A Retrospective, Observational Cohort Study Evaluating the Effectiveness and Cardiovascular Safety of Abaloparatide in Postmenopausal Women New to Anabolic Therapies
Conditions: Osteoporosis, Postmenopausal
Sponsor: Radius Health, Inc.

Sponsor Name: Radius Health, Inc. 
Protocol Number: BA058-05-028 
Protocol Version and Date: Original, V1.0 dated 09 July 2021 
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Clinical Study Protocol 
A Retrospective, Observational Cohort Study Evaluating the 
Effectiveness and Cardiovascular Safety of Abaloparatide in 
Postmenopausal Women New to Anabolic Therapies 
This study will be conducted according to the protocol and in compliance with 
International Council for Harmonisation (ICH) Good Clinical Practice (GCP), Health 
Insurance Portability and Accountability Act (HIPAA) guidelines, the ethical principles 
stated in the Declaration of Helsinki, and other applicable regulatory requirements. 
Protocol Number: 
BA058-05-028 
Version Number: 
Original 
Version Date: 
V1.0, 09 July 2021 
IND Number: 
N/A 
NCT Number: 
Requested 
Active Ingredient: 
Abaloparatide, a novel synthetic peptide analogue of 
PTHrP (human parathyroid hormone-related protein) 
Country of Study: 
United States of America 
Sponsor: 
Radius Health, Inc. (Radius) 
22 Boston Wharf Road, 7th Floor 
Boston, Massachusetts 02210 
United States of America 
Telephone: (617) 551-4700 
Fax: (617) 551-4701 
Contact: 
 
Radius Health, Inc. 
 
Clinical Research 
Organization: 
PRA Health Sciences 
Confidentiality Notice: 
This document contains information that is confidential and proprietary to Radius Health, Inc. (Radius). 
This document must not be disclosed to anyone other than the study team and internal review committee 
as required. The information in this document cannot be used for any purpose other than the evaluation 
or conduct of the research without the prior written consent of Radius Health, Inc. 
NCT #NCT04974723

Sponsor Name: Radius Health, Inc. 
Protocol Number: BA058-05-028 
Protocol Version and Date: Original, V1.0, dated 09 July 2021 
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1. 
TABLE OF CONTENTS 
1 
TABLE OF CONTENTS ................................................................... 2 
LIST OF TABLES ................................................................................................. 4 
LIST OF FIGURES ............................................................................................... 4 
LIST OF ABBREVIATIONS ................................................................................ 5 
2 
PROTOCOL SYNOPSIS ................................................................... 8 
3 
RESPONSIBLE PARTIES ............................................................... 12 
4 
VERSION HISTORY ....................................................................... 13 
5 
RESEARCH QUESTIONS, OBJECTIVES, AND ENDPOINTS ... 14 
5.1 
Research Questions ........................................................................... 14 
5.2 
Objectives and Endpoints ................................................................. 15 
5.3 
Justification of Objectives and Endpoints ........................................ 16 
6 
BACKGROUND AND RATIONALE............................................. 17 
6.1 
Disease Background ......................................................................... 17 
6.2 
Currently Available Treatment Options ........................................... 19 
6.2.1 
Teriparatide ....................................................................................... 19 
6.2.2 
Romosozumab .................................................................................. 20 
6.2.3 
Abaloparatide .................................................................................... 22 
6.3 
Study Rationale ................................................................................. 23 
7 
RESEARCH METHODS ................................................................. 25 
7.1 
Study Design ..................................................................................... 25 
7.1.1 
Rationale for the Current Study Design ............................................ 28 
7.1.2 
Strengths of the Study Design .......................................................... 29 
7.1.3 
Limitations of the Study Design ....................................................... 29 
7.1.4 
Generalizability ................................................................................. 31 
7.2 
Setting ............................................................................................... 31 
7.3 
Duration of Subject Participation ..................................................... 31 
7.4 
Study Population ............................................................................... 32 
7.4.1 
Subject Selection Criteria ................................................................. 32 
7.4.2 
Discussion of Study Population ........................................................ 35 

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7.5 
Data Sources ..................................................................................... 36 
7.6 
Variables for Data Extraction ........................................................... 37 
7.6.1 
Demographics and Baseline Variables ............................................. 37 
7.6.2 
Risk Factors ...................................................................................... 38 
7.6.3 
Co-morbidities .................................................................................. 38 
7.6.4 
Osteoporosis Treatment History ....................................................... 39 
7.6.5 
Concomitant Medications ................................................................. 39 
7.6.6 
Anabolic Drug Exposure .................................................................. 39 
7.6.7 
Outcomes .......................................................................................... 39 
7.6.7.1 
Evaluation of Effectiveness .............................................................. 40 
7.6.7.2 
Evaluation of Cardiovascular Safety ................................................ 41 
7.7 
Data Management ............................................................................. 41 
7.8 
Statistical Methods and Analyses ..................................................... 41 
7.8.1 
Power and Sample Size .................................................................... 42 
7.8.1.1 
Sample Size ...................................................................................... 42 
7.8.1.2 
Power Calculation ............................................................................. 42 
7.8.2 
Creation of Analytic Cohorts and Source Data Characteristics ........ 42 
7.8.3 
Analysis Populations ........................................................................ 42 
7.8.4 
Baseline Descriptive Statistics .......................................................... 43 
7.8.5 
Propensity Score Matching ............................................................... 43 
7.8.6 
Effectiveness Analyses ..................................................................... 43 
7.8.7 
Safety Analyses ................................................................................ 45 
7.8.8 
Anabolic Drug Exposure .................................................................. 45 
7.8.9 
Concomitant Use of Osteoporosis Treatment ................................... 46 
7.8.10 
Sensitivity Analyses.......................................................................... 46 
7.8.10.1 
Population Selection ......................................................................... 46 
7.8.10.2 
Safety Endpoints ............................................................................... 46 
7.8.11 
Subgroup Analyses ........................................................................... 47 
7.8.11.1 
Subgroup Analyses for Effectiveness: .............................................. 47 
7.8.11.2 
Subgroup Analyses for Safety: ......................................................... 47 
7.9 
Quality Assurance ............................................................................. 48 

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8 
PROTECTION OF HUMAN SUBJECTS ....................................... 49 
9 
MANAGEMENT AND REPORTING OF ADVERSE 
EVENTS/ADVERSE REACTIONS ................................................ 50 
10 
PLANS FOR DISSEMINATING AND COMMUNICATING 
STUDY RESULTS ........................................................................... 51 
11 
REFERENCES ................................................................................. 52 
12 
APPENDICES .................................................................................. 59 
12.1 
Appendix 1: List of Parameters for Propensity Score Matching ...... 59 
12.2 
Appendix 2: Fracture Algorithm ...................................................... 60 
12.3 
Appendix 3: Cardiovascular Event Codes ........................................ 61 
12.4 
Appendix 4: Diagnostic Codes for All Comorbidities ..................... 62 
12.5 
Appendix 5: National Drug Code (NDC) Codes and HCPCS Codes 
for the Medications Used .................................................................. 63 
 
LIST OF TABLES 
Table 1 
Inclusion Criteria .............................................................................. 33 
Table 2 
Exclusion Criteria ............................................................................. 34 
Table 3 
Metadata About Data Source and Software...................................... 37 
 
LIST OF FIGURES 
Figure 1 
Study Design Schematic ................................................................... 25 
 
 
 
 
 

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LIST OF ABBREVIATIONS 
Abbreviation 
Term 
ABL 
Abaloparatide subcutaneous (abaloparatide-SC; TYMLOS®) 
ACTIVE 
Abaloparatide Comparator Trial in Vertebral Endpoints 
ACTIVExtend 
Abaloparatide Comparator Trial in Vertebral Endpoints extension 
study 
AE 
Adverse event 
AT 
As-Treated (analysis) 
AVA 
Anabolic Versus Antiresorptive 
BMD 
Bone mineral density 
bpm 
beats per minute 
CCI 
Charlson Comorbidity Index 
CDC 
Centers for Disease Control and Prevention 
CFR 
Code of Federal Regulations 
CI 
Confidence interval 
CMS 
Centers for Medicare & Medicaid Services 
CPT 
Common Procedural Terminology 
CQ 
Case qualifying 
CTX 
C-telopeptides of type 1 collagen crosslinks 
CV 
Cardiovascular 
EMA 
European Medicines Agency 
EU 
European Union 
FDA 
Food and Drug Administration 
GCP 
Good Clinical Practice 
CTX 
C-telopeptides of type 1 collagen crosslinks 
CV 
Cardiovascular 
CTX 
C-telopeptides of type 1 collagen crosslinks 
CV 
Cardiovascular 
EMA 
European Medicines Agency 

Sponsor Name: Radius Health, Inc. 
Protocol Number: BA058-05-028 
Protocol Version and Date: Original, V1.0, dated 09 July 2021 
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EU 
European Union 
FDA 
Food and Drug Administration 
GCP 
Good Clinical Practice 
HCPCS 
Healthcare Common Procedure Coding System 
HIPAA 
Health Insurance Portability and Accountability Act 
hPTHrP(1-34) 
First 34 amino acids of human parathyroid hormone-related peptide 
HR 
Hazard ratio 
HRQoL 
Health-related quality of life 
ICD 
International Classification of Diseases 
ICD-9 
International Classification of Diseases, 9th Revision 
ICD9-CM 
International Classification of Diseases, 9th Revision, Clinical 
Modification 
ICD10-CM 
International Classification of Diseases, 10th Revision, Clinical 
Modification 
ICH 
International Council on Harmonisation 
ICSR 
Individual Case Safety Report 
IOF 
International Osteoporosis Foundation 
IRB 
Institutional review board 
IRR 
Incidence rate ratio 
ITT 
Intention-to-treat (analysis) 
MACE 
Major adverse cardiovascular events 
MedDRA 
Medical Dictionary for Regulatory Activities 
MI 
Myocardial infarction 
NDC 
National Drug Code 
NOF 
National Osteoporosis Foundation 
P1NP 
Procollagen type 1 N-terminal propeptide 
PS 
Propensity score 
PTD 
Patient Transactional Dataset 
SAS 
Statistical Analysis Software 
SC 
Subcutaneous 
SD 
Standard deviation 

Sponsor Name: Radius Health, Inc. 
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SmPC 
Summary of Product Characteristics 
TPTD 
Teriparatide subcutaneous (Forteo) 
USA 
United States of America 
WHO 
World Health Organization 

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2. 
PROTOCOL SYNOPSIS 
Title: 
A Retrospective, Observational Cohort Study Evaluating the Effectiveness and 
Cardiovascular Safety of Abaloparatide in Postmenopausal Women New to Anabolic 
Therapies 
Name of Sponsor: 
Radius Health, Inc. (Radius) 
Reference Drug: 
Teriparatide subcutaneous (TPTD; Forteo®) 
Background and Rationale: 
Osteoporosis is a highly prevalent systemic skeletal disorder characterized by 
compromised bone strength predisposing individuals to an increased risk of fractures. 
Anabolic drugs, which can add bone and potentially improve bone microarchitecture, 
have become available as an additional treatment options for individuals with 
osteoporosis. These anabolic drugs include teriparatide (TPTD; Forteo: Eli Lilly and 
Co., Indianapolis, IN, USA), a first-in-class anabolic agent that received United States 
of America (USA) Food and Drug Administration (FDA) approval in 2002, 
abaloparatide (ABL; TYMLOS: Radius Health, Inc., Boston, MA, USA), approved by 
the FDA in 2017 and romosozumab (Romo; Evenity: Amgen and UCB, Thousand 
Oaks, CA, USA), a humanized monoclonal antibody with dual action approved by the 
FDA in 2019. The FDA approval of ABL included consideration of results at 
18 months from the landmark ACTIVE trial and the first 6 months of the 
ACTIVExtend trial, which demonstrated consistent significant and rapid reductions in 
the risk of vertebral and nonvertebral fractures regardless of age, years since 
menopause, presence or absence of previous fracture, and bone mineral density 
(BMD). 

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Research Question: 
The purpose of the current study is to evaluate the real-world comparative 
effectiveness and cardiovascular safety of ABL compared with TPTD during the 
18-month period after treatment initiation in propensity score (PS)-matched cohorts.  

Sponsor Name: Radius Health, Inc. 
Protocol Number: BA058-05-028 
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Objectives and Endpoints: 
Objectives 
Endpoints 
Primary Objective: 
• To evaluate the effectiveness of ABL 
for the treatment of osteoporosis in 
postmenopausal women in the real-
world healthcare setting in the USA 
compared with TPTD using the same 
cohort of PS-matched patients. 
Primary Endpoint: 
• Time to the first incidence of 
nonvertebral fracture (hip, pelvis, 
shoulder, radius or ulna, wrist, femur, 
tibia or fibula, ankle) within the 
18 months after treatment initiation. 
Secondary Objective: 
• To evaluate the cardiovascular safety 
of ABL for the treatment of 
osteoporosis in postmenopausal 
women in the real-world healthcare 
setting in the USA compared with 
TPTD using the same cohort of 
PS-matched patients. 
Secondary Endpoints: 
• Time to the first incidence of the 
composite endpoint of nonfatal MI, 
nonfatal stroke, or in-hospital 
cardiovascular death within the 
18 months after treatment initiation and 
while still on therapy. 
• Time to the first incidence of the 
composite endpoint of nonfatal MI, 
nonfatal stroke, heart failure or in-
hospital cardiovascular death within 
the 18 months after treatment initiation 
and while still on therapy. 
Exploratory Objective: 
• To further evaluate the effectiveness 
and cardiovascular safety of ABL for 
the treatment of osteoporosis in 
postmenopausal women in the real-
world healthcare setting in the USA 
compared with TPTD using the same 
cohort of PS-matched patients. 
Exploratory Endpoints: 
Efficacy 
• Time to the first incidence of hip 
fracture within the 18 months after 
treatment initiation. 
Safety 
• Time to the first incidence of MI 
• Time to the first incidence of stroke 
• Time to the first incidence of -in-
hospital cardiovascular death  
• Time to the first incidence of heart 
failure 
Study Design: 
This is a retrospective observational cohort study using healthcare administrative 
claims data from the USA. 

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Setting: 
This study will use anonymized patient level data from PRA’s Symphony Health 
Patient Source Integrated Dataverse (IDV) database. Data are routinely collected in 
healthcare encounters from all available healthcare sites (inpatient hospital, outpatient 
hospital, physician office, pharmacy, etc.) for all types of provided services including 
specialty, preventive care, and office-based treatments. 
Study Population: 
Patient Population: 
The patients for inclusion in the study analyses will be identified based on the 
prescribed anabolic therapy filled (ABL or TPTD). The study intake period was chosen 
to coincide with the date of the FDA approval of ABL in the USA. 
Data Sources: 
This study will use anonymized patient claims data from the Symphony Health Patient 
Source IDV database including the enhanced hospital data.  
Study Size: 
This retrospective cohort study is expected to include a total of 16,000 patients; 
8000 patients per treatment cohort (ABL or TPTD). 
Data Analysis: 
Comparison of the time to the first incidence of fracture or cardiovascular outcomes 
between the PS-matched treatment cohorts will be based on the 2-sided 95% 
confidence interval of hazard ratio between ABL versus TPTD from a Cox 
proportional hazards model in both the effectiveness and safety analyses, The Kaplan-
Meier method will be used to estimate event rates. Log rank test p-value will be 
calculated. All analyses will be based on the index medication cohort (ABL or TPTD). 
Milestones: 
No interim analyses or reporting are planned. 
 

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3. 
RESPONSIBLE PARTIES 
This study is sponsored by Radius Health, Inc. 
Contact Information: 
 
 
22 Boston Wharf Road, 7th floor Boston MA 02210 
Telephone: 
 
Email: 
 
 

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4. 
VERSION HISTORY 
Version 
Date 
Version 
Number 
Change Log 
Rationale for Change 
09 July 2021 
1.0 
Not applicable. 
Not applicable. 
 

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5. 
RESEARCH QUESTIONS, OBJECTIVES, AND 
ENDPOINTS 
5.1. 
Research Questions 
The purpose of the current study is to evaluate the real-world effectiveness and 
cardiovascular safety of abaloparatide (ABL) compared with teriparatide (TPTD) for the 
treatment of osteoporosis in postmenopausal women during the 18-month period after 
treatment initiation in propensity score (PS)matched cohorts. 

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5.2. 
Objectives and Endpoints 
Objectives 
Endpoints 
Primary Objective: 
• To evaluate the effectiveness of ABL 
for the treatment of osteoporosis in 
postmenopausal women in the real-
world healthcare setting in the United 
States of America (USA) compared 
with TPTD using the same cohort of 
propensity score (PS)-matched 
patients. 
Primary Endpoint: 
• Time to the first incidence of 
nonvertebral fracture (hip, pelvis, 
shoulder, radius or ulna, wrist, femur, 
tibia or fibula, ankle) within the 
18 months after treatment initiation. 
Secondary Objective: 
• To evaluate the cardiovascular safety 
of ABL for the treatment of 
osteoporosis in postmenopausal 
women in the real-world healthcare 
setting in the USA compared with 
TPTD using the same cohort of 
PS-matched patients. 
Secondary Endpoints: 
• Time to the first incidence of the 
composite endpoint of nonfatal MI, 
nonfatal stroke, or in-hospital 
cardiovascular death within the 
18 months after treatment initiation and 
while still on therapy.  
• Time to the first incidence of the 
composite endpoint of nonfatal MI, 
nonfatal stroke, heart failure or in-
hospital cardiovascular death within 
the 18 months after treatment initiation 
and while still on therapy. 
Exploratory Objective: 
• To further evaluate the effectiveness 
and cardiovascular safety of ABL for 
the treatment of osteoporosis in 
postmenopausal women in the real-
world healthcare setting in the USA 
compared with TPTD using the same 
cohort of PS-matched patients. 
Exploratory Endpoints: 
Efficacy 
• Time to the first incidence of hip 
fracture within the 18 months after 
treatment initiation. 
Safety 
• Time to the first incidence of MI 
• Time to the first incidence of stroke 
• Time to the first incidence of in-
hospital cardiovascular death  
• Time to the first incidence of heart 
failure 
 

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5.3. 
Justification of Objectives and Endpoints 
Comparative effectiveness for PS-matched ABL-treated patients versus TPTD-treated 
patients will be evaluated for nonvertebral fracture and hip fracture using a claims-based 
algorithm (Wright et al, 2019). Evaluation will be limited to nonvertebral fractures given 
the evidence requirement from regulatory authorities in the European Union (EU). Hip 
fractures, an important type of nonvertebral fractures that are associated with a greater 
disease burden including functional decline, loss of independence, and increased risk of 
mortality, will also be evaluated. Although hip fractures account for 14% of all fractures, 
they constitute 72% of the cost of osteoporotic-related fracture in the USA (Burge et al, 
2007; Gold et al, 2019; Kochanek et al, 2019). The ACTIVE study was not sufficiently 
powered to demonstrate a significant reduction in hip fracture, but an observational study 
reported a 45% reduction in hip fracture for patients treated with TPTD (Burge et al, 
2017). Evaluation of vertebral fracture incidence will not be carried out given the 
limitation of healthcare claims data that would only capture clinical vertebral fractures 
(ie, those corresponding to a healthcare encounter). Subgroup analyses will be conducted 
based on osteoporosis treatment history, given the variation in response to anabolic 
treatment  by osteoporosis treatment history, and in patients considered to be at high risk 
for fractures. 
Comparative cardiovascular safety for PS-matched ABL-treated patients versus TPTD-
treated patients will be evaluated based on time to the first incidence of the composite 
endpoint of nonfatal myocardial infarction (MI), nonfatal stroke, and in-hospital 
cardiovascular death  within the 18 months after treatment initiation while still on 
therapy. The International Classification of Diseases, 10th Revision, Clinical 
modification (ICD-10-CM) codes for MI, stroke, and heart failure from the Food and 
Drug Administration (FDA) Mini-Sentinel database (FDA, 2021) will be used as a guide 
for event identification with modifications to ensure that relevant endpoints are captured 
(Appendix 4). Published results from a validation study of the MI codes used in the 
Mini-Sentinel database demonstrated a positive predictive value of 86% (Cutrona et al, 
2013). For evaluation of mortality, hospital discharge status will be used. It is important 
to note that hospital claims do not specify the cause of death nor causal association with a 
specific medication. A previously validated claims-based algorithm will be used to derive 
in-hospital cardiovascular death (indirect approach 2, Xie et al, 2018). Compared to a 
previously published fatal MI and stroke method (Ritchey et al, 2017), the algorithm we 
adopted has higher sensitivity while maintaining high specificity.  The net reclassification 
index is improved.    

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6. 
BACKGROUND AND RATIONALE 
6.1. 
Disease Background 
Osteoporosis is a highly prevalent systemic skeletal disorder characterized by low bone 
mineral density (BMD), decreased bone strength, and microarchitectural deterioration of 
bone tissue that leads to enhanced fragility and increased risk of fractures (Rizzoli et al, 
2001). The fractures associated with the greatest morbidity and mortality, as well as an 
economic burden to society, together make up the clinically significant and medically 
relevant group termed major osteoporotic fractures. According to the International 
Osteoporosis Foundation (IOF), approximately 10 million Americans have osteoporosis 
and 44 million more have low bone mass (osteopenia) (IOF, 2020). In the USA, there are 
an estimated 2 million osteoporotic fractures annually (Litwic et al, 2014). At the age of 
50, a woman in the USA has a lifetime risk of approximately 50% for incurring 
osteoporotic fractures (Cosman et al, 2014). As the population ages, the number of 
osteoporotic fractures is certain to increase in both men and women by more than 3-fold 
over the next 50 years (World Health Organization [WHO] 2007). 
One of the major public health problems associated with osteoporosis is its association 
with low-energy trauma or fragility fractures. Fragility fractures affect up to one-half of 
women and one-third of men over the age of 50 years and are often associated with low 
bone density (Cummings and Melton, 2002; Ross, 1996; Jones et al, 1994). Such 
fractures occur most commonly in the hip, spine, and wrist (Cummings and Melton, 
2002; Tosteson et al 2001). Clinical studies have demonstrated that treatment of patients 
with fragility fractures can reduce the risk of future fractures (Delmas, 2002; Hochberg 
2000). Thus, it is important that these patients not only receive treatment for the 
presenting fracture, but also treatment for the prevention of future fractures (Tosi and 
Lane, 1998; Rosier 2001). 

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Spinal fractures have also been associated with poor outcomes and high mortality rates 
(Suzuki et al, 2008). It has been reported that once a patient has sustained a vertebral 
fracture, the risk of a subsequent vertebral fracture increases by > 300% and the risk of a 
subsequent hip fracture increases by 200% (Black et al, 1999). Additional studies have 
shown that almost half of patients with a prior vertebral fracture will experience 
additional vertebral fractures within the next 3 years, many within the first year 
(Robinson et al, 2002; Lindsay et al, 2001). Patients who sustained a vertebral body 
fragility fracture had a prolonged course, which can lead to significant disability even 1 
year later (Suzuki et al, 2008). Patients with a diagnosis of osteoporosis who have 
experienced any fracture have an 86% increase in their risk for another fracture (Kanis et 
al, 2004). With the severity of these implications, prevention of subsequent fractures has 
become a primary focus from a patient care and societal standpoint. 
Major osteoporotic fractures (those of the wrist, shoulder, hip, and clinical spine) account 
for 94% of the fracture risk for women with low or minimal trauma (Ensrud et al, 2016). 
Major osteoporotic fractures contribute to accumulated frailty such that the Frailty Index 
is significantly higher in those elderly women who have experienced a major osteoporotic 
fracture. As a result, these women have worsening frailty and greater morbidity after a 
major osteoporotic fracture (Li et al, 2016). The Frailty Index was associated with a 
predicted increase in the risk of falls, fractures, death, and overnight hospitalizations 
(Li et al, 2014). Consequently, prevention of clinically significant and medically relevant 
major osteoporotic fractures has the potential to reduce health care costs, and reduce the 
risk of falls, fractures, hospitalizations, and death as well as benefit postmenopausal 
women based on reduced frailty. 
Suffering a major osteoporotic fracture substantially increases the risk of subsequent 
fractures (Burshell et al, 2010), and this risk is highest in the first few years after a 
fracture. There is strong evidence demonstrating that after hospital discharge, patients 
with an osteoporotic fracture face higher morbidity, subsequent fractures, and increased 
mortality (Nazrun et al, 2014). Of the estimated 2 million annual osteoporotic fractures in 
the USA, the most common fracture site is the distal radius where 640,000 cases were 
reported in 2001 alone (Litwic et al, 2014). These incident fractures represent an event 
that is associated with a highly elevated risk of a subsequent major osteoporotic fracture 
within the next 1 to 2 years (Barrett-Connor et al, 2009; Chen et al, 2013). Anabolic 
therapy is often recommended for patients at a high risk of future fracture, including 
patients with recent fracture and patients with multiple fractures, but evidence for rapid 
protection of all-site fractures is lacking. 

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Previously, it has been observed that postmenopausal women with osteoporosis have a 
4-fold higher increased risk for cardiovascular events compared with postmenopausal 
women without osteoporosis (Tankó et al, 2005). Additionally, Tanko et al reported that 
postmenopausal women with a low BMD value, bone loss, and presence of previous 
fracture have an increased mortality due to cardiovascular causes. The inverse 
relationship between bone mass and coronary heart disease risk in women found in this 
study is supported by reports that postmenopausal women with low BMD values or a 
greater degree of bone loss have a greater prevalence and severity of aortic calcification, 
a predictor of cardiovascular disease incidence and mortality. This inverse relationship 
was independent of age, education, smoking, alcohol, systolic blood pressure, lipids, 
diabetes, and menopausal history. The inverse relationship between the metacarpal 
cortical area and the incidence of coronary heart disease observed in women might 
suggest that efforts to reduce the risk of osteoporosis may decrease the risk of coronary 
disease in women (Samelson et al, 2004). 
6.2. 
Currently Available Treatment Options  
Treatment options for osteoporosis that are currently available in the EU can be divided 
into 2 broad categories: 
• Antiresorptives: bisphosphonates and denosumab 
• Anabolics: TPTD and romosozumab 
Most of the antiresorptive products provide only a moderate rate of increase in BMD and 
require several years of treatment to reach their fracture reduction benefit. Given that 
ABL is an anabolic, only ABL and the approved anabolics – TPTD and romosozumab – 
are discussed further herein.  
6.2.1. 
Teriparatide 
Teriparatide [rhPTH(1-34)] is closely related to ABL and among its approved European 
Medicines Agency (EMA) indications is the treatment of osteoporosis in postmenopausal 
women (and men) at increased risk of fracture. This approval was based on a significant 
reduction in the incidence of vertebral and nonvertebral fractures. 

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Clinical data supporting anabolic the effects of TPTD include the demonstration of 
elevated levels of the bone formation marker procollagen type 1 N-terminal propeptide 
(P1NP) and the bone resorption marker C-telopeptides- of type 1 collagen crosslinks 
(CTX) in the Anabolic Versus Antiresorptive (AVA) study in patients receiving TPTD at 
20 µg SC daily (Dempster et al, 2016). Levels increased significantly starting at 1 and 3 
months, and continued to rise through 6 months, and were greater for P1NP suggesting 
evidence for a “net anabolic effect”. 
In the AVA study, using quadruple labeling, TPTD increased from baseline all forms of 
bone formation (modeling, remodeling, and overflow modeling bone formation) in the 
cancellous and endocortical bone envelopes and modeling-based bone formation was 
seen in the periosteum (Dempster et al, 2018). 
The Summary of Product Characteristics (SmPC) for teriparatide shows that nausea, pain 
in limb, headache, and dizziness are the most commonly reported adverse reactions. For 
cardiovascular adverse events (AEs), palpitations are noted to be common, but 
tachycardia is uncommon. Orthostatic hypotension is included in the Warnings and 
Precautions section. Despite the occurrence of these class-effect AEs, there is no 
evidence of an increase in major cardiovascular AEs for TPTD. 
6.2.2. 
Romosozumab 
Romosozumab, a monoclonal antibody that acts as a sclerostin inhibitor. was approved in 
2019 for the treatment of osteoporosis in postmenopausal women at high risk of fracture. 
Nonclinical and clinical studies have demonstrated a mixed anabolic/antiresorptive effect 
on bone. Romosozumab was approved based on the results from 2 randomized controlled 
pivotal studies in 2 different populations with different levels of fracture risk. In the 
ARCH study (Saag et al, 2017), romosozumab demonstrated a statistically significant 
reduction in fracture rate at 12 months compared with alendronate, including a reduction 
in the rate of nonvertebral fractures. However, in the placebo-controlled FRAME study 
(Cosman et al, 2016), while the results were positive for the primary endpoint of new 
vertebral fractures, the rate of improvement of nonvertebral fractures did not reach 
statistical significance. 

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Clinical data supporting the anabolic effects of romosozumab include demonstration of a 
rapid increase in bone formation demonstrated by P1NP levels peaking at 1 month, but 
then declining to the baseline level by Month 6 in both the FRAME and ARCH studies. 
In addition, there was evidence of suppression of bone resorption, as CTX levels rapidly 
decreased by 50% from baseline at 1 month and remained at that level throughout the 
remaining 12 months of treatment. 
The histomorphometry completed on patients in the FRAME study at 2 months after 
quadruple label staining demonstrated a doubling from baseline of dynamic bone 
formation indices but bone resorption was halved suggesting this initial gain was 
modelling based; biopsies at 12 months showed that the parameters of dynamic bone 
formation were decreased; there was no significant evidence for cortical porosity 
(Chavassieux et al, 2019). 
In the SmPC, the use of romosozumab is contraindicated in patients with a history of MI 
or stroke. In randomized controlled clinical studies, an increase in serious cardiovascular 
events (MI and stroke) was observed in subjects treated with romosozumab compared 
with control subjects. 

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6.2.3. 
Abaloparatide 
Abaloparatide is a chemically synthesized analog of the first 34 amino acids of human 
parathyroid hormone-related peptide [hPTHrP(1-34)] that is anabolic in bone. Secondary 
pharmacological effects of ABL include transient and reversible increases in heart rate 
after injection, with associated AEs of palpitations and tachycardia observed in the 
pivotal clinical study. A post-hoc analysis was performed to examine the effects of 
treatment on heart rate, blood pressure, and cardiovascular-related AEs, including MACE 
with or without heart failure in the ACTIVE/ACTIVExtend study (Cosman et al, 2020). 
ABL and TPTD transiently increased heart rate relative to placebo. After the first dose, 
the mean (SD) change in heart rate from before treatment to 1 hour after treatment was 
7.9 (8.5) bpm for ABL, 5.3 (7.5) bpm for TPTD, and 1.2 (7.1) bpm for placebo. A similar 
pattern was observed over subsequent visits. The corresponding change in mean supine 
systolic and diastolic blood pressure 1 hour after treatment was -2.7/-3.6 mmHg for 
ABL, -2.0/-3.6 mmHg for TPTD, and -1.5/-2.3 mmHg for placebo. The percentage of 
participants with serious cardiac AEs was similar across the treatment groups (0.9% and 
1.0%). In a post-hoc analysis, the percentage of participants with MACE during the 
ACTIVE study was 0.5% in the ABL group, 0.6% in the TPTD group, and 1.2% in the 
placebo group; MACE + heart failure increased the proportion in the placebo group to 
1.7%. Time-to-first incidence of MACE + heart failure was significantly longer for ABL 
compared with placebo (p = 0.02) and for TPTD compared with placebo (p = 0.04; 
Cosman et al, 2020). During the 2-year ACTIVExtend period, the incidence of MACE, 
with or without heart failure, was similar between the placebo/alendronate and 
ABL/alendronate groups leading to the conclusion that ABL was associated with 
transient increases in heart rate and small decreases in blood pressure in postmenopausal 
women with osteoporosis, with no increase in risk of serious cardiac AEs, MACE, or 
heart failure. 
No published epidemiological studies have examined the cardiovascular risk associated 
with transitory, intermittent increases in heart rate due to an external intervention, as is 
the case with ABL administration, in the general population and in the target population 
of postmenopausal women. A review of data on the long-term effects of TPTD, which 
has a similar mechanism of action to ABL, and whose administration is associated with a 
similar (although slightly less pronounced) increase in heart rate, follow-up study of the 
Fracture Prevention Trial of TPTD versus placebo (N = 1262), did not show a discernible 
increase in cardiovascular disease after 5 years (Tashjian et al, 2006; Prince et al, 2005). 

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6.3. 
Study Rationale 
Currently, no published studies on the comparative effectiveness or cardiovascular safety 
of ABL versus TPTD in a real-world setting are available (Fuggle et al, 2020). There are 
also no real-world data on the cardiovascular safety of these osteoanabolic agents in this 
patient population. In an earlier study, Radius used data from claims and electronic 
medical records to characterize patients new to ABL relative to patients new to TPTD. 
Results from that study were previously reported and the differences in patient 
characteristics in the real-world setting were noted when compared with the 
characteristics of patients in clinical studies (Imel et al, 2020). Furthermore, patients new 
to ABL treatment were similar in their disease characteristics to patients on TPTD. This 
observation reflects comparable market access to the 2 anabolic therapies in addition to 
an equivalence in prescribing recommendations based on clinical practice guidelines 
(Camacho et al, 2020). 
Previously, Radius completed an analysis of Symphony Health Patient Source 
administrative claims data for evaluation of the market dynamics for osteoporosis 
treatments (Williams et al, 2019). The purpose of that study was to characterize patients 
new to anabolic treatment in the real-world setting and to evaluate incidence of fractures 
following treatment initiation. Baseline cardiovascular risk factors (ie, dyslipidemia, 
hypertension, diabetes, and obesity), disease history and cardiovascular events before and 
after treatment initiation were evaluated as exploratory analyses. Because the study 
started shortly after the launch of ABL (TYMLOS), limited longitudinal data were 
available to assess impact of treatment on fracture outcomes. Furthermore, the data 
capture rate for inpatient events was low (only 25% of inpatient events were captured). 
The current protocol incorporates the recommendations received from several national 
scientific advisory meetings in the EU to expand data scope and further evaluate the real-
world comparative effectiveness and comparative cardiovascular safety of ABL versus 
TPTD. First, additional real-world data have been accumulated leading to a larger 
number of evaluable patients with a longer longitudinal data availability. Second, we 
have secured access to the enhanced hospital data through PRA Health Sciences, which 
provide access to 60% of inpatient events. Furthermore, mortality data, which were 
previously not available, can now be determined using hospital discharge records. A 
validated cardiovascular mortality algorithm (Xie et al, 2018) will be used to derive in-
hospital cardiovascular mortality Both in-hospital cardiovascular mortality and all-causes 
mortality will be included.  

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The purpose of the current study is to evaluate the real-world comparative effectiveness 
and comparative cardiovascular safety of ABL versus TPTD during the 18-month period 
after treatment initiation in PS matched cohorts. 
This study will be conducted by PRA Health Sciences using this prespecified protocol 
and statistical analysis plan. In the absence of randomization, this observational study 
will use propensity score (PS) matching for comparative evaluation of safety in addition 
to effectiveness outcomes. Further quality control work has been performed on the data 
including the assignment of codes. The study has reasonable power to show comparable 
effectiveness and safety profile between the 2 active treatment arms. 

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7. 
RESEARCH METHODS 
7.1. 
Study Design 
This will be a retrospective observational study using administrative data for the period 
from May 2012 to January 2021. The most recently available data at the time of data 
extraction will be used. This study will use anonymized patient claims data from PRA’s 
Symphony Health Patient Source Integrated Dataverse (IDV) database with the inclusion 
of enhanced hospital data, which are claims and remittance from the inpatient hospital 
setting and proprietary Patient Transactional Dataset (PTD) claims and prescription data, 
to apply the inclusion/exclusion criteria for patient selection (see Section 7.4.1) to form 
the data set. 
Figure 1 
Study Design Schematic 
 
ABL= abaloparatide; TPTD = teriparatide. 
The Identification Period is the period of time during which subjects will be identified based on their index date 
(01 May 2017 to 30 June 2019). 
The Index Date is the date on which the subject filed their first claim for a prescription for either ABL or TPTD during 
the identification period. 
The Pre-index Period consists of the 5 years before the Index Date during which medical history was available for the 
subject. 
The Post-index Treatment Period consists of the 18 months plus 30 days follow-up after the initiation of treatment 
with anabolic drug (ABL or TPTD) on the index date and continuing for the duration of treatment. 
Treatment Cohorts 
This is a real-world observational study. Treatments will not be assigned. Instead, cohorts 
are defined as follows: 
• Abaloparatide Cohort: Patients who filled ≥ 1 prescription for ABL (TYMLOS) 
as their index medication during the identification period. 

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• Teriparatide Cohort: Patients who filled ≥ 1 prescription for TPTD (Forteo) as 
their index medication during the identification period. 
Evaluation Period 
The date of the first claim for either ABL or TPTD during the identification period 
(between May 1, 2017 and June 30, 2019) will be considered the index date. 
• The medication received on the index date will be considered the index 
medication. 
• Treatment duration may continue for up to 18 months (540 days) after the index 
date. 
• The evaluation of outcomes will start immediately after treatment initiation and 
will continue for 18 months after the index date for both effectiveness and 
cardiovascular safety evaluation regardless of the drug possession gap between 
any 2 prescription fills.  
Primary Analyses 
• The primary effectiveness analysis will be an Intention-to-Treat (ITT) analysis.  
• The primary safety analysis will be an As-Treated (AT) analysis. 
Estimated Sample Size 
Between May 2017 and June 2019, a total of 16,482 unique female patients ≥ 50 years of 
age treated with ABL and 45,350 unique patients ≥ 50 years of age treated with TPTD 
have been identified in the Symphony database. After applying inclusion/exclusion 
criteria, the final number of patients will be determined through PS matching. Based on 
previous studies, it is estimated that approximately 8,000 matched patients will be 
included in each cohort. 
 
 

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7.1.1. 
Rationale for the Current Study Design 
Currently, there are no published studies on the effectiveness of ABL in the real-world 
setting. Furthermore, neither the comparative effectiveness nor comparative safety of 
ABL versus TPTD have been previously reported. In addition to clinical outcomes 
evaluated in randomized controlled studies, real-world effectiveness data are important in 
guiding treatment decisions. An evaluation of real-world effectiveness will provide data 
on a broader population of patients beyond the patient population included in the 
ACTIVE trial. Romosozumab is excluded from this study because of its association with 
an increased risk of cardiovascular events. 

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7.1.2. 
Strengths of the Study Design 
The methodology for the current study design has several strengths: 
• Data are from multiple payers and geographically diverse settings across the 
USA, and the PRA IDV data captures over 90% of pharmacy claims in the USA. 
The prescription claims are for prescriptions filled not just prescriptions written. 
As such, the findings from the study are expected to have a high generalizability. 
• A claims-based algorithm, which has a high specificity and has been shown to 
have over 90% accuracy in previous studies, will be used (Wright et al, 2019). 
• Although this is not a randomized study, PS-matching will be used to define study 
cohorts and provide confidence that the 2 cohorts were comparable in their 
probability to receive and benefit from treatment. Patients will be matched on all 
indicators of disease severity and fracture risk, including fracture and treatment 
history as per evidence-based clinical practice guidelines (Camacho et al., 2020). 
Furthermore, patients will also be matched on conditions associated with 
increased risk of fall and the requirement for treatments associated with poor bone 
health or quality. 
7.1.3. 
Limitations of the Study Design 
There are several limitations associated with the design of the current study: 
• The data source is administrative claims data, which are not collected for research 
purposes. Administrative claims data have inherent limitations including coding 
errors, inconsistencies, underestimation, overestimation, or incomplete diagnoses 
data. The current study, however, will use a claims-based algorithm to identify 
case qualifying (CQ) fragility fractures associated with osteoporosis. Furthermore, 
any misclassification of fractures is nondifferential between the treatment groups 
being compared and should not affect the results. 
• Claims-based studies include potential inaccuracies related to the use of 
prescription medications. The prescription claim is for the date of first fill and not 
the date of first use of the medication, so the assumption is that these are the same 
date. 

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• Detailed clinical data such as BMD T-scores or the presence of other risk factors 
(eg, family history, smoking status, alcohol intake) associated with disease 
severity and increased future fracture risk are not available and, therefore, no 
adjustments have been made for these factors. 
• Only deaths recorded on the hospital discharge status are available for the 
derivation of cardiovascular death in this study. The current study, however, will 
use a claims-based algorithm to identify deaths that are likely to be caused by 
cardiovascular events. Furthermore, any misclassification of deaths is 
nondifferential between the treatment groups being compared and should not bias 
the results. 
The comparative evaluation of treatment options is ideally done within the controlled 
clinical study setting. The current study is observational, and treatments are not assigned. 
As such, randomization is not possible. Although this is not a randomized study, PS 
matching will be used to define the study cohorts and provide confidence that the 
2 treatment groups are comparable in their probability to receive and benefit from 
treatment. Patients will be matched on all indicators of disease severity and fracture risk 
including prior disease and treatment history. Furthermore, patients will also be matched 
on history of falls as well as comorbid conditions and concomitant medications 
associated with increased fall risk or with bone quality and strength. While several 
comorbidities were initially considered, some measures were ultimately not included in 
the end due to lack of available data. This includes duodenitis. Lastly, there could be 
residual confounding despite matching. The study is planned as an intention-to-treat 
(ITT) analysis for effectiveness evaluation and as-treated (AT) analysis for safety 
evaluation with additional consideration for duration of treatment exposure using 
sensitivity analyses on various length of observed treatment duration for the study 
cohorts. Interpretation is limited by the variable length of follow-up. 

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7.1.4. 
Generalizability 
All patients will be required to have a total of 12 months of pre-index medical and 
prescription data. This may result in a study population that is less healthy with a higher 
incidence of fracture compared with that reported in ACTIVE. Regardless, this selection 
bias is not likely to differentially affect the 2 treatment cohorts. Results of the study are 
expected to be generalizable to the population of managed care enrollees including 
commercial and Medicare Advantage members. Regardless of this limitation, the data are 
representative of a broad population of patients from multiple payers and are 
geographically diverse across the USA. The study will provide additional information on 
real-world use and outcomes in patients new to treatment with ABL outside of the 
clinical study setting. 
7.2. 
Setting 
This study will use anonymized patient claims data from PRA (Symphony Health) IDV 
database. Data are routinely collected in healthcare encounters from all available 
healthcare sites (inpatient hospital, outpatient hospital, physician’s office, etc.) for all 
types of provided services including specialty, preventive care, and office-based 
treatments. 
The rationale for the study population and the generalizability of the results of these 
analyses to the general population of postmenopausal women with osteoporosis are 
discussed in Section 7.4.2. 
This is a retrospective observational study evaluating real-world data and, therefore, there 
are no assigned study visits. The study uses a convenience sample. Patients in the 
Symphony database who meet the study inclusion/exclusion criteria (Table 1 and 
Table 2) will be included in the analyses. 
7.3. 
Duration of Subject Participation 
Because this is a retrospective claims-based cohort study, subjects included in the 
analyses will not actively participate in the study and no study visits will be required. The 
study will use anonymized patient data. 
Both effectiveness and cardiovascular safety outcomes will be assessed for patients with 
18 months of follow-up data from their index date. 

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7.4. 
Study Population 
7.4.1. 
Subject Selection Criteria 
Table 1details the inclusion criteria and Table 2 provides the exclusion criteria for this 
study. 
 

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Table 1 
Inclusion Criteria 
Criterion 
Details 
Order of 
Application 
Assessment 
Window 
Code Type 
Diagnosis 
Position 
Applied to 
Study 
Populations 
Varied for 
Sensitivity 
Source for 
Algorithm 
Women 
Sex female 
Before 
selection of the 
index date 
Any time 
NA 
NA 
Yes 
No 
NA 
Age: ≥ 50 years 
≥ 50 years of age Before 
selection of the 
index date 
[0, 0] 
NA 
NA 
Yes 
No 
Proxy for 
postmenopausal 
≥1 prescription fill 
for ABL or TPTD 
between May 1, 
2017 and June 
30, 2019 
(identification 
period) 
Will determine 
the index date 
May 1, 2017 
and June 30, 
2019 
NDC 
NA 
Yes 
Yes. Further 
restriction on 
various 
duration of 
treatment 
NA 
≥ 1 claim for 
medical or hospital 
visit and a 
pharmacy claim 
in the 12 months 
before the index 
date 
Before 
selection of the 
index date 
0-12 months 
pre-index 
NDC, ICD, 
HCPCS 
Any 
Yes 
Yes 
PRA 
proprietary 
algorithm to 
determine 
coverage 
ABL = abaloparatide; NA= not applicable; NDC = National Drug Code; post-index = after the index date; pre-index = before the index treatment date; 
TPTD = teriparatide. 

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Table 2 
Exclusion Criteria 
Criterion 
Details 
Assessment 
Window 
Code Type 
Diagnosis 
Position 
Applied to 
Study 
Populations 
Varied for 
Sensitivity 
Source for 
Algorithm 
Paget’s disease 
Presence of ≥ 1 claim 
for Paget’s disease 
Pre-index 
ICD 
Any  
Yes 
No 
NA 
Malignancy  
Presence of ≥ 1 claim 
except for 
nonmelanoma skin 
cancers, carcinoma in-
situ of the cervix, 
ductal carcinoma in-
situ of breast 
Pre-index 
ICD 
Any 
Yes 
No 
NA 
Indicators of high 
disease burden and 
high risk of death 
CCI > 10  
Pre-index 
ICD 
Any 
Yes 
No 
NA 
With prior index 
anabolic treatment 
Anabolic treatment 
includes ABL, TPTD 
and romosozumab 
Pre-index 
NDC and 
HCPCS 
NA 
Yes 
No 
NA 
Switch to a different 
anabolic treatment 
after index 
Anabolic treatment 
includes ABL, TPTD 
and romosozumab 
Index-date to 18 
months after 
NDC and 
HCPCS 
NA 
Yes 
No 
NA 
CCI = Charlson Comorbidity Index; ICD = International Classification of Diseases; NA= not applicable; pre-index = before the index treatment date; post-index 
= after the index date. 
 

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7.4.2. 
Discussion of Study Population 
The study eligibility criteria are in alignment with the prescribing information in the FDA 
approved labels for both ABL and TPTD and include women with postmenopausal 
osteoporosis. Patients with secondary osteoporosis, Paget’s disease, as well as those who 
would not be candidates for the anabolic drug due to high morbidity burden or high risk 
of mortality (ie, malignancies), are excluded. Patients with prior alendronate therapy will 
be included because almost half of the population of patients new to anabolic therapies 
have previously used bisphosphonates. Although the treatment guidelines suggest 
initiation of therapy with bone building agents followed by antiresorptive agents to 
maintain bone, market access criteria requiring step therapy and prior authorization for 
anabolic agents result in the use of anabolic agents later in the course of disease 
progression and severity (McClung et al, 2017; Tsourdi et al, 2017; Zanchetta et al, 
2018). Limiting the study population to those without any prior therapy would 
significantly reduce the size of the study population. Therefore, subgroup analyses will be 
performed based on prior use of osteoporosis treatment. Furthermore, there is no 
evidence indicating diminished treatment response to anabolic therapy following 
alendronate use (Cosman et al, 2017; Langdahl et al, 2018). As such the study criteria 
provide a suitable population for evaluation of the effectiveness and cardiovascular safety 
of ABL without compromising sample size yet minimizing potential selection biases. 

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7.5. 
Data Sources 
This study will use anonymized patient level data from PRA’s Symphony Health IDV 
database, including the enhanced hospital data. PRA’s IDV database is a large nationally 
representative claims-based database. The database cross-sectionally covers over 80% of 
the population in the USA (approximately 300 million lives) annually. It includes claims 
submitted to all payer types, including commercial plans, Medicare, and Medicaid. In 
terms of the pharmacy claims, it captures approximately 92% of the retail and 65% 
specialty pharmacy claims, and approximately 68% of mail order claims. For medical 
claims, it covers approximately 60% of professional claims in an outpatient setting. 
Medical claims are open unadjudicated claims. Mortality data recorded on the hospital 
(medical facility) discharge status covers about 1/3 of the total death records According 
to National Center for Health Statistics data published on CDC wonder (CDC, 2021), 
35% total deaths in 2019 happened at medical facility which includes hospital inpatient, 
outpatient or emergency room, death on arrival or unknow status. This number is slightly 
high (37%) for cardiovascular death (cause of death is I00 to I99 in ICD-10). For women 
at age 50, 54% percentage of cardiovascular death occurred in medical facilities. The data 
are de-identified, in compliance with Health Insurance Portability and Accountability Act 
(HIPAA) guidelines, with stable unique identifiers to allow for longitudinal tracking over 
time. The terms of the Research Exception provisions of the Privacy Rule, 45 Code of 
Federal Regulations (CFR) Part 164.514(e) exempts Institutional Review Board (IRB) 
approval for this nonexperimental study, which is fully HIPAA compliant. 
Table 3 summarizes the calendar time range used to ascertain cohort entry (index date), 
as well as the calendar time range of data available for pre-index- assessment windows 
and post-index follow up (study period). The data source name and version are identified, 
as well as any sampling criteria applied (for example, the data cut only includes patients 
with a diagnosis of diabetes). If data linkage is involved, provide a citation or an 
appendix with description of the linkage (how, performance characteristics). 
All individual data will be de-identified and signed data license agreements will be in 
place for all data sources. 

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Table 3 
Metadata About Data Source and Software 
Data Sources: 
PRA (Symphony Health’s) Integrated Dataverse (IDV) database. 
Study Period: 
May 1, 2012 to January 31, 2021 
Eligible Cohort Entry 
Period: 
May 1, 2017 to June 30, 2019 
Data Extraction 
Date/Version: 
June – 2021 via a single data pull 
Data Sampling/ Extraction 
Criteria: 
Refer to Section 7: Research Methods for study design, treatment 
cohorts, evaluation periods, and subject selection criteria. 
Types of Data: 
Medical, hospital and prescription claims data. 
Data Linkage: 
No data linkage will be required. 
Data Conversion: 
No data conversion will be required. 
Software to Create Study 
Population: 
 software used to connect to the 
Cloudera Hadoop CDH 7.1.4-1 database for data extraction. R software 
might be used for propensity score matching. 
 
7.6. 
Variables for Data Extraction 
The endpoints to be assessed in this study are provided in Section 5.2. 
7.6.1. 
Demographics and Baseline Variables 
Potential confounding variables include the following: 
• Demographic variables (age, race/ethnicity if available) 
• Insurance information (commercial, Medicare, Other) 
• Patient geographic region (majority coverage includes West, South, Northeast, 
and Midwest) 
• ABL and TPTD prescriber physician’s specialty (endocrinology, rheumatology, 
surgery, family medicine, internal medicine, nursing related, obstetric 
• Index date year quarter  

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7.6.2. 
Risk Factors 
Conditions associated with increased risk of fractures or falls (Camacho et al, 2020), 
including pre-index fracture history, for which data are available in administrative claims, 
will be assessed by the presence of ICD codes in the claims database. History of 
co-morbidities associated with an increased risk of falls and/or fractures, as well as those 
conditions associated with poor bone health, will be assessed (see Section 7.6.3 for the 
list of co-morbidities). 
Data for medical history associated with a high risk of cardiovascular events will also be 
extracted: MI, stroke, and heart failure.  
7.6.3. 
Co-morbidities 
Known co-morbidities identified in the target patient population that will be included in 
this retrospective cohort study are: 
• Comorbidities that increase the risk of fall: prior history of any stroke, mobility 
impairments, vision impairments, hearing impairments, Parkinson’s disease, 
muscle atrophy, muscle weakness, or sarcopenia. 
• Comorbidities that lengthen healing time: diabetes, liver disease, or renal 
disease. 
• Other comorbidities associated with an increased risk of fractures or 
requiring treatment associated with an increased fracture risk: hypertension, 
dyslipidemia, arthritis, respiratory diseases including asthma and chronic 
obstructive pulmonary disease, depression, anxiety, sleep disorders, 
cardiovascular disease, hypothyroidism, obesity, or gastrointestinal disorders. 

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7.6.4. 
Osteoporosis Treatment History 
Pharmacy claim for previous use of any of the following medications: 
• Bisphosphonates (ie, alendronate, ibandronate, risedronate, or zoledronate) 
• Denosumab 
• Oral glucocorticoids 
• Hormone replacement therapy 
7.6.5. 
Concomitant Medications 
Concomitant osteoporosis medications expected to be used in the target patient 
population include the following: 
• Bisphosphonates (ie, alendronate, ibandronate, risedronate, zoledronate) 
• Denosumab 
• Hormone replacement therapy 
7.6.6. 
Anabolic Drug Exposure 
Cohort anabolic drug exposure is defined as a prescription filled for either abaloparatide 
(ABL cohort) or teriparatide (TPTD cohort). Exposure will be determined using 
pharmacy claims data in the NDC Codes and HCPCS Codes for Medications. For a list of 
codes please see Appendix 5. 
7.6.7. 
Outcomes 
Both effectiveness and cardiovascular safety outcomes will be assessed for patients with 
18 months of follow-up data after their index date. Cardiovascular safety outcomes will 
be evaluated during the treatment period which is from index date to the last prescription 
anabolic drug possessed within the 18 months after the index date, regardless of the 
anabolic drug possession gap between any 2 prescription fills. 

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7.6.7.1. 
Evaluation of Effectiveness 
A claims-based algorithm with high specificity for fracture site developed by Wright et al 
(Wright et al, 2019) will be used to identify osteoporosis-related fractures (see 
Appendix 2). Case qualifying (CQ) fractures are those identified during an inpatient 
hospital stay, in any position on the medical claim, or in an outpatient setting 
accompanied by a fracture repair procedure code. Validation of this algorithm was done 
using International Classification of Diseases, 9th Revision (ICD-9) codes before the 
Healthcare System in the USA switched to the ICD-10 system. In a separate study, 
Wright et al mapped the ICD-9 codes to ICD-10 codes in an evaluation of hip fracture 
trends in the Medicare data. The results from that study did not suggest that the ICD-10 
version systematically identified a higher or lower number of fractures than the ICD-9 
codes.  
The evaluation period for nonvertebral fracture events will start from the index date (date 
of initial prescription dispensed) to the earliest of the following events: 
a) First non-vertebral fracture event date (hip, pelvis, shoulder, radius or ulna, wrist, 
femur, tibia or fibula, ankle) 
b) 18 months after the index date 
c) In-hospital death 
The evaluation period for hip fracture events will start from the index date (date of initial 
prescription dispensed) to the earliest of the following events: 
a) first hip fracture event date 
b) 18 months after index date 
c) In-hospital death 

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7.6.7.2. 
Evaluation of Cardiovascular Safety 
Evaluation of cardiovascular safety outcomes will be carried out for patients who have 
18 months of follow-up data from their index date while on treatment with ABL or 
TPTD. The following data variables will be extracted: 
• End of treatment date which is the last prescription date + supply day 
• Date of the first incidence of MI 
• Date of the first incidence of stroke 
• Date of the first incidence of heart failure 
• Date of in-hospital cardiovascular death  
• Date of all-cause in-hospital death 
7.7. 
Data Management 
For this study there will be no new primary data collected. The study will be conducted 
using real-world (secondary) data for analysis. 
 software 
will be used to connect to the Cloudera Hadoop (CDH) 7.1.4-1 database for data 
extraction. R software may be used for PS matching. Datasets and tables, figures, and 
listings (TFLs) will be created on a Citrix Windows platform using SAS V9.4 M6. 
7.8. 
Statistical Methods and Analyses 
The study will use a convenience sample of patients who meet the study selection 
criteria. Medical and pharmacy claims will be used to determine the presence of 
conditions. International Classification of Diseases Clinical Modification, 9th Revision 
(ICD-9-CM) and 10th Revision (ICD-10-CM) codes will be used to assess comorbidities. 
National Drug Code (NDC) codes will be used to identify the anabolic drug (ABL or 
TPTD) and to identify prior and concomitant medications. Additionally, the Current 
Procedural Terminology (CPT) and Healthcare Common Procedure Coding System 
(HCPCS) will be used to support the derivation. 
Detailed code lists are included in Fracture Algorithm (Appendix 2), Cardiovascular 
Event Codes (Appendix 3), Diagnostic Codes for All Comorbidities (Appendix 4), and 
NDC Codes and HCPCS Codes for Medications (Appendix 5). 

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7.8.1. 
Power and Sample Size 
7.8.1.1. 
Sample Size 
All patients who meet the inclusion criteria and do not meet any of the exclusion criteria 
will be included in the analysis. After PS matching, it is estimated that approximately 
16,000 patients will be included in the analyses, 8,000 patients in each cohort. 
7.8.1.2. 
Power Calculation 
• Abaloparatide versus teriparatide with a noninferiority margin of 30% 
o Assuming a nonvertebral fracture rate of 3.5% at the end of 18 months 
o 8,000 matched subjects in each group 
o 95% power for HR up to 1.3 
7.8.2. 
Creation of Analytic Cohorts and Source Data Characteristics 
A flow diagram will be provided to reflect the order of operations used to create the 
analytic cohort from the source database. Temporality of assessment windows will be 
provided relative to the cohort entry (index date) including additional data to denote 
exclusion/inclusion criteria and how the final sample was derived. 
A table about the source of data (pharmacy claims, medical claims, enhanced hospital 
claims) will be provided. The table will include calendar time cohort entry (index date) 
and calendar time range of available data in the pre-index and post-index assessment 
windows. 
7.8.3. 
Analysis Populations 
Analysis population are all patients meeting the study inclusion/exclusion criteria and 
selected after propensity matching. The same matched population will used for both 
effectiveness and safety analysis.    
The effectiveness evaluation will be conducted using an ITT analysis, meaning that the 
first fracture event during the 18 months (+ 30 days follow-up) after the index date will 
be summarized regardless of when treatment ended. Cardiovascular safety outcomes will 
be evaluated using an As-Treated (AT) analysis based on only events occurring during 
the treatment period which is from the index date to the last prescription anabolic drug 
obtained within the 18 months after the index date, regardless of the anabolic drug 
possession gap between any 2 prescription fills.  

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7.8.4. 
Baseline Descriptive Statistics 
Summary statistics will be provided for all demographic and clinical characteristics 
(Section 7.6.1) for both before and after PS matching of patients to the 2 treatment 
cohorts (ABL and TPTD). (See Appendix 4 for the list of baseline comorbidities, prior 
medications, previous fractures, fall risk, and prior cardiovascular risks.) Numbers and 
percentages will be provided for dichotomous and polychotomous variables. Means, 
medians, standard deviations, will be provided for continuous variables. 
7.8.5. 
Propensity Score Matching 
In the absence of randomization, logistic regression-based PS matching will be used to 
create the analytic cohorts from all patients meeting the study inclusion/exclusion criteria. 
A greedy matching algorithm with no replacement will be adopted. The default caliper, 
the number of standard deviations of the distance measure within which to draw control 
unit, will be set at 0.20 (Austin et al, 2011). Additional sensitivity analyses using a tighter 
(0.15) or wider (0.3) caliper will be carried out. Cohorts will be prospectively specified to 
match on age, prior fracture history, osteoporosis-related hospitalization, and chronic 
comorbidities and concomitant medications during the pre-index period that are 
associated with an increased fall risk (see Appendix 1 for the full list of matching 
variables). The R software MatchIt package (https://cran.r-
project.org/web/packages/MatchIt/MatchIt.pdf) will be used to find matched pairs. 
Pre-match and post-match will be evaluated using prespecified criteria for mean standard 
difference to ensure that PS matching was successful. After matching, the mean standard 
difference on each covariate between ABL and TPTD is expected to be < 0.10. 
7.8.6. 
Effectiveness Analyses 
In this study, the primary analyses of effectiveness will be noninferiority analyses for 
ABL versus TPTD on time to the first incidence of nonvertebral fractures with a margin 
of 30%. Noninferiority of ABL to TPTD will be concluded if the upper bound of 2-sided 
95% CI of HR between ABL versus TPTD is < 1.3. 

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In a recently published historical control observational study evaluating the real-world 
effectiveness of osteoporosis therapies using Medicare claim data between 2008 and 
2011, the fracture incidence rates for the 12-month pre-treatment period were compared 
with the 12-month on-treatment period (Yusuf et al, 2018). For patients treated with 
TPTD, the incidence rate ratio (IRR [95% CI]) was 0.36 (0.31 to 0.41) for any fracture 
and 0.34 (0.32 to 0.36) for hip fracture. Based on the EMA and FDA noninferiority 
margin guidelines (EMA, 2005; FDA, 2016), if the upper boundary of ABL versus TPTD 
is HR < 1.3, then ABL will preserve approximately 70% of the TPTD benefit, which is 
higher than the minimum required threshold of 50%.  
Comparisons of the time to fracture between the PS-matched ABL and TPTD groups will 
be based on a Cox proportional hazards model. The hazard ratio (HR) and 95% 
confidence interval (CI) between the 2  treatment groups will be calculated. The Kaplan-
Meier method will be used to estimate event rates. Log rank test will be calculated. 
For the fracture outcomes, and the corresponding sensitivity and subgroup analyses, 
patients will be followed for up to 18 months or until their first fracture event (for the 
fracture site of interest under evaluation), whichever comes first. All analyses are based 
on index medication cohort. 
The duration, in days, from the date of the first filled prescription for ABL or TPTD to 
the first incidence of nonvertebral fracture will be derived. If a patient does not 
experience any nonvertebral fractures during the 18 months of treatment + 30-day 
follow--up (for a total of 19 months), the patient will be censored at the day of in-hospital 
death or the 19 months. 
For the fracture outcomes, an ITT analysis will be applied. Patients will be followed for 
up to 18 months + 30-day follow-up, or until their first fracture event (for the fracture site 
of interest under evaluation) or in-hospital death, whichever comes first. Duration, in 
days, from the index date to the last follow-up date will be calculated. For calculation 
purposes, 1 month is equivalent to 30 days. All analyses are based on the patient’s index 
medication cohort. 
A nonvertebral fracture is any fragility fracture at the hip, pelvis, femur, ankle, shoulder 
(including shoulder, humerus, clavicle), radius/ulna, wrist, or tibia/fibula. Derivation 
details are provided in Appendix 2. 

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The time to the first incidence of hip fracture and the other 7 individual fracture sites will 
be summarized using similar methods as those used for nonvertebral fracture. 
The Kaplan-Meier curves will be generated to graphically display the fracture event or 
18 months plus 30-day follow-up. 
7.8.7. 
Safety Analyses 
A Cox proportional hazards model will be used for the time to first cardiovascular event. 
HR and 95% CIs will be presented. 
The ICD-10 diagnosis from the hospital claim and physician claim will be used to derive 
post-index myocardial infarction (MI) (I21.x, I22.x), stroke (I61.x – I63.x), or heart 
failure (I50.x, excluding I50.x2, I50.8x). Hospital discharge status code (20 to 29 or 40 to 
42) will be derived for in-hospital death. A detailed code list is provided in Appendix 3. 
Subjects with in-hospital cardiovascular death derived from indirect approach 2 (Xie et 
al, 2018) will be included in the analyses. 2018 ICD-10 CM and General Equivalence 
Mappings (GEMS) from the Centers for Medicare & Medicaid (CMS) ( will be used to 
translating ICD-9 CM to ICD-10 CM (CMS, 2018).  
The As-Treated (AT) analysis will be conducted for the safety evaluation. The first 
incidence of a cardiovascular event after the index date and before the 30 days after the 
end of treatment will be analyzed. 
A cardiovascular event is the first incidence of the composite endpoint of nonfatal MI, 
nonfatal stroke, or in-hospital cardiovascular death; the first incidence of the composite 
incidence of nonfatal MI, nonfatal stroke, heart failure or in-hospital cardiovascular 
death; or the first incidence of MI, stroke, heart failure, or in-hospital cardiovascular 
death separately.  
7.8.8. 
Anabolic Drug Exposure 
The overall duration of cohort anabolic drug exposure will be summarized for 
PS-matched patients. 
Overall Duration of Anabolic Drug Exposure (day) = Date of last anabolic drug 
prescription fill + Supply days – index date + 1 
 

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Consecutive treatment duration is determined from the index date to the last drug supply 
date without any gap exceeding  60 days.   
Because the efficacy and safety evaluations are limited to an 18-month period, the overall 
duration of the anabolic drug exposure will be cut off at 540 days, even if the patient used 
the anabolic drug (ABL or TPTD) for > 18 months. 
7.8.9. 
Concomitant Use of Osteoporosis Treatment 
Between the index date and the end of the Treatment Period (18 months + 30-day follow-
up period), use of any osteoporosis treatment options other than ABL or TPTD will be 
summarized for the PS-matched populations. 
7.8.10. 
Sensitivity Analyses 
7.8.10.1. Population Selection 
To evaluate the stability of the PS matched cohorts, 2 different calipers (0.15, 0.3) will be 
carried out to the PS matching. The sensitivity analyses on effectiveness and safety 
endpoints will be performed on these matching populations.  
Sensitivity analyses on effectiveness and safety endpoints will be performed on sub 
population with each of additional exclusion criteria: 
• Exclude patients without a minimum of 12 months of anabolic drug exposure: 
(1) 12-consecutive months of anabolic drug exposure as assessed by medication 
dispensed; and 
(2) 12-month cumulative anabolic drug exposure during the 18-month follow-up 
• Exclude patients with prescriptions dispensed for ≤ 1 month, 3 months, 6 months, 
and 9 months 
• Exclude patients with previous use of denosumab or zoledronic acid 
7.8.10.2. Safety Endpoints 
All-cause in-hospital death without any restriction will replace in-hospital cardiovascular 
death as the sensitivity analysis on secondary safety endpoints.  
The cardiovascular events (MI, stroke, and heart failure) in the secondary endpoint is the 
first post-index incidence recorded on a hospital claim or physician claim. To evaluate 
possible overestimation, new incident of MI, stroke, or heart failure as no previous 
diagnosis in the 183 days preceding the index date as in the Sentinel Initiative (Cutrona et 
al, 2013) will be carried as a sensitivity analysis. 

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7.8.11. 
Subgroup Analyses 
The absence of BMD data is a limitation of the study because BMD is often used for risk 
assessment. Instead, we propose to evaluate patients based on previous fracture history, 
which is the greatest predictor of future fracture risk (Banefelt et al, 2019; 
Balasubramanian et al, 2019). 
We propose to have a subgroup analysis of patients with previous bisphosphonate use. 
Most (64.7%) of patients in the EU had previously used bisphosphonates (Langdahl et al, 
2018). Additional subgroup analyses include evaluation of outcomes by age group and by 
race/ethnicity. 
7.8.11.1. Subgroup Analyses for Effectiveness: 
The following subgroup analyses will be performed: 
• Age group: < 75 years vs. ≥ 75 years 
• Race/ethnicity: White, Hispanic, African American, Asian, Other, Unknown, 
Missing 
• Prior use of bisphosphonates within 5 years before the index date: with vs. 
without 
• Prior fracture within 1 year before the index date: with vs. without 
Each subgroup will be PS-matched separately to ensure the comparability between the 
patients in the ABL and TPTD groups. 
7.8.11.2. Subgroup Analyses for Safety: 
Subgroup analyses will be performed by: 
• Age group (age < 75 years vs. age ≥ 75 years) 
• Race/ethnicity: White, Hispanic, African American, Asian, Other, Unknown, 
Missing  
• With or without prior cardiovascular risk 
• With or without MI or stroke within 1 year before the index date.  

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Each subgroup will be PS-matched separately to ensure the comparability between ABL 
and TPTD. 
7.9. 
Quality Assurance 
Outlined by phase below is PRA Medical Informatics’ Codes Review and Quality 
Control procedure: 
1. Pre-Programming 
Quality Control 
In this phase, the Internal Developer will ensure access to all 
data tables is available, as well as ensure all clinical codes 
given by the Clinical Lead are aligned with PRA internal 
standard reference data management tables in the database. 
2. Code Quality 
Control 
Documentation 
In this phase, the Internal Developer will ensure the QC 
documentation captures each step of the coding process and 
details all requirements each step is required to implement, 
the database tables used and/or created as well as the desired 
outcomes. 
3. Code Quality 
Control Review 
In this phase, the Internal Developer and the Peer QC Analyst 
will walk through the entirety of the code section by section 
ensuring the code matches the Technical Specifications 
Document and is producing the desired outcomes. A Quality 
Gate Acceptance/Failure designation will be assigned at each 
step in the QC documentation. If a Quality Gate fails at a 
specific point, the root cause of the failure will be determined 
(ie, coding errors, problematic business rules, etc.) and 
appropriate corrective action will be taken. 
If there has been a coding error, the Internal Developer will 
correct the error, re-run the code, and produce the requisite 
outputs. If one or more business rules are causing problems, 
the business rules may need to be modified. Any business rule 
modifications will need Project Manager sign-off and be 
detailed on any documentation going back to the client. 
Regardless of the failure, the Quality Control phases will be repeated to ensure the issues 
have be corrected. (if so, modifications to the business rules may be necessary to match 
the produced results), or data (if so, additional steps may be required to clean the data as 
needed to achieve the expected results). 

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8. 
PROTECTION OF HUMAN SUBJECTS 
The terms of the Research Exception provisions of the Privacy Rule, 45 CFR 164.514(e) 
exempt Institutional Review Board approval for this non-experimental study, which is 
fully HIPAA compliant. Data are maintained in a de-identified manner, thus are not 
subject to institutional review board. 
No patients will be directly contacted for participation in the study and no intervention 
will be provided as part of the protocol. The study will use a secondary data source 
containing de-identified data. 

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9. 
MANAGEMENT AND REPORTING OF ADVERSE 
EVENTS/ADVERSE REACTIONS 
The design of this study is characterized by secondary use of data previously collected 
from health insurance claims. Thus, for this study the submission of suspected adverse 
reactions in the form of Individual Case Safety Reports (ICSRs) is not required. The 
study design was retrospective and there were no claims adjudication. No validated 
algorithm was used to confirm events were incident events. Claims are therefore only 
“proxies” for events. 

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10. 
PLANS FOR DISSEMINATING AND COMMUNICATING 
STUDY RESULTS 
The study will be registered on ClinicalTrials.gov. The results from this retrospective 
cohort study evaluating real-world evidence will be submitted as a manuscript to a peer-
reviewed journal. 

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12. 
APPENDICES 
Appendix 1: List of Parameters for Propensity Score Matching 
 

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Appendix 2: Fracture Algorithm 
A claims-based algorithm with high specificity for fracture site will be used for 
evaluation of treatment effectiveness (Wright et al, 2019). 
1. All claims with a fracture diagnosis in any position will be evaluated starting 5 
years before index to 19 months (18 months plus 30 days follow up, 1 month = 30 
days) after index date. 
2. Case-qualifying (CQ) fractures are those identified during an inpatient stay or in 
an outpatient setting accompanied by a fracture repair procedure code on the same 
day. 
3. CQ fracture diagnoses and non-CQ fracture diagnoses will then be used to extend 
the fracture episode for evaluation of subsequent fractures. Episodes at the same 
site continued until a gap of 90 days is observed between consecutive claims. 
Fracture diagnoses will be based on primary or secondary ICD-9 code before October 
2015 or ICD-10 code after that date listed on the same claim. 

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Appendix 3: Cardiovascular Event Codes 
 

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Appendix 4: Diagnostic Codes for All Comorbidities 
 

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Appendix 5: National Drug Code (NDC) Codes and HCPCS Codes for 
the Medications Used