, J9062
Carboplatin 
J9045

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Therapy agents
HCPCS codes
Oxaliplatin 
C9205, J9263
Methotrexate
J8610, J9250, J9260
Vinorelbine
C9440, J9390
Vinblastine
J9360
Leuprolide
C9430, J1950, J9217, J9218, J9219, J1952
Irinotecan
C9474, J9205, J9206
Ixabepilone
C9240, J9207
Etoposide
C9414, C9425, J8560, J9181, J9182
Mitomycin
C9432, J9280, J9290, J9291, J9281, C9064
NOS chemotherapy agent
J8999, J9999
Other targeted therapies
Olaparib
N/A
Alpelisib
N/A
Lapatinib
N/A
Tucatinib
N/A
Neratinib
N/A
Larotrectinib
N/A
Entrectinib
N/A
Talazoparib
N/A
Trastuzumab (all forms)
C9131, J9316, J9354, J9355, J9356, J9358, Q5112, Q5113, 
Q5114, Q5116, Q5117
Pertuzumab
C9292, J9306, J9316
Bevacizumab
Q2024, C9257, C9214, Q5118, Q5107, C9142, Q5126, Q5129, 
S0116, J9035
Pembrolizumab
C9027, J9271
Atezolizumab
C9483, J9022
Sacituzumab
C9066, J9317
Nivolumab
C9453, J9299, J9298

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CDK4/6 = cyclin-dependent kinase 4 and 6; HCPCS = Healthcare Common Procedure Coding 
System; N/A = not available; NOS = not otherwise specified.
Note: All treatments will be identified using a combination of HCPCS codes and the generic drug 
names in the Medicare Prescription Drug Event file that are linked to corresponding National Drug 
Codes assigned to each drug. Treatments with no applicable HCPCS codes will only be identified 
using generic drug names.