Real-World Medication Adherence Trajectories to Nintedanib Among Idiopathic Pulmonary Fibrosis Patients
Conditions: Idiopathic Pulmonary Fibrosis
Sponsor: Boehringer Ingelheim

ABCD
xx / Saved on: 8/6/2021
Non-Interventional Study (NIS) Protocol 
TITLE PAGE
Document Number:
c39084239-01
BI Study Number:
1199-0471
BI Investigational
Product(s):
Nintedanib
Title:
Real-World Medication Adherence Trajectories to Nintedanib among 
Idiopathic Pulmonary Fibrosis Patients
Brief lay title:
Adherence to Nintedanib among IPF Patients
Protocol version 
identifier:
1.0
Date of last version of 
protocol:
Not applicable
PASS:
No
EU PAS register 
number:
Will be registered
Active substance:
Not applicable
Medicinal product:
Nintedanib (Ofev)
Product reference:
Not applicable
Procedure number:
Not applicable
Marketing 
authorisation holder(s):
Not applicable
Joint PASS:
No
Research question and 
objectives:
1)
Identification of adherence patterns of nintedanib among idiopathic 
pulmonary fibrosis (IPF) patients.
2)
Understanding characteristics of patients within each nintedanib 
adherence trajectory among IPF patients. 
Country(-ies) of study:
United States
Author:

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Marketing 
authorisation holder(s):
NA
Date:
12 May 2022
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© 2022 Boehringer Ingelheim Group of companies. All rights reserved.
This document may not - in full or in part - be passed on, reproduced, published or otherwise used without prior written permission

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1.
TABLE OF CONTENTS
TITLE PAGE.............................................................................................................................1
1.
TABLE OF CONTENTS...................................................................................................3
2.
LIST OF ABBREVIATIONS............................................................................................5
3.
RESPONSIBLE PARTIES................................................................................................6
4.
ABSTRACT.......................................................................................................................7
5.
AMENDMENTS AND UPDATES.................................................................................10
6.
MILESTONES.................................................................................................................11
7.
RATIONALE AND BACKGROUND............................................................................12
8.
RESEARCH QUESTION AND OBJECTIVES .............................................................14
9.
RESEARCH METHODS ................................................................................................15
9.1
STUDY DESIGN..........................................................................................15
9.2
SETTING ......................................................................................................16
9.2.1
Study sites......................................................................................................16
9.2.2
Study population............................................................................................16
9.2.3
Study visits ....................................................................................................17
9.2.4
Study discontinuation....................................................................................17
9.3
VARIABLES.................................................................................................17
9.3.1
Exposures ......................................................................................................17
9.3.2
Outcomes.......................................................................................................17
9.3.2.1
Primary outcomes ........................................................................................17
9.3.3
Covariates......................................................................................................18
9.4
DATA SOURCES.........................................................................................25
9.5
STUDY SIZE ................................................................................................25
9.6
DATA MANAGEMENT..............................................................................26
9.7
DATA ANALYSIS .......................................................................................26
9.7.1
Main analysis.................................................................................................26
9.7.2
Additional analysis........................................................................................27
9.8
QUALITY CONTROL .................................................................................28
9.9
LIMITATIONS OF THE RESEARCH METHODS....................................28
9.10
OTHER ASPECTS........................................................................................29
9.10.1
Data quality assurance...................................................................................29

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9.10.2
Study records.................................................................................................29
10.
PROTECTION OF HUMAN SUBJECTS ..................................................................30
10.1
STUDY APPROVAL, PATIENT INFORMATION, AND INFORMED 
CONSENT
.......................................................................................................................30
10.2
STATEMENT OF CONFIDENTIALITY....................................................30
11.
MANAGEMENT AND REPORTING OF ADVERSE EVENTS/ADVERSE 
REACTIONS...........................................................................................................................31
11.1
ADVERSE EVENT AND SERIOUS ADVERSE EVENT COLLECTION 
AND REPORTING .............................................................................................................31
12.
PLANS FOR DISSEMINATING AND COMMUNICATING STUDY RESULTS .32
13.
REFERENCES ............................................................................................................33
13.1
PUBLISHED REFERENCES.......................................................................33
14.
ANNEXURES .............................................................................................................37
ANNEX 1. LIST OF STAND-ALONE DOCUMENTS.....................................................38
ANNEX 2. ENCEPP CHECKLIST FOR STUDY PROTOCOLS .....................................39
ANNEX 3. ADDITIONAL INFORMATION.....................................................................40
ANNEX 4. REVIEWERS AND APPROVAL SIGNATURES..........................................41

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2.
LIST OF ABBREVIATIONS
ADR
Adverse Drug Reaction
AE
Adverse Event
AESI
Adverse Event of Special interest
CA
Competent Authority
CCDS
Company Core Data Sheet
CI
Confidence Interval
CML
Local Clinical Monitor 
CRA
Clinical Research Associate
CRF
Case Report Form
CTCAE
Common Terminology Criteria for Adverse Events
CTP
Clinical Trial Protocol
eCRF
Electronic Case Report Form
ENCePP 
European Network of Centres for Pharmacoepidemiology and 
Pharmacovigilance 
FDA
GBTM
Food and Drug Administration
Group-Based Trajectory Model
GCP
Good Clinical Practice
GEP
Good Epidemiological Practice
GPP
Good Pharmacoepidemiology Practice
GVP
Good Pharmacovigilance Practices
IB
Investigator’s Brochure
IEC
Independent Ethics Committee
IPF
IRB
Idiopathic Pulmonary Fibrosis
Institutional Review Board
MAH
Marketing Authorization Holder
MedDRA
Medical Dictionary for Regulatory Activities
NIS
OOP
Non-Interventional Study
Out Of Pocket
PASS
PDC
Post-Authorization Safety Study
Proportions of Days Covered
SAE
Serious Adverse Event

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3.
RESPONSIBLE PARTIES
BI NIS

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4.
ABSTRACT
Name of company:
Boehringer Ingelheim
Name of finished medicinal
product:
Nintedanib (Ofev)
Name of active ingredient:
Nintedanib (Ofev)
Protocol date:
Study
number:
Version/Revision:
Version/Revision date:
12 May 2022
1199-0471
1.0
NA
Title of study:
Real-World Medication Adherence Trajectories of Nintedanib among 
Idiopathic Pulmonary Fibrosis Patients
Rationale and 
background:
Medication adherence is of great importance to health care 
stakeholders.  The most common method to measure adherence in 
real-world settings is via the proportion of days covered (PDC) in 
prescription drug claims databases.  However, PDC is limited by its 
inability to distinguish between different patterns of adherence over 
time.  This study will use group-based trajectory modeling (GBTM) 
to assess adherence trajectories to nintedanib and patient 
characteristics associated with those trajectories.  The results of this 
study can help providers/payers to better identify adults with IPF who 
are at high risk of nintedanib non-adherence. The identified non-
adherence trajectories may highlight the need for a targeted
intervention strategy to improve adherence in patients with each 
trajectory.
Research 
question and 
objectives:
Identification of adherence trajectories of nintedanib use among IPF 
patients.
Understanding characteristics of patients within each nintedanib
adherence trajectory among IPF patients. 
Study design:
This is a non-interventional, retrospective cohort study.

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Name of company:
Boehringer Ingelheim
Name of finished medicinal
product:
Nintedanib (Ofev)
Name of active ingredient:
Nintedanib (Ofev)
Protocol date:
Study
number:
Version/Revision:
Version/Revision date:
12 May 2022
1199-0471
1.0
NA
Population:
The study sample will consist of community-dwelling Medicare 
beneficiaries with IPF who initiate treatment with nintedanib.
Variables:
The main study outcome is nintedanib adherence trajectories.  
A range of predictor variables will be measured in the administrative 
data during the baseline and index date, including patient 
demographic characteristics (e.g., age, sex, race/ethnicity), Social 
Deprivation Index, residential location, clinical characteristics (e.g., 
comorbidity index, IPF-related costs and use), and all-cause health 
care costs and use.
Data sources:
This study will use administrative enrollment and claims data from 
the U.S. federal Medicare program for beneficiaries aged 65 years and 
older who were continuously enrolled in Original Medicare insurance 
coverage, including Parts A, B and D.  The study will use Medicare 
data covering the period from October 1, 2013 through December 31, 
2019. 
Study size:
The minimum needed sample size for GBTM analysis is 500. A prior study 
found 1,464 Medicare beneficiaries who initiated nintedanib between 
10/15/2014 and 12/31/2015 (Corral, Chang et al., 2020).  By covering a 
longer period with the same data, this study should have sufficient sample.
Data analysis:
In the first part, monthly trends in nintedanib PDC will be analysed via 
GBTM estimation, which assumes there is a fixed number of latent clusters 
of individuals who share a common outcome pattern.  The methodology will 
yield estimated probabilities of cluster membership for each individual and 
an estimated trajectory curve over time for each cluster.  In the second part, 
multinomial logistic models will be estimated to identify individual-level 
predictors of cluster membership.
Milestones:
The analytic database is expected to be constructed by August 30, 2021, 
pending access to the Medicare data.  Data analyses are expected to be 
completed by October 15, 2021.  A draft report of the findings from this 
study is expected to be available for review by November 15, 2021 and

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Name of company:
Boehringer Ingelheim
Name of finished medicinal
product:
Nintedanib (Ofev)
Name of active ingredient:
Nintedanib (Ofev)
Protocol date:
Study
number:
Version/Revision:
Version/Revision date:
12 May 2022
1199-0471
1.0
NA
completed by February 15, 2022. 

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5.
AMENDMENTS AND UPDATES
None.

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6.
MILESTONES
Milestone
Date
First draft of synopsis
2/1/21
VDT synopsis review
2/25/21
IET synopsis review
4/23/21
Kick-off meeting with vendor
05/07/21
NPCC synopsis review
05/21/21
Study protocol first draft
06/01/21
Study protocol VDT and NIS review 
07/22/21
  Study protocol NPCC review
08/17/21
Study protocol final draft 
08/20/21
Analytical database creation
08/30/21
Descriptive results to BI
09/15/21
GBTM results to BI
10/15/21
Final report first draft
11/15/21
Final report comments from BI
01/15/22
Final report final draft
02/15/22

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7.
RATIONALE AND BACKGROUND
Medication adherence is of great importance to health care stakeholders based on increasing 
evidence of prevalence of non-adherence to chronic medications in the United States (Desai 
et al., 2019) and increasing evidence that adherence is associated with better health outcomes 
and lower costs (Cutler et al., 2018).  There are several ways by which adherence to 
medications can be measured. Each of these methods has its own strengths and limitations 
(Forbes et al., 2018).  Direct methods, which include blood drug assays, and direct 
observation of the patients are expensive, laborious, and time intensive (Farmer, 1999).  In 
real-world settings, use of prescription claims databases, patient self-report, pill counts, and 
electronic monitoring are the common measures of adherence (Lam and Fresco, 2015).  The 
most common method to measure adherence in real-world settings is via the proportion of 
days covered (PDC) in prescription drug claims databases (Harbaugh and Cooper, 2018).  
PDC has been defined as the number of days supplied of a specific medication in the follow-
up period divided by the total number of days during follow-up, multiplied by 100 to yield a 
percentage from 0%–100% (Raebel et al., 2013).
Although PDC is useful in that it offers in a single number a metric of medication adherence 
over a predefined period of time, it is also limited because it does not distinguish between 
different patterns of adherence over that same period (Alhazami et al., 2020).  For example, a 
patient who took medications every other day for a year would have a PDC of 50%; likewise, 
a patient who took the medication for the first 6 months uninterrupted and then not again for 
the remainder of the year would also have a PDC of 50%.  Distinguishing these different 
patterns is critical to understanding adherence to prescription medication and designing 
interventions to improve adherence.
One alternative is to detect trajectories of adherence over time across patients using group-
based trajectory modeling (GBTM) (Nagin and Odgers, 2010).  GBTM has been applied with 
increasing frequency for this purpose in several conditions (Alhazami et al., 2020; Bateman 
et al., 2016; Dillon et al., 2018; Feldman et al., 2018; Franklin et al., 2018; Franklin et al., 
2015; Franklin et al.; 2013; Hargrove et al., 2017; Juarez et al., 2015; Kim et al., 2017; Li et 
al., 2014; Lo-Ciganic et al., 2016; MacEwen et al., 2016; Paranjpe et al., 2020; Vadhariya et 
al., 2019; Wang et al., 2019; Zongo et al., 2019).  In most of these studies, monthly PDC was 
assessed for each patient , and a GBTM was fit on the patient-level sequences of monthly 
PDC values to identify a best-fitting set of adherence trajectories as well as, for each patient, 
estimates of the probability of belonging to each trajectory group.
Studies that examine adherence trajectories with GBTMs frequently include analyses of the 
predictors of patients’ membership in trajectory groups.  Individual characteristics that have 
been observed to be related to adherence trajectories include age, race, sex, disease severity, 
comorbidity burden, educational attainment, and socioeconomic status (Alhazami et al., 
2020).
Adherence to nintedanib has been evaluated in a few recent studies in the United States.  
Recent observational studies of nintedanib and pirfenidone suggest that despite different side-

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effect profiles, adherence to and persistence with therapy are similar. Adherence (measured 
as PDC ≥80%) of nintedanib during one year has been estimated at 60.5% (Corral, Chang, et 
al., 2020), 51% (Corral, DeYoung, and Kong, 2020), and 71.3% (Ipatova et al., 2019).  No 
studies to our knowledge have analyzed nintedanib adherence using GBTM or comparable 
methods nor have any studies assessed predictors of adherence.
Therefore, the objective of this study is to assess nintedanib adherence trajectories using 
GBTMs and understand characteristics of patients within each trajectory. The results of this 
study can help providers and payers to better identify adults with IPF who are at high risk of 
nintedanib non-adherence. The identified non-adherence trajectories may highlight the need 
for a fit-for-targeted intervention strategy to improve adherence in patients within each 
trajectory.

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8.
RESEARCH QUESTION AND OBJECTIVES
This study has two objectives:
1. Identification of adherence trajectories of nintedanib among IPF patients.
2. Understanding characteristics of patients within each nintedanib adherence trajectory 
among IPF patients. 

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9.
RESEARCH METHODS
9.1
STUDY DESIGN
This is a non-interventional cohort study using existing administrative data from the U.S. 
Medicare program. There will be no comparison groups nor any exposure variables.  As 
shown in Figure 1, the study sample will consist of community-dwelling Medicare 
beneficiaries with IPF who initiated treatment with nintedanib between 10/01/2014 to 
12/31/2018.  To allow for a one-year baseline period and one-year follow-up period for all 
beneficiaries who initiated nintedanib between 10/01/2014 and 12/31/2018, the span of data
will be from 10/01/2013 to 12/31/2019.
Figure 1
Study design schematic

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9.2
SETTING
9.2.1
Study sites
The study will use 100% Medicare claims and enrollment data from the U.S. Medicare 
program on community-dwelling beneficiaries continuously enrolled in traditional, or fee-for-
service, Medicare insurance coverage for inpatient hospital, skilled nursing and outpatient 
facility services (Part A), physician and other professional services (Part B), and outpatient 
prescription drugs (Part D). 
The enrollment file contains monthly information on individuals’ enrollment in each part of 
Medicare, demographic information, residential location (at the 5-digit ZIP Code level), and 
date of death.  
Claims data are available for all medical services covered by the program and are organized 
into data files based on the nature and source of the claim.  The Inpatient, Outpatient, and 
SNF files include institutional claims from hospitals for inpatient and outpatient services and 
from nursing homes for short-stay “skilled” admissions, respectively.  The Carrier file 
includes fee-for-service claims submitted by professional providers, including physicians, 
physician assistants, clinical social workers, nurse practitioners.  (Claims for some 
organizational providers, such as free-standing facilities are also found in the Carrier file.  
Examples include independent clinical laboratories, ambulance providers, free-standing 
ambulatory surgical centers, and free-standing radiology centers.)  Separate files include 
claims for durable medical equipment, home health visits and hospice care.  
Pharmacy Part D claims include complete prescription drug information, and all standardized 
prescription-level fields collected on a typical pharmacy claim (e.g., date of fill or refill, drug 
name and class, strength, quantity, and days’ supply).
9.2.2
Study population
Inclusion criteria:

Newly initiated nintedanib during 10/01/2014 to 12/31/2018 (see tab “T1 ANTIFIB 
RX” in standalone document “Code_List.xlsx”)

Were at least 66 years old as of the date of their first nintedanib prescription claim 
(index date)

Qualified for Medicare based on age

Had at least 12 months of continuous enrollment in Medicare Parts A, B and D before 
(baseline period) and 12 months after the index date (follow-up period)

Had at least one inpatient or two outpatient claims (>14 days apart) with a diagnosis 
code for IPF (ICD-10-CM: J84.112; ICD-9-CM: 516.31) during the baseline period
(see tab “T3 IPF DX” in standalone document “Code_List.xlsx

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Exclusion criteria:

Had any history of pirfenidone or nintedanib use during the baseline period (see tab 
“T1 ANTIFIB RX” in standalone document “Code_List.xlsx”)

Had any history of lung transplant during the baseline, index date or follow-up 
periods (see tabs “T4 LUNG_TRANS PR” and “T5 LUNG_TRANS DX” in 
standalone document “Code_List.xlsx”)

Had any claims for skilled nursing facility, long-term care facility or hospice during 
the baseline, index date or follow-up period

Had evidence (≥2 ICD-9-CM or ICD-10-CM diagnosis codes on different dates) 
during the baseline period of any of the following conditions: lung cancer, 
autoimmune, or connective tissue diseases (i.e. rheumatoid arthritis (RA), sarcoidosis, 
systemic lupus erythematosus (SLE), dermatopolymyositis, systemic sclerosis, 
Sjogren’s, and mixed connective tissue disease (CTD)) during the baseline period
(Appendix-1)

Had dual eligibility of Medicare and Medicaid.

Had history of using pirfenidone at the same time with nintedanib during follow-up
9.2.3
Study visits
Not applicable.
9.2.4
Study discontinuation
Not applicable
9.3
VARIABLES
9.3.1
Exposures
This is a study of nintedanib adherence patterns, and all the included patients will be patients 
with a prescription claim for nintedanib.
9.3.2
Outcomes
9.3.2.1
Primary outcomes
The main outcome of this study is adherence trajectories to nintedanib. This study will use
the GBTM method to identify trajectories of adherence.
For this purpose, first claim for nintedanib will be identified in the Part D claims file based on 
the presence of an 11-digit NDC code for nintedanib (00597-0143-60, 00597-0145-60 
[CanMED, 2021]).  Nintedanib supply calendars will be constructed for the all twelve 30-day 
time intervals following the individual’s nintedanib initiation date based on each claim’s 
service date and days’ supply. 
-Supply will be assigned as 1 for each day with positive days’ supply of nintedanib.

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-If a patient has multiple prescription claims for nintedanib on the same date, the maximum 
of days’ supply will be calculated across the claims and applied to the supply calendar.  
-Stockpiling will be implemented if a patient has prescription claims for nintedanib that 
overlap.  Days’ supply for the newer claim will be postponed to the first day after end of the 
older claim’s supply.
-For each 30-day month following the nintedanib initiation date, PDC will be calculated as 
the sum of days with supply divided by 30.
In this way, we are able to calculate monthly nintedanib proportion of days covered. By using 
the twelve 30-days PDC values as outcomes in the GBTM, the probabilities that a given 
individual is in each of the latent clusters will be calculated by the GBTM estimation 
procedure. GBTMs will be estimated using the Stata plugin traj (Jones and Nagin, 2013).
9.3.3
Covariates
The index date will be defined as the date of the beneficiary’s first Part D claim for 
nintedanib.  The baseline period will consist of the 360 consecutive days ending the day 
before the index date.  The follow-up period will consist of the 360 consecutive days starting 
with the index date.  
Variable
Description
Demographic and socioeconomic characteristics
Age
Age as of index date in years rounded to the 
nearest whole number will be calculated based on 
the difference between the beneficiary’s birth date 
and index date. 
Age group
Age group will be a categorical variable for age 
defined as one of 65–74, 75–84, 85+.
Female
Female will be a binary indicator for whether the 
beneficiary was female as indicated on the 
enrollment record covering the index date.
Race
Race will be a categorical variable for the 
beneficiary’s self-reported race/ethnicity taking the 
categories Non-Hispanic White, Black or African-
American, Other, Asian/Pacific Islander, Hispanic, 
American Indian/Alaska Native, and unknown race 
from the enrollment record covering the index date.
Census region
Census region will be a categorical variable 
defined by categorizing the beneficiary’s state of 
residence into Census regions (Northeast, South, 
Midwest, West) from the enrollment record 
covering the index date (see tab “T2 STATES” in 

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Variable
Description
standalone document “Code_List.xlsx”).
Index year
Index year will be a categorical variable defined as 
the year of the index date and will take the values, 
2014, 2015, 2016, 2017 or 2018.
Social deprivation index
Social deprivation index will be a continuous 
variable ranging between 0 and 100 representing 
the Social Deprivation Index for the beneficiary’s 
residential ZIP Code as calculated by the
 
.  Social Deprivation Index “is a 
composite measure of seven demographic 
characteristics collected in the American 
Community Survey (ACS): percent living in 
poverty, percent with less than 12 years of 
education, percent single parent household, percent 
living in rented housing unit, percent living in 
overcrowded housing unit, percent of households 
without a car, and percent non-employed adults 
under 65 years of age.”  This version was 
calculated in 2015 using data from 2015 and 
updates the original version created by Butler et al. 
(2013).  Higher values for the Social Deprivation 
Index indicate more social deprivation.
Clinical characteristics
Combined comorbidity index
The combined comorbidity index will be a 
continuous comorbidity score ranging from 0 to 26 
as measured from comorbidity diagnosis codes in 
any position on inpatient, outpatient and Carrier 
claims that occurred during the baseline period.  
The combined comorbidity score is based on 
research by Gagne et al. (2011) and Sun et al. 
(2017) to identify the conditions in the union of the 
two most-popular comorbidity indexes used in 
administrative claims data, the Charlson index and 
the Elixhauser index.  For a sample of Medicare 
beneficiaries, Gagne et al. (2011) found that a 
subset of 20 conditions predicted mortality at 30, 
60, 90 and 180 days, and 1 year more accurately 
than either of the two indexes on their own.  
Moreover, unlike the Charlson and Elixhauser 
indexes, Gagne et al.’s (2011) combined index is 
not conditioned on inpatient hospital admission.  

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Variable
Description
The 20 conditions in Gagne et al.’s (2011)
combined comorbidity index (with their weights in 
parentheses) include: 
- Metastatic cancer (5), 
- Congestive heart failure (2), 
- Dementia (2), 
- Renal failure (2), 
- Weight loss (2), 
- Hemiplegia (1), 
- Alcohol abuse (1), 
- Any tumor (1), 
- Cardiac arrhythmias (1), 
- Chronic pulmonary disease (1), 
- Coagulopathy (1), 
- Complicated diabetes (1), 
- Deficiency anemia (1), 
- Fluid and electrolyte disorders (1), 
- Liver disease (1), 
- Peripheral vascular disease (1), 
- Psychosis (1), 
- Pulmonary circulation disorders (1), 
- HIV/AIDS (−1), and 
- Hypertension (−1).
Pulmonary hypertension
A binary indicator for whether the beneficiary had 
a diagnosis code for pulmonary hypertension on at 
least one inpatient, outpatient or Carrier claim that 
occurred during the baseline period (see tab “T7 
PULM_HTN DX” in standalone document 
“Code_List.xlsx”).
Gastroesophageal reflux
A binary indicator for whether the beneficiary had 
a diagnosis code for gastroesophageal reflux 
disease on at least one inpatient, outpatient or 
Carrier claim that occurred during the baseline 
period (see tab “T8 GASTR_REFLX DX” in 
standalone document “Code_List.xlsx”).
Asthma
A binary indicator for whether the beneficiary had 

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Variable
Description
a diagnosis code for asthma on at least one 
inpatient, outpatient or Carrier claim that occurred 
during the baseline period (see tab “T9 ASTHMA 
DX” in standalone document “Code_List.xlsx”).
Sleep apnea
A binary indicator for whether the beneficiary had 
a diagnosis code for obstructive sleep apnea on at 
least one inpatient, outpatient or Carrier claim that 
occurred during the baseline period (see tab “T10 
SLEEP_APNEA DX” in standalone document 
“Code_List.xlsx”).
Lung biopsy
A binary indicator for whether the beneficiary had 
a diagnosis or procedure code for a lung biopsy on 
at least one inpatient, outpatient or Carrier claim 
that occurred during the baseline period (see tab 
“T11 LUNG_BIOPSY PR” in standalone 
document “Code_List.xlsx”).
HRCT scan
A binary indicator for whether the beneficiary had 
a procedure code for a high-resolution 
computerized tomography scan on at least one 
inpatient, outpatient or Carrier claim that occurred 
during the baseline period (see tab “T12 
HRCT_SCAN PR” in standalone document 
“Code_List.xlsx”).
Oxygen
A binary indicator for whether the beneficiary had 
a diagnosis, procedure revenue center code for 
oxygen therapy or supplemental oxygen on at least 
one inpatient, outpatient, Carrier, skilled nursing 
facility, home health or durable medical equipment 
claim that occurred during the baseline period (see 
tabs “T13 OXYGEN PR”, “T14 OXYGEN REV” 
and “T15 OXYGEN DX” in standalone document 
“Code_List.xlsx”).
Pulmonary rehabilitation
A binary indicator for whether the beneficiary had a 
procedure or revenue center code for pulmonary 
rehabilitation services on at least one inpatient, 
outpatient, Carrier, skilled nursing facility, home health 
or durable medical equipment claim that occurred 
during the baseline period (see tabs “T16 
PULM_REHAB PR” and “T17 PULM_REHAB REV” 
in standalone document “Code_List.xlsx”).
Ventilator use
A binary indicator for whether the beneficiary had a 

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Variable
Description
diagnosis or procedure code for ventilator use on at 
least one inpatient, outpatient or Carrier claim that 
occurred during the baseline period (see tabs “T18 
VENT_USE PR” and “T19 VENT_USE DX” in 
standalone document “Code_List.xlsx”).
COPD
A binary indicator for whether the beneficiary had a 
diagnosis code for chronic obstructive pulmonary 
disease on at least one inpatient, outpatient or Carrier 
claim that occurred during the baseline period (see tab 
“T20 COPD DX” in standalone document 
“Code_List.xlsx”).
Hypoxia
A binary indicator for whether the beneficiary had a 
diagnosis code for hypoxia on at least one inpatient, 
outpatient or Carrier claim that occurred during the 
baseline period (see tab “T21 HYPOXIA DX” in 
standalone document “Code_List.xlsx”).
Pharmacy use and spending
Medication count
The count of the number of unique outpatient 
prescription medications for which beneficiary has Part 
D claims during the baseline period  
Total pharmacy spending
A continuous, non-negative variable representing the 
total amount paid by all parties for outpatient 
prescription medications as reported in Part D claims 
during the baseline period.
OOP pharmacy spending
A continuous, non-negative variable representing the 
total amount paid out-of-pocket by the beneficiary for 
outpatient prescription medications as reported in Part 
D claims during the baseline period.
Inpatient hospitalization use and spending
Any inpatient stay
A binary indicator for whether a beneficiary had at least 
one inpatient hospitalization for any cause during the 
baseline period.
Inpatient stay count
A count of the number of inpatient hospitalizations for 
any cause a beneficiary had during the baseline period.
Inpatient length of stay
A count of the number of days (length of stay) a 
beneficiary was hospitalized in an inpatient facility 
during the baseline period.  Length of stay for each 
inpatient hospitalization claim will be calculated as the 
arithmetic difference between each claim’s from and 
through dates.
Total inpatient spending
A continuous, non-negative variable representing the 
total amount paid by all parties for all inpatient 

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Variable
Description
hospitalizations (for any cause) as reported in inpatient 
facility claims during the baseline period. This is 
calculated as the sum of the Medicare payment amount, 
the Medicare per diem amount, the non-Medicare payer 
amount, and the patient OOP amount.
OOP inpatient spending
A continuous, non-negative variable representing the 
total amount paid out-of-pocket by the beneficiary for 
all inpatient hospitalizations (for any cause) as reported 
in inpatient facility claims during the baseline period.
Outpatient facility use and spending
Any outpatient visit
A binary indicator for whether a beneficiary had at least 
one claim for services provided by an outpatient facility 
for any cause during the baseline period.  
Outpatient visit count
A count of the number of unique dates with an 
outpatient facility for any cause claim a beneficiary had 
during the baseline period.
Total outpatient spending
A continuous, non-negative variable representing the 
total amount paid by all parties for outpatient facility 
claims during the baseline period.  This is calculated as 
the sum of the Medicare payment amount, the blood 
deductible liability amount, the non-Medicare payer 
amount, and the patient OOP amount.
OOP outpatient spending
A continuous, non-negative variable representing the 
total amount paid out-of-pocket by the beneficiary for 
services as reported in outpatient facility claims during 
the baseline period.
Home health use and spending
Any home health
A binary indicator for whether a beneficiary had at least 
one claim for home health services during the baseline 
period.
Home health count
A count of the number of home health visits a 
beneficiary had during the baseline period, calculated 
by summing the total visit count on each claim across 
claims.
Total home health spending
A continuous, non-negative variable representing the 
total amount paid by all parties for home health claims 
during the baseline period.  This is calculated as the 
sum of the Medicare payment amount, the blood 
deductible liability amount, the non-Medicare payer 
amount, and the patient OOP amount.

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Variable
Description
Part B spending
Total Part B spending
A continuous, non-negative variable representing the 
total amount paid by all parties for Carrier and Durable 
Medical Equipment claims during the baseline period, 
calculated as the sum of the allowed amounts on each 
claim.
OOP Part B spending
A continuous, non-negative variable representing the 
total amount paid out-of-pocket by the beneficiary for 
Carrier and Durable Medical Equipment claims during 
the baseline period.
Total spending
Total medical spending
A continuous non-negative variable representing the 
total amount paid by all parties for all medical services 
during the baseline period, including inpatient, 
outpatient, home health and Part B.
Total spending
A continuous non-negative variable representing the 
total amount paid by all parties for all medical and 
pharmacy claims during the baseline period and will be 
calculated as the sum of total pharmacy and total 
medical spending.
Other use
Any ED visit
A binary indicator for whether a beneficiary had at least 
one emergency department visit during the baseline 
visit.  Note that hospitals bill for emergency department 
visits on either the Inpatient or Outpatient facility 
claims, as described by
 here.  Thus, 
emergency department visits will be identified in 
inpatient and outpatient facility claims via the presence 
of revenue center codes 
 and 
.
ED visit count
A count of the number of unique dates with an 
emergency department visit that a beneficiary had 
during the baseline period.
Any pulmonology visit
A binary indicator for whether a beneficiary was treated 
by a specialist in pulmonary disease during the baseline 
period.  Treatment by a pulmonologist will be identified 
by the presence of a pulmonary specialty code in 
inpatient, outpatient and Carrier claims.
Pulmonology visit count
A count of the number of unique dates with treatment 
by a specialist in pulmonary disease that a beneficiary 
had during the baseline period.  

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Variable
Description
Pulmonology prescriber
A binary indicator for whether the clinician who 
prescribed the beneficiary’s index nintedanib 
medication was a pulmonologist, as documented in the 
Part D Prescriber Characteristics file for the index 
nintedanib claim.
Inflation Adjustment
All dollar-denominated variables will be inflated to 2019 USD using the Consumer Price 
Index, which is listed in Table 1.
Table 1
Consumer price index for medical care implicit price deflator
Year
Inflation Multiplier
2013
1.180
2014
1.152
2015
1.123
2016
1.077
2017
1.057
2018
1.038
2019
1.000
Source:  U.S. BUREAU OF LABOR STATISTICS; Bureau of Labor Statistics Data (bls.gov), July 22, 2021.
9.4
DATA SOURCES
As described above in Section 9.2.1, all variables will be constructed from the 100% 
Medicare claims and enrolment data from the U.S. Medicare program for the individuals 
meeting the sample selection criteria.  To allow for a one-year baseline period and one-year 
follow-up period for all beneficiaries who initiated nintedanib between 10/01/2014 and 
12/31/2018, the Medicare will span 10/01/2013 through 12/31/2019.
9.5
STUDY SIZE
This study will use the GBTM technique to adherence pattern. Based on empirical results 
generated by resampling of population data, the maximum likelihood estimates obtained in 
group-based trajectory models provide reasonably close estimates of their true population 
values and have approximately normal distributions, even when estimated with a sample size 
as small as n = 500 (Loughran and Nagin, 2006). A prior study found 1,464 Medicare 

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beneficiaries who initiated nintedanib between 10/15/2014 and 12/31/2015 (Corral, Chang et 
al., 2020). By covering a longer period with the same data, this study should have a sufficient 
sample.
9.6
DATA MANAGEMENT
Pursuant to requirements guiding participation in the 
 
 Innovator program, 
 staff will access the Medicare claims and 
enrolment data only through the 
.  As such, 
 will not directly possess any 
beneficiary-level 
 data, and the only data to be downloaded from the 
 will be 
summary-level statistics.
 staff will access the 
 environment via the VMware Horizon Client (or 
whichever client is mandated by 
) from dedicated 
computers.  All 
 
computers are secured with Microsoft BitLocker (for hard drive encryption at rest), Microsoft 
Azure Active Directory (to implement local security policies, including password 
management), BitDefender (anti-virus software), and LoJack (for computer tracking and 
remote wiping if ever needed).
All data manipulation and analysis will be conducted on the 
 environment 
remotely by 
 staff.  Data manipulation and analysis will be conducted using SAS 
software (
) and Stata software (
).
9.7
DATA ANALYSIS
9.7.1
Main analysis
The objective of the main analysis is to identify the adherence patterns of nintedanib among 
IPF patients. The unit of analysis will be the individual beneficiary, each of whom will have 
twelve 30-days time intervals measurments of nintedanib PDC spanning the follow-up 
period.  The 12 PDC values (PDC01–PDC12) will be characterized descriptively with 
measured of central tendency (mean, median), spread (min, max, interquartile range), and 
proportions of beneficiaries with PDC in each of 0 to <20%, 20 to <40%, 40 to <60%, 60 to 
<80%, and 80 to 100%.
GBTM will be used to explore the presence of distinct trajectories based on the change in 
medication adherence over time. The output of GBTM will include estimated probabilities of 
cluster membership for each individual and an estimated trajectory curve over time for each 
cluster. Different models will be used to identify 2 to 5 groups by using either cubic, 
quadratic, or quartic terms. The final models will be based on Bayesian information criteria, 
and Nagin's criteria for model adequacy (Nagin and Odgers, 2010), with consideration given 
to group size such that the proportion of patients in each group is not less than 5% of the total 
sample. The output of the final model will give us the estimated average trajectory for each 
group, as well as the estimated probabilities of membership in each trajectory for each 
patient. We will decide the number and shapes of trajectories based on both model fit, 

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ensuring enough class size and clinical usefulness (Hickson, et al, 2020). An example of 
using GBTM for assessing medication adherence is provided in Figure2 .
Figure 2
GBTM medication adherence example
Two sensitivity analyses will be conducted: 
-
First, the analysis will be repeated for beneficiaries who have positive PDC in the 
fourth month.  In these models, only data from months 4 through 12 will be used to 
estimate the GBTMs because PDC01–PDC03 will have been used to condition the 
estimation sample.  
-
Second, the GBTMs will be re-estimated using as the outcome continuous PDC (for 
the same twelve30-day time intervals) and the beta distribution (Elmer, Jones and 
Nagin, 2018).
For each trajectory group identified, the mean PDC and the percent of beneficiaries in that 
group who discontinued nintedanib will be calculated.
9.7.2
Additional analysis
The objective of the additional analysis is to understand characteristics of patients within 
each nintedanib adherence trajectory among IPF patients. The unit of analysis will be the 
individual beneficiary, and the outcomes will be the vector of estimated probabilities of 

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cluster membership from a given GBTM.  The predictor variables will be the baseline 
covariates (Section 9.3.3).
The distributions of the covariates will be characterized with descriptive statistics, including 
frequencies and percentages for discrete or categorical variables, and means, standard 
deviations, medians, and interquartile ranges for continuous variables.
Associations between the predictors and the probabilities of trajectory membership will be 
quantified using fractional multinomial logit models estimated via quasi-maximum likelihood 
(Papke and Wooldridge, 1996).  Associations will be calculated both as odds ratios 
(exponentating the fractional multinomial logit model’s coefficients) and average marginal 
effects on the probability scale.
9.8
QUALITY CONTROL
The Medicare administrative data collection occurred in the past and the data will be obtained 
from 
. No additional insight into the data collection procedures is available beyond that 
which has been published by 
. As a result, we are relying on quality control measures 
implemented by 
 during data collection.
has established protocols for quality control of analyses. All analyses are audited 
before the finalization of results.  The auditor will independently review the conceptual and 
technical elements of the analysis with a critical eye to identifying any flaws or potential 
errors.
The auditor will understand the goal of the analysis and the expected results, and then review 
each step of the 
 data analysis audit checklist (available upon request). The auditor 
will keep a list of questions or issues and then meet with the project lead to discuss any 
findings.  If errors or issues are found that change the results, the auditor will perform an 
incremental audit to ensure that changes have been implemented properly and any change in 
results aligns with expectations about magnitude and direction.
9.9
LIMITATIONS OF THE RESEARCH METHODS
This study has several limitations, which include:
-
Absence of data on some behavioral factors (e.g. patient-physician communication, 
fear of disease/side effects, and family support) which may affect non-adherence. 
-
Reliance on prescription fill data to define adherence may not capture the actual use 
of the medication.
-
In this study, patients who had 12 months of continued fee-for-service enrollment of 
Parts A, B, and D before and after index dates were included. 

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9.10
OTHER ASPECTS
9.10.1
Data quality assurance
The following quality assurance and quality control measures will be applied to all programming that 
executes data extraction and transformation by 
:

Check program logs for notes, warning messages, and errors

Check derived data values against source data for a patient sample to ensure correct 
derivation

Verify that variables needed to support tables/listings/figures/ are found in the derived 
data set

Check that data fields are not truncated

Check data points for values outside expected ranges, where appropriate

Check that data are rounded correctly and in accordance to the analysis plan

Check that abbreviations, range categories, and subgroups conform to the analysis 
plan

Ensure the consistency of sample counts across relevant tables/listings/figures

Check formats consistent with the analysis plan

Ensure no typos, misspellings, or false values

Check that summary statistics are correct; check at least one category in each 
summary table against the data listings

Check that data are in accordance with the Data Plan

Check that subgroups conform to the Data Plan

Ensure there are no duplicate observations
9.10.2
Study records
Not applicable

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10.
PROTECTION OF HUMAN SUBJECTS
Not applicable based on secondary use of data without access to identifiable patient data.
10.1
STUDY APPROVAL, PATIENT INFORMATION, AND INFORMED 
CONSENT
Not applicable to this study.
10.2
STATEMENT OF CONFIDENTIALITY
Not applicable to this study

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11.
MANAGEMENT AND REPORTING OF ADVERSE 
EVENTS/ADVERSE REACTIONS
Given the information available within the Medicare claims database for this study, 
extraction on adverse events data will not be conducted and only data related to the study 
objectives will be extracted.  Therefore, information about individual adverse events will not 
be available.  Only data on aggregate-level medication use will be analysed.
11.1
ADVERSE EVENT AND SERIOUS ADVERSE EVENT COLLECTION 
AND REPORTING
Not applicable based on secondary use of data without any potential that any employee of BI 
or 
will access individually identifiable patient data. 

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12.
PLANS FOR DISSEMINATING AND COMMUNICATING 
STUDY RESULTS
The results of this study will be considered for dissemination in the form of scientific 
publications (e.g., an abstract/poster for presentation at a national conference, a manuscript 
for submission to a peer-reviewed journal).

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13.
REFERENCES
13.1
PUBLISHED REFERENCES
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Medicare enrollees on pirfenidone versus nintedanib for idiopathic pulmonary fibrosis. 
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costs among patients with idiopathic pulmonary fibrosis treated with antifibrotic medications 
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Dillon P, Stewart D, Smith SM, Gallagher P, Cousins G. Group‐based trajectory models: 
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setting. Clinical Pharmacology & Therapeutics. 2018 Jun;103(6):1052-60.

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Dunn A, Grosse SD, Zuvekas SH. Adjusting health expenditures for inflation: a review of 
measures for health services research in the United States. Health Services Research. 2018 
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Elmer J, Jones BL, Nagin DS. Using the Beta distribution in group-based trajectory models. 
BMC medical research methodology. 2018 Dec;18(1):1-5.
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Feldman CH, Collins J, Zhang Z, Subramanian SV, Solomon DH, Kawachi I, Costenbader 
KH. Dynamic patterns and predictors of hydroxychloroquine nonadherence among Medicaid 
beneficiaries with systemic lupus erythematosus. In: Seminars in arthritis and rheumatism 
2018 Oct 1 (Vol. 48, No. 2, pp. 205-213). WB Saunders.
Franklin JM, Gopalakrishnan C, Krumme AA, Singh K, Rogers JR, Kimura J, McKay C, 
McElwee NE, Choudhry NK. The relative benefits of claims and electronic health record data 
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Franklin JM, Shrank WH, Pakes J, Sanfélix-Gimeno G, Matlin OS, Brennan TA, Choudhry 
NK. Group-based trajectory models: a new approach to classifying and predicting long-term 
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Forbes CA, Deshpande S, Sorio-Vilela F, Kutikova L, Duffy S, Gouni-Berthold I, Hagström 
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therapy. American journal of hypertension. 2017 Oct 1;30(10):1015-23.

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Hickson, R.P., et al., Opening the black box of the group‐based trajectory modeling process 
to analyze medication adherence patterns: An example using real‐world statin adherence data. 
Pharmacoepidemiology and drug safety, 2020. 29(3): p. 357-362.
Ipatova AY, Koerner PH, Miller RT, Staskon F, Radi M. Retrospective analysis of 
medication utilization and clinical outcomes in patients with idiopathic pulmonary fibrosis 
treated with nintedanib or pirfenidone. Clinical Medicine Insights: Circulatory, Respiratory 
and Pulmonary Medicine. 2019 Mar;13:1179548419834922.
Jones BL, Nagin DS. A note on a Stata plugin for estimating group-based trajectory models. 
Sociological Methods & Research. 2013 Nov;42(4):608-13.
Juarez DT, Williams AE, Chen C, Daida YG, Tanaka SK, Trinacty CM, Vogt TM. Factors 
affecting medication adherence trajectories for patients with heart failure. The American 
journal of managed care. 2015 Mar 1;21(3):e197.
Kim SC, Choudhry N, Franklin JM, Bykov K, Eikermann M, Lii J, Fischer MA, Bateman 
BT. Patterns and predictors of persistent opioid use following hip or knee arthroplasty. 
Osteoarthritis and cartilage. 2017 Sep 1;25(9):1399-406.
Lam WY, Fresco P. Medication adherence measures: an overview. BioMed research 
international. 2015 Oct 11;2015.
Li Y, Zhou H, Cai B, Kahler KH, Tian H, Gabriel S, Arcona S. Group-based trajectory 
modeling to assess adherence to biologics among patients with psoriasis. ClinicoEconomics 
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Lo-Ciganic WH, Gellad WF, Gordon AJ, Cochran G, Zemaitis MA, Cathers T, Kelley D, 
Donohue JM. Association between trajectories of buprenorphine treatment and emergency 
department and in‐patient utilization. Addiction. 2016 May;111(5):892-902.
Loughran T, Nagin DS. Finite sample effects in group-based trajectory models. Sociological 
methods & research. 2006 Nov;35(2):250-78.
MacEwan JP, Forma FM, Shafrin J, Hatch A, Lakdawalla DN, Lindenmayer JP. Patterns of 
adherence to oral atypical antipsychotics among patients diagnosed with schizophrenia. 
Journal of managed care & specialty pharmacy. 2016 Nov;22(11):1349-61.
Nagin DS, Odgers CL. Group-based trajectory modeling in clinical research. Annual review 
of clinical psychology. 2010 Apr 27;6:109-38.
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application to 401 (k) plan participation rates. Journal of applied econometrics. 1996 
Nov;11(6):619-32.

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Paranjpe R, Johnson ML, Essien EJ, Barner JC, Serna O, Gallardo E, Majd Z, Fleming ML, 
Ordonez N, Holstad MM, Abughosh SM. Group-Based Trajectory Modeling to Identify 
Patterns of Adherence and Its Predictors Among Older Adults on Angiotensin-Converting 
Enzyme Inhibitors (ACEIs)/Angiotensin Receptor Blockers (ARBs). Patient preference and 
adherence. 2020;14:1935.
Raebel MA, Schmittdiel J, Karter AJ, Konieczny JL, Steiner JF. Standardizing terminology 
and definitions of medication adherence and persistence in research employing electronic 
databases. Medical care. 2013 Aug;51(8 0 3):S11.
Sun JW, Rogers JR, Her Q, Welch EC, Panozzo CA, Toh S, Gagne JJ. Adaptation and 
validation of the combined comorbidity score for ICD-10-CM. Medical care. 2017 Dec 
1;55(12):1046-51.
Vadhariya A, Fleming ML, Johnson ML, Essien EJ, Serna O, Esse T, Choi J, Boklage SH, 
Abughosh SM. Group-based trajectory models to identify sociodemographic and clinical 
predictors of adherence patterns to statin therapy among older adults. American health & 
drug benefits. 2019 Jun;12(4):202.
Wang X, Chen H, Essien E, Wu J, Serna O, Paranjpe R, Abughosh S. Medication adherence 
to antihypertensive triple-combination therapy among patients enrolled in a medicare 
advantage plan. Journal of managed care & specialty pharmacy. 2019 Jun;25(6):678-86.
Zongo A, Simpson S, Johnson JA, Eurich DT. Change in Trajectories of Adherence to Lipid‐
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14.
ANNEXURES

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ANNEX 1. LIST OF STAND-ALONE DOCUMENTS
Number
Document 
Reference Number
Date
Title
1
<Number>
July 7 2021
Code_List.xlsx

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ANNEX 2. ENCEPP CHECKLIST FOR STUDY PROTOCOLS
A copy of the European Network of Centers for Pharmacoepidemiology and 
Pharmacovigilance (ENCePP) Checklist for Study protocols available at website: 
encepp.eu/standards_and_guidances/index.html completed and signed by the main author of 
the study protocol should be included in Annex 2.
The checklist will facilitate the review of the protocol and evaluation of whether investigators 
have considered important methodological aspects.
In question 9.5 of the Checklist, Revision 1:
“Study start” means “Start of data collection”
“Study progress” means “Progress report(s)”
“Study completion” means “End of data collection”
“Reporting” means “Final report of the study results”

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ANNEX 3. ADDITIONAL INFORMATION
Additional annexes may be included if necessary.

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ANNEX 4. REVIEWERS AND APPROVAL SIGNATURES
The NIS Protocol must be sent for review to the following individuals prior to approval.
Reviewer
NIS involving BI 
product(s)
NIS not involving BI product(s)
Global NIS
Local NIS
NIS Lead
X
X
X
Global TM Epi
X
X
X
Global TMM / TMMA / TM Market 
Access
X
X
Global Project Statistician
X
X
Global TM RA
X
Global PVWG Chair
X
GPV SC
X
X
X
Global CTIS representative
X
Local Medical Director 
X (if local study)
X
Local Head MAcc / HEOR Director
X (if local study)
X
Global TA Head Epi*
X
X
Global TA Head Clinical 
Development / Medical Affairs /
Market Access*
X
X
Global TA Head PV RM*
X
RWE CoE
X
X
PSTAT / PSTAT-MA                   
(for NISnd only)
X
X
X
NIS DM
X
X
X 
Local Head MA/Clinical 
Development
X (does not apply to 
NISed without chart 
abstraction)
* After review by Global TM for function
Include this Annex if signatures of external investigators are required and/or for studies that will not be stored in the DMS 
for submission documents. For non-interventional studies approval signatures must be obtained from the individuals 
as noted in section 5.1.3 “Manage NIS Protocol” in the corresponding SOP 001-MCS-90-118. If the study is a PASS, 
additional approvals are necessary; refer to SOP 001-MCS-90-140 “Post Authorization Safety Studies”.

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Study Title: 
Study Number: 
Protocol Version:
I herewith certify that I agree to the content of the study protocol and to all documents 
referenced in the study protocol.
Note: Please insert respective signatories with regard to the SOP.
Position: ____PI_
Name/Date: < >
Signature: _______________
Position: NIS Lead
Name/Date: < >
Signature: _______________
Position: ________
Name/Date: _______________________
Signature: _______________
Position: ________
Name/Date: _______________________
Signature: _______________
Position: ________
Name/Date: _______________________
Signature: _______________
Position: ________
Name/Date: _______________________
Signature: _______________
Position: ________
Name/Date: _______________________
Signature: _______________