Economic Burden Associated With Nintedanib Non-adherence Among Medicare Beneficiaries With IPF
Conditions: Idiopathic Pulmonary Fibrosis
Sponsor: Boehringer Ingelheim

Non-Interventional Study (NIS) Protocol
 
Document Number:
c42255024-02
BI Study Number:
1199-0520
BI 
Investigational 
Product(s):
Title:
Economic burden associated with nintedanib non-adherence among 
Medicare beneficiaries with IPF
Brief lay title:
Burden of nintedanib non-adherence among IPF patients
Protocol 
version 
identifier:
3.0
Date of last version 
of protocol:
Not applicable
PASS:
EU PAS 
register 
number:
NA
Active substance:
<List of pharmacotherapeutic group(s) (ACT codes) and 
active substance(s) subject to the study, if applicable>
Medicinal product:
Nintedanib (Ofev)
Product reference:
<Reference number(s) of centrally authorised products 
and/or, if possible, of nationally authorised products subject 
to the study, if applicable>
Procedure number:
Not applicable
Marketing 
authorisation 
holder(s):
<Marketing authorisation holder(s) which initiate(s), 
manage(s) or finance(s) the study>
Joint PASS:
No

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Research question 
and objectives:
1) To quantify the association between nintedanib adherence trajectory 
group (as measured from a group-based trajectory model or GBTM) 
and health care resource use, with a focus on inpatient 
hospitalization, among patients with IPF.
2) To quantify the association between a patient’s nintedanib 
adherence trajectory group (as measured from a GBTM) and their 
medical costs among patients with IPF.
Country(-ies) of study:
United States
Author:
Marketing 
authorisation 
holder(s):
<Name, address and contact details of the marketing 
authorisation holder(s)>
Date:
05/09/2023
Page 1 of 35
Proprietary confidential information
© 2023 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.............................................................................................................................2
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 ......................................................................................................15
9.2.1
Study sites......................................................................................................15
9.2.2
Study population............................................................................................16
9.2.3
Study visits ....................................................................................................16
9.2.4
Study discontinuation....................................................................................16
9.3
VARIABLES.................................................................................................17
9.3.1
Exposures ......................................................................................................17
9.3.2
Outcomes.......................................................................................................17
9.3.3
Covariates......................................................................................................17
9.4
DATA SOURCES.........................................................................................23
9.5
STUDY SIZE ................................................................................................24
9.6
DATA MANAGEMENT..............................................................................24
9.7
DATA ANALYSIS .......................................................................................25
9.7.1
Main analysis.................................................................................................25
9.8
QUALITY CONTROL .................................................................................26
9.9
LIMITATIONS OF THE RESEARCH METHODS ....................................27
9.10
OTHER ASPECTS........................................................................................27
9.10.1
Ethical Approval............................................................................................27
9.10.2
Study records.................................................................................................27
10.
PROTECTION OF HUMAN SUBJECTS ..................................................................28

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10.1
STUDY APPROVAL, PATIENT INFORMATION, AND INFORMED 
CONSENT
.......................................................................................................................28
10.2
STATEMENT OF CONFIDENTIALITY ....................................................28
11.
MANAGEMENT AND REPORTING OF ADVERSE EVENTS/ADVERSE
REACTIONS...........................................................................................................................29
11.1
ADVERSE EVENT AND SERIOUS ADVERSE EVENT COLLECTION 
AND REPORTING .............................................................................................................29
12.
PLANS FOR DISSEMINATING AND COMMUNICATING STUDY RESULTS .30
13.
REFERENCES ............................................................................................................31
13.1
PUBLISHED REFERENCES.......................................................................31
14.
ANNEXES...................................................................................................................33
ANNEX 1. LIST OF STAND-ALONE DOCUMENTS.....................................................33
ANNEX 2. REVIEWERS AND APPROVAL SIGNATURES..........................................34

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2.
LIST OF ABBREVIATIONS
CI
Confidence Interval
ENCePP
European Network of Centres for Pharmacoepidemiology and
PVG
Pharmacovigilance
GBTM
Group-based Trajectory Modelling
IPF
Idiopathic Pulmonary Fibrosis
IRB
Institutional Review Board
NIS
Non-Interventional Study
PASS
Post-Authorization Safety Study
PDC
Proportion of Days Covered

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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:
9 May 2023
Study 
number:
1199-
0520
Version/Revision:
5.0
Version/Revision date:
19 April 2023
Title of study:
Economic burden associated with nintedanib non-adherence among 
Medicare beneficiaries with IPF
Rationale 
and 
background:
Recent systematic reviews found links between medication non-
adherence and higher healthcare spending (Cutler et al., 2018) and 
elevated risks of inpatient hospitalization and mortality (Walsh et al., 
2019).  To our knowledge, however, there have been no studies 
examining the association between non-adherence and economic 
burden specific to nintedanib, antifibrotics, or idiopathic pulmonary 
fibrosis (IPF).
A prior study by this team quantified nintedanib adherence 
trajectories using group-based trajectory modelling (GBTM) and 
identified characteristics of patients associated with membership in 
each trajectory group.  A handful of existing studies examine 
associations between medication non-adherence as measured from 
GBTM and coincident or subsequent health care resource use, 
although none were specific to the setting of interest here.  This 
study seeks to build on our prior study and fill that gap in 
knowledge.  We expect the findings from this study to be highly 
relevant to clinicians and payers to help optimize care in a cost-
effective manner.
Research 
question and 
objectives:
1) Assess the association between nintedanib adherence trajectory group 
(as measured from a GBTM) and health care resource use, with a focus 
on inpatient hospitalization, among patients with IPF.
2) Assess the association between a patient’s nintedanib 
adherence trajectory group (as measured from a GBTM) and 
their medical costs among patients with IPF.
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:
9 May 2023
Study 
number:
1199-
0520
Version/Revision:
5.0
Version/Revision date:
19 April 2023
Population:
The study sample will consist of community-dwelling Medicare 
beneficiaries with IPF who initiate treatment with nintedanib.
Variables:
The main exposure will be a five-category measure of adherence 
trajectory to nintedanib in the year following initiation.
Outcomes will reflect inpatient hospitalization (any and number of 
days) and health care spending for both all-cause and IPF-related care.
A range of covariates will be measured during the baseline period and 
at the index date, including baseline patient demographic 
characteristics (e.g., age, sex, race/ethnicity), residential location, 
clinical characteristics (e.g., comorbidity index, IPF-related health care 
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, 
2020.
Study size:
Based on our prior study, the sample for analysis of the primary 
outcomes will consist of 1,798 Medicare beneficiaries with IPF who 
initiated nintedanib between October 1, 2014, and December 31, 2018. 
The sample available for the secondary outcomes will be smaller by an 
unknown amount due to loss to follow-up.
Data analysis:
Mean health care cost and use outcomes will be compared between 
pairs of nintedanib adherence trajectory membership categories using a 
regression modeling framework.  Unadjusted means will be compared 
first using an “empty” linear regression model specification (with no 
covariates or weights).  Adjusted means will then be compared using a 
“doubly robust” generalized linear model (GLM) specification.  
Potential confounding by observed covariates will be addressed in two 
ways to achieve double robustness.  The first way will be to calculate 
observation-level inverse propensity weights that balance covariates 

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Name of company:
Boehringer Ingelheim
Name of finished 
medicinal product:
Nintedanib (Ofev)
Name of active ingredient:
Nintedanib (Ofev)
Protocol date:
9 May 2023
Study 
number:
1199-
0520
Version/Revision:
5.0
Version/Revision date:
19 April 2023
across exposure categories.  The second way will be through 
regression adjustment; that is, observed covariates will be added to the 
GLM specification.
Milestones:
Final report of this study is expected to be available for review by May
15th, 2023.

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5.
AMENDMENTS AND UPDATES
None.

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6.
MILESTONES
Milestone
Planned Date
Final report 
05/15/2023
Poster draft
TBD
Poster BI approval (all steps in 
DataVision)
TBD
Manuscript draft
TBD
Manuscript BU approval (all steps in 
DataVision)
TBD
Publication
TBD

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7.
RATIONALE AND BACKGROUND
It is taken as self-evident in the medical community that medication non-adherence leads to 
negative outcomes; as C. Everett Koop said, “Drugs don’t work in patients who don’t take 
them” (Osterberg and Blaschke, 2005).  Recent systematic reviews of the published literature 
found links between medication non-adherence and higher spending (Cutler et al., 2018) and 
elevated risks of inpatient hospitalization and mortality (Walsh et al., 2019).  To our 
knowledge, however, there have been limited studies examining the association between non-
adherence and economic burden specific to nintedanib, antifibrotics, or idiopathic pulmonary 
fibrosis (IPF).
Measuring medication adherence is difficult.  There are several approaches to measuring 
adherence, each with its own strengths and limitations (Forbes et al., 2018).  A common 
method in real-world settings, such as prescription drug claims databases, is the proportion of 
days covered (PDC), which is defined for a specific medication as the number of days 
supplied divided by the total number of days in the study period (Anghel et al., 2019).  
Although PDC is useful in that it offers in a single number a metric of medication adherence 
over a predefined period, its simplicity masks any heterogeneity in adherence between 
patients and specifically does not capture changes in individual patient adherence over time.  
An alternative approach is to use group-based trajectory modelling (GBTM) to detect 
trajectories of medication adherence over time and identify clusters of individuals who follow 
similar longitudinal adherence patterns (Nagin and Odgers, 2010).  GBTM has been used to 
understand adherence to medications in several therapeutic areas (Alhazami et al., 2020).  
Longitudinal evaluation of medication adherence using GBTM can yield more informative 
classifications compared to traditional methods such as PDC (Franklin et al., 2013).
A prior study by this study team quantified nintedanib adherence trajectories using GBTMs 
and identified characteristics of patients associated with membership in each trajectory group.  
Individuals with idiopathic pulmonary fibrosis (IPF) who initiated nintedanib during 
10/1/2014–12/31/2018 were identified in 100% Medicare claims and enrollment data.  The 
sample consisted of community-dwelling older adults (≥66 years) with continuous coverage 
in Medicare Parts A, B and D for one year before (baseline) and after (follow-up) starting 
nintedanib.  PDC was calculated for each of 12 consecutive 30-day months based on 
nintedanib claim fill dates and days’ supply.  PDC was dichotomized, with PDC ≥0.8 
considered adherent.  A series of GBTMs of adherence was estimated to identify the best-
fitting specification.  Patients were then grouped based on their estimated adherence 
trajectories.  Associations between baseline patient characteristics, including demographics, 
comorbidities, and health case use, and group membership probabilities were quantified as 
odds ratios using fractional multinomial logit modelling.
In thas study, the best-fitting GBTM had five adherence trajectory groups: highly adherent 
(43.1%), medium adherent (11.9%), gradual decliners (10.4%), intermediate decliners 
(13.2%), and rapid decliners (21.5%) (Figure 1).  The principal factors associated with higher 
odds of being in at least one of the lower-adherence groups were older age, female sex, race 
and ethnicity other than non-Hispanic white, and taking an additional medication during 
baseline.

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A handful of existing studies examine associations between medication non-adherence as 
measured from GBTMs and coincident or subsequent health care resource use.  Dillon et al. 
(2019) found lower GBTM-based adherence to antihypertensive medications was associated 
with a higher rate of visits to general practitioners but not hospitalizations in Ireland.   
Franklin et al. (2015) found lower GBTM-based adherence to statin medications was 
associated with higher risk of clinical events as measured by a composite of hospitalization 
for an acute coronary event, revascularization, a cerebrovascular event and heart failure in a 
large commercial insurance claims database in the United States.  A pair of studies by Lo-
Ciganic and colleagues used Medicaid claims data from Pennsylvania.  They found lower 
GBTM-based adherence to buprenorphine led to higher risk of hospitalization and emergency 
department (ED) visits (Lo-Ciganic, Gellad, et al., 2016), and lower GBTM-based adherence 
to oral hypoglycemic medications led to higher risk of diabetes-related hospitalizations/ED 
visits.
This study seeks to fill the gap in knowledge about the potential association between 
adherence to nintedanib and patients’ health care resource use and spending.  We expect the 
findings from this study to be highly relevant to clinicians and payers to develop actionable 
strategies to improve adherence to nintedanib.
Figure 1. Nintedanib adherence trajectories

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8.
RESEARCH QUESTION AND OBJECTIVES
This study has two objectives:
1) To assess the association between nintedanib adherence trajectory group (as measured 
from a GBTM) and health care resource use, with a focus on inpatient hospitalization, 
among patients with IPF.
2) To assess the association between a patient’s nintedanib adherence trajectory group
(as measured from a GBTM) and their medical costs among patients with IPF.

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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 2, 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 two-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 12/31/2020.
Figure 2. Study design schematic
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.  

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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 
!
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

Exclusion criteria:
!
Had any history of pirfenidone or nintedanib use during the baseline period 
!
Had any history of lung transplant during the baseline, index date or follow-up 
periods 
!
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.

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9.3
VARIABLES
9.3.1
Exposures
The exposure will be a categorical variable indicating which of the five nintedanib adherence 
trajectory groups a patient was most likely to belong, given their 12-month pattern of 
nintdanib claims as estimated from the GBTM.  The groups (and their prevalence in the 
sample) are highly adherent (43.1%), medium adherent (11.9%), gradual decliners (10.4%), 
intermediate decliners (13.2%), and rapid decliners (21.5%).
9.3.2
Outcomes
The primary outcome will be total all-cause medical costs, which will be calculated as the 
sum of total amounts paid (by payers and patients) for all medical services.  Types of medical 
services covered by Medicare include inpatient facility, outpatient facility, skilled nursing 
facility, home health care, hospice, durable medical equipment, and clinician office visits 
(and other services covered under the Part B benefit).  This outcome will exclude spending on 
outpatient pharmacy (as covered under the Part D benefit).  
The secondary outcomes will be:
1. Total IPF-related medical costs, which will be calculated as the sum of total amounts 
paid (by payers and patients) for all medical services for an IPF-related reason (at 
least one IPF diagnosis code).
2. All-cause inpatient hospitalization 
3. IPF-related inpatient hospitalization.
9.3.3
Covariates
Study covariates are listed here and will be measured at the index date or during the 12 
months prior.
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 

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Variable
Description
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.
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 Robert 
Graham Center.  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 

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Variable
Description
on their own.  Moreover, unlike the Charlson and 
Elixhauser indexes, Gagne et al.’s (2011) combined 
index is not conditioned on inpatient hospital 
admission.  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.
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.
Asthma
A binary indicator for whether the beneficiary had a 
diagnosis code for asthma on at least one inpatient, 
outpatient or Carrier claim that occurred during the 
baseline period.
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.
Lung biopsy
A binary indicator for whether the beneficiary had a 
diagnosis or procedure code for a lung biopsy on at 

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Variable
Description
least one inpatient, outpatient or Carrier claim that 
occurred during the baseline period.
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.
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.
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.
Ventilator use
A binary indicator for whether the beneficiary had a 
diagnosis or procedure code for ventilator use on at 
least one inpatient, outpatient or Carrier claim that 
occurred during the baseline period.
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.
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.
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 

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Variable
Description
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 
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.

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Variable
Description
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.
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 

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Variable
Description
ResDAC here.  Thus, emergency department visits 
will be identified in inpatient and outpatient facility 
claims via the presence of revenue center codes 
0450-0459 and 0981.
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.  
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.
9.4
DATA SOURCES
The study will use data from the federal Medicare program on elderly beneficiaries 
consistently enrolled in traditional, or fee-for-service, Medicare insurance coverage for 
hospital (Part A), physician (Part B) and pharmacy (Part D) claims.  As of October 2021, 
there were over 36 million beneficiaries in traditional Medicare.  The study will focus on 
enrollment and claims data from October 1, 2013, through December 31, 2020.  Access to 
these data has been obtained by 
 through the 
.
This data source was selected for multiple reasons.  First, Medicare data capture the 
preponderance of IPF cases.  In a study reported by Ipatova et al. (2019), 76% of patients on 
nintedanib or pirfenidone were ≥65 years of age.  Second, Medicare claims are 
comprehensive in their inclusion of covered services, including outpatient prescription drugs 
(through Medicare Part D).  Third, there is comparatively little turnover among Medicare 
beneficiaries compared with commercial insurance enrollees, which means that it is possible 
to follow Medicare beneficiaries for longer periods on average and more patients will qualify 
for a study requiring a given minimum continuous coverage period.  Fourth, Medicare claims 
were used in the prior study of nintedanib adherence, so it is natural to continue using them 
for this study.

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9.5
STUDY SIZE
In the prior study, there were 18,733 Medicare beneficiaries with at least one claim for 
nintedanib between 10/01/2014 and 12/31/2018.  After sample selection criteria were applied, 
the final analytic sample consisted of 1,798 patients (Figure 3).
Figure 3. Sample selection in the prior study of nintedanib adherence trajectories
9.6
DATA MANAGEMENT
Data manipulation and analysis will be conducted by 
.  Pursuant to 
requirements guiding participation in the 
 
Innovator program, 
 staff will access the Medicare claims and enrollment
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.
Data security will be assured along two dimensions.  First, the data will reside only in 
 
secure 
environment.  
 staff have completed a rigorous identity 
verification process to gain access to the 
.  
 prohibits any patient-level data from 

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being copied from the 
; only aggregated data based on at least 11 patients is allowable.  
Second, only a limited set of patient identifiers will be present in the data, including 
residential ZIP Code and full dates of health insurance enrollment and claims and subject’s
birth and death dates.  The unique patient ID used by 
 is a synthetic identifier, and no 
study staff will have access to the linking file to real subject identifiers.
Additionally, study documents, including synopsis, protocol, and final results as well as 
publications, will be archived on the 
 Team site.
9.7
DATA ANALYSIS
9.7.1
Main analysis
Descriptive statistics of the covariates will be calculated stratified by the five categories of 
patients’ adherence trajectory group membership, after assigning patients to the group for 
which they had the highest posterior probability of membership as estimated in the GBTM 
(from the prior study).  Tests of joint equality across groups will be computed using ANOVA 
and nonparametric Kruskal-Wallis rank tests for continuous measures and χ2 tests for 
categorical measures.
Mean outcomes will be compared between pairs of nintedanib adherence trajectory 
membership categories (referent group: highly adherent) using a regression modeling 
framework.  Unadjusted means will be compared first using an “empty” linear regression 
model specification (with no covariates or weights) that makes standard errors robust to 
heteroskedasticity of unknown form.  Adjusted means will then be compared using a “doubly 
robust” generalized linear model (GLM) specification that also uses heteroskedasticity-robust 
standard errors.  More detail about the double-robustness is provided below.
The functional form of the GLM will be selected for each outcome based on its observed 
distribution in the data.  The binary outcome of “any hospitalization” will likely be modeled 
with logistic regression.  The non-negative “total LOS” outcome may be modeled with a 
quasi-maximum likelihood Poisson model if the proportion of patients with zero days of 
hospitalization is sufficiently low (less than 10%), or a two-part model (e.g., logistic model of 
any hospitalization vs. none and a simultaneously estimated Poisson model of total LOS 
conditional on having positive LOS) if not (Belotti et al., 2015).  Similarly, if the “total 
medical costs” outcome has a pile-up at zero, a two-part model will be estimated; if not, a 
GLM with a log link and a distribution family decided by application of the modified Park 
test will be estimated (Manning, 1998).
Potential confounding by observed covariates will be addressed in two ways, thus leading to 
“doubly robust” estimates.  The first way is to calculate two sets of observation-level weights 
that balance covariates across exposure categories: inverse probability of treatment weights 
(IPTWs) and weights derived from a marginal mean weighting through stratification 
(MMWS) procedure (Hong, 2012; Linden, 2014).  To generate the weights, a multinomial 
logit model of nintedanib adherence trajectory group category will be estimated as a function 
of the covariate roster.  Predicted probabilities (i.e., propensity scores) for each category will 
be generated from the model results and used to calculate both the IPTW and MMWS 

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weights.  Because multiple exposure categories are to be compared, the weights will provide 
estimates of the average treatment effect (ATE) (and not the average treatment effect on the 
treated [ATT]).  
The second way confounding will be addressed is through regression adjustment; that is, 
observed covariates will be added to the GLM specification.  Combining weighting with 
regression adjustment yields a double robustness property that attenuates bias in the case 
when either the treatment model (i.e., the multinomial logit model of exposure category) or 
the outcome model (i.e., the GLM of the health care resource use or spending outcome) is 
misspecified (Linden et al., 2016).
To facilitate interpretability, the regression model results will be transformed back to the 
scale of the outcome variable.  Average marginal effects and their 95% confidence intervals 
(CIs) will be calculated using the delta method.  Data preparation will be performed using 
SAS 9.4 (
) and data analysis will be conducted using Stata 17 
(
).
9.8
QUALITY CONTROL
Regarding quality control procedures around the data, the Medicare administrative data 
collection occurred in the past and access to the data is being provided by
. 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.
Regarding quality control procedures around the analyses, 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

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!
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
9.9
LIMITATIONS OF THE RESEARCH METHODS
The study has several limitations, including:
!
The data source consists of administrative claims data, which do not include 
important determinants of health care resource use and cost outcomes that may be 
correlated with the adherence exposure, such as IPF disease severity.
!
Although the outcomes should be measured accurately, the adherence exposure is 
necessarily based on prescription fill data from claims and may mismeasure actual 
nintedanib use.
!
Sample selection bias may arise from the design decision to exclude patients who do 
not have at least 12 months of follow-up data after nintedanib initiation for primary 
outcomes (and 24 months for secondary outcomes).
!
Generalizability may be limited to IPF patients who are continuously covered under 
fee-for-service Medicare, including Parts A, B and D.
9.10
OTHER ASPECTS
9.10.1
Ethical Approval
Although these data are not de-identified, pursuant to 
 requirements, this research study 
has been determined to be exempt from institutional review board (IRB) review and has been 
granted a full waiver of HIPAA authorization for use and disclosure of protected health 
information by the WCG Institutional Review Board.
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 analyzed.
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
Alhazami M, Pontinha VM, Patterson JA, Holdford DA. Medication adherence trajectories: a 
systematic literature review. Journal of Managed Care & Specialty Pharmacy. 2020 
Sep;26(9):1138-52.
Anghel LA, Farcas AM, Oprean RN. An overview of the common methods used to measure 
treatment adherence. Medicine and pharmacy reports. 2019 Apr;92(2):117.
Belotti F, Deb P, Manning WG, Norton EC. twopm: Two-part models. The Stata Journal. 
2015 Apr;15(1):3-20.
Butler DC, Petterson S, Phillips RL, Bazemore AW. Measures of social deprivation that 
predict health care access and need within a rational area of primary care service delivery. 
Health services research. 2013 Apr;48(2pt1):539-59.
Cutler RL, Fernandez-Llimos F, Frommer M, Benrimoj C, Garcia-Cardenas V. Economic 
impact of medication non-adherence by disease groups: a systematic review. BMJ open. 2018 
Jan 1;8(1):e016982.
Dillon P, Smith SM, Gallagher P, Cousins G. The association between pharmacy refill-
adherence metrics and healthcare utilisation: a prospective cohort study of older hypertensive 
adults. International Journal of Pharmacy Practice. 2019 Oct;27(5):459-67.
Forbes, C.A., et al., A systematic literature review comparing methods for the measurement 
of patient persistence and adherence. Current medical research and opinion, 2018. 34(9): p. 
1613-1625.
Franklin JM, Krumme AA, Tong AY, Shrank WH, Matlin OS, Brennan TA, Choudhry NK. 
Association between trajectories of statin adherence and subsequent cardiovascular events. 
Pharmacoepidemiology and drug safety. 2015 Oct;24(10):1105-13.
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 
medication adherence. Medical care. 2013 Sep 1:789-96.
Gagne JJ, Glynn RJ, Avorn J, Levin R, Schneeweiss S. A combined comorbidity score 
predicted mortality in elderly patients better than existing scores. Journal of clinical 
epidemiology. 2011 Jul 1;64(7):749-59.
Hong G. Marginal mean weighting through stratification: a generalized method for evaluating 
multivalued and multiple treatments with nonexperimental data. Psychological methods. 
2012 Mar;17(1):44.

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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.
Linden A. Combining propensity score‐based stratification and weighting to improve causal 
inference in the evaluation of health care interventions. Journal of evaluation in clinical 
practice. 2014 Dec;20(6):1065-71.
Linden A, Uysal SD, Ryan A, Adams JL. Estimating causal effects for multivalued 
treatments: a comparison of approaches. Statistics in Medicine. 2016 Feb 20;35(4):534-52.
Lo-Ciganic WH, Donohue JM, Jones BL, Perera S, Thorpe JM, Thorpe CT, Marcum ZA, 
Gellad WF. Trajectories of diabetes medication adherence and hospitalization risk: a 
retrospective cohort study in a large state Medicaid program. Journal of general internal 
medicine. 2016 Sep;31(9):1052-60.
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.
Manning WG. The logged dependent variable, heteroscedasticity, and the retransformation 
problem. Journal of health economics. 1998 Jun 1;17(3):283-95.
Nagin DS, Odgers CL. Group-based trajectory modeling in clinical research. Annual review 
of clinical psychology. 2010 Apr 27;6:109-38.
Osterberg L, Blaschke T. Adherence to medication. New England journal of medicine. 2005 
Aug 4;353(5):487-97.
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.
Walsh CA, Cahir C, Tecklenborg S, Byrne C, Culbertson MA, Bennett KE. The association 
between medication non‐adherence and adverse health outcomes in ageing populations: A 
systematic review and meta‐analysis. British journal of clinical pharmacology. 2019 
Nov;85(11):2464-78.

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14.
ANNEXES
ANNEX 1. LIST OF STAND-ALONE DOCUMENTS
None.

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ANNEX 2. 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
Study Title: Economic burden associated with nintedanib non-adherence among 
Medicare beneficiaries with IPF
Study Number:
Protocol Version:

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I herewith certify that I agree to the content of the study protocol and to all documents 
referenced in the study protocol.
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:

 
 
 
APPROVAL / SIGNATURE PAGE 
Document Number: 
 
Technical Version Number:
Document Name: 
                                                                                                                 
 
Title: 
                                                                                                                   
Signatures (obtained electronically) 
Meaning of Signature  
Signed by 
Date Signed 
 
 
 
 
 
 
 
 
 
 
 
 
 
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Boehringer Ingelheim 
Page 2 of 2
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