Comparative Effectiveness of New Initiators of Tofacitinib and Other Biologic/Targeted Synthetic DMARDs in Patients With Rheumatoid Arthritis
Conditions: Rheumatoid Arthritis
Sponsor: Pfizer

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NON-INTERVENTIONAL (NI) STUDY PROTOCOL
Study Information
Title
Comparative Effectiveness of New Initiators of Tofacitinib and 
Other Biologic/Targeted Synthetic DMARDs in Patients with 
Rheumatoid Arthritis
Protocol number
A3921445
Protocol version 
identifier
1.0
Date
07 May 2024
Medicinal Product
Tofacitinib
Research question 
and objectives
What is the effectiveness of tofacitinib compared to biologic disease-
modifying antirheumatic drugs (bDMARDs) in rheumatoid arthritis 
(RA) patients, overall and by subgroups based on demographic and 
clinical characteristics?
What is the effectiveness of tofacitinib compared to other 
bDMARDs in tumor necrosis factor inhibitor (TNFi)-experienced 
RA patients?
The objectives of this study are to:
1. To compare the real-world effectiveness of tofacitinib 
compared to other advanced treatments in RA patients 
utilizing assessments performed in routine clinical care
a.
New initiators [regardless of treatment history, in 
alignment with United States (US) Food & Drug 
Administration (FDA) and European Medicines Agency 
(EMA) labeled indications]:
i. Tofacitinib versus TNFi (as a group, defined 
as etanercept, adalimumab, certolizumab, 
golimumab, infliximab) 
ii. Tofacitinib versus abatacept
iii. Tofacitinib versus tocilizumab / sarilumab (as 
a group)
2. To explore the comparative effectiveness of tofacitinib 
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compared to other advanced treatments in RA patients, 
stratified by demographic and clinical characteristics
3. To compare the real-world effectiveness of tofacitinib
compared to other advanced treatments in TNFi-experienced 
RA patients utilizing assessments performed in routine 
clinical care
a.
Tofacitinib versus abatacept
b. Tofacitinib versus tocilizumab/sarilumab
Author
 
Pfizer, Inc.
66 Hudson Boulevard East
New York, NY 10001-2192, USA
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1. TABLE OF CONTENTS
1. TABLE OF CONTENTS.......................................................................................................3
2. LIST OF ABBREVIATIONS................................................................................................5
3. RESPONSIBLE PARTIES....................................................................................................7
4. ABSTRACT...........................................................................................................................7
5. AMENDMENTS AND UPDATES.......................................................................................7
6. MILESTONES.......................................................................................................................8
7. RATIONALE AND BACKGROUND..................................................................................9
8. RESEARCH QUESTION AND OBJECTIVES .................................................................10
9. RESEARCH METHODS ....................................................................................................11
9.1. Study Design ...........................................................................................................11
9.2. Setting......................................................................................................................11
9.2.1. Inclusion Criteria ........................................................................................11
9.2.2. Exclusion Criteria.......................................................................................12
9.3. Variables..................................................................................................................12
9.3.1. Exposures....................................................................................................12
9.3.2. Covariates ...................................................................................................13
9.3.3. Patient Follow-Up.......................................................................................16
9.3.4. Outcomes....................................................................................................16
9.4. Data Sources............................................................................................................17
9.5. Study Size................................................................................................................17
9.6. Data Management ...................................................................................................17
9.7. Data Analysis ..........................................................................................................18
9.7.1. Objective 1: Real-World Comparative Effectiveness of Tofacitinib in 
New Initiators of Select bDMARDs................................................................18
9.7.2. Objective 2: Treatment Effect Heterogeneity in Assessing the 
Comparative Effectiveness of Tofacitinib vs. bDMARDS..............................19
9.7.3. Objective 3: Real-World Comparative Effectiveness of Tofacitinib 
versus New Initiators of Select bDMARDs in TNFi-experienced 
Patients.............................................................................................................20
9.8. Quality Control........................................................................................................20
9.9. Limitations of the Research Methods......................................................................20
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9.10. Other Aspects ........................................................................................................21
10. PROTECTION OF HUMAN PARTICIPANTS ...............................................................22
10.1. Patient Information................................................................................................22
10.2. Patient Consent......................................................................................................22
10.3. Institutional Review Board (IRB)/ Ethnics Committee (EC)................................22
10.4. Ethical Conduct of the Study ................................................................................22
11. MANAGEMENT AND REPORTING OF ADVERSE EVENTS/ADVERSE 
REACTIONS ......................................................................................................................23
12. PLANS FOR DISSEMINATING AND COMMUNICATING STUDY RESULTS........23
13. REFERENCES ..................................................................................................................24
14. LIST OF TABLES.............................................................................................................26
15. LIST OF FIGURES ...........................................................................................................26
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2. LIST OF ABBREVIATIONS
Abbreviation
Definition
AE
adverse event
BID
bis in die (twice a day)
BMI 
body mass index 
CCI
Charlson Comorbidity Index
(Anti-)-CCP
cyclic citrullinated peptide
CDAI
Clinical Disease Activity Index
CI 
confidence interval
CRP
C-reactive protein
(c)/(b)/(ts)DMARD
(conventional)/(biologic)/(targeted synthetic) Disease-Modifying 
Anti-Rheumatic Drugs
CV
cardiovascular
EMA
European Medicines Agency
EMR
Electronic Medical Record
FDA
Food & Drug Administration
GPP
Good Pharmacoepidemiology Practices 
HR
hazard ratio
ICD
International Classification of Diseases
IPCW
inverse probability of censoring weights
IPTW
inverse probability of treatment weighting
ISPE
International Society for Pharmacoepidemiology
ISPOR
International Society for Pharmacoeconomics and Outcomes 
Research
IRB
Institutional Review Board
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Abbreviation
Definition
JAKi
janus kinase inhibitor
JIA
juvenile idiopathic arthritis
MACE
major adverse cardiovascular event
NIS
non-interventional study
NMSC
non-melanoma skin cancer
PASS
post-authorization safety study
PhRMA
Pharmaceutical Research and Manufacturers Association
PS
Propensity Score
PsA
psoriatic arthritis
QC
quality control
RA
rheumatoid arthritis
RCT
randomized controlled trial
RF
rheumatoid factor
SQL
Structured Query Language 
TNFi
Tumor Necrosis Factor inhibitor
UC
ulcerative colitis
US
United States
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3. RESPONSIBLE PARTIES
Principal Investigator(s) of the Protocol
Name, Degree(s)
Job Title
Affiliation
Address
Milena Gianfrancesco, 
PhD MPH
Director, Epidemiology 
Scientist; Global Medical 
Epidemiology
Pfizer, Inc.
66 Hudson Boulevard East
New York, NY 10001-
2192, USA
Jose Rivas, MD
Sr. Director, Medical 
Strategy Head 
Rheumatology
Pfizer, Inc.
Av. de Europa, 20, B, 
28108 Alcobendas, 
Madrid, Spain
Genevieve Gauthier
Director, I&I RWE 
Scientist
Pfizer, Inc.
17300 Trans-Canada 
Highway Kirkland, QC, 
Canada, H9J 2M5
4. ABSTRACT
None.
5. AMENDMENTS AND UPDATES
None.
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6. MILESTONES
Milestone
Planned Date
Final Protocol
15 May 2024
Start of data collection 
16 May 2024
End of data collection
15 July 2024
Final study report
31 December 2024
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7. RATIONALE AND BACKGROUND
Rheumatoid arthritis (RA) is a common, systemic autoimmune inflammatory disease, 
characterized by synovial inflammation leading to pain, swelling, stiffness, and progressive 
destruction and deformity of small and large joints. Patients experience impaired physical 
function, social participation, and health-related quality of life. Patients also have increased 
risk of significant comorbidities that are not musculoskeletal. For RA, therapeutic options 
currently available include conventional synthetic disease-modifying anti-rheumatic drugs 
(cDMARDs), biological DMARDs (bDMARDs), and targeted-synthetic DMARDs 
(tsDMARDs). Strategies have been developed to treat RA by inhibiting Janus kinase (JAK) 
pathways and the first JAK inhibitor approved for the treatment of RA in the U.S. was 
tofacitinib (Xeljanz®; Pfizer; November 2012).1
Unlike biological therapies, such as TNFi, monoclonal antibodies that markedly inhibit one 
cytokine pathway over an extended period of time, JAK inhibition by tofacitinib results in a 
pattern of partial and reversible inhibition of the intracellular effects from several 
inflammatory cytokines. Tofacitinib is a potent, selective inhibitor of the JAK family of 
kinases with a high degree of selectivity relative to other kinases in the human genome.
As a commitment to the US FDA, Pfizer conducted the ORAL Surveillance Study 
(A3921133),2 a post-authorization safety study (PASS). A3921133 evaluated the risk of 
major adverse cardiovascular event (MACE) and malignancies excluding non-melanoma 
skin cancer (NMSC) with tofacitinib (5 and 10 mg BID) versus TNFi in patients with 
moderately or severely active RA who had an inadequate response to methotrexate and who 
were 50 years of age or older and had at least 1 additional cardiovascular (CV) risk factor. In 
February 2019, a safety analysis of ongoing study, A3921133, reported the frequency of 
pulmonary embolism and all-cause mortality in patients receiving tofacitinib 10 mg BID was 
higher than in patients treated with a TNFi. On 26 July 2019, the FDA approved new 
warnings about an increased risk of blood clots and death with the 10 mg twice daily dose of 
tofacitinib, which was only approved for ulcerative colitis (UC). Use of the 10 mg twice 
daily dose became limited beyond the induction phase. The approved use of JAKi for UC 
also became limited to patients who demonstrated inadequate response or intolerance to one 
or more TNF blockers.
In January 2021, in the final analysis of ORAL Surveillance, for the combined tofacitinib 
doses (5 and 10 mg BID) versus TNFi, non-inferiority was not shown for either adjudicated 
MACE or adjudicated malignancies (excluding NMSC). In December 2021, a JAKi class 
label
extended the post-TNFi restriction to all gastroenterological and rheumatologic indications
for all future approved JAKi in the US. These findings also led to a review by the EMA, not 
resulting in a change in the line of therapy, but a limitation of the use of JAKi in certain 
patient sub-populations (only be used if no suitable treatment alternatives are available in 
patients: 65 years of age and older; patients with history of atherosclerotic cardiovascular 
disease or other cardiovascular risk factors (such as current or past long-time smokers); 
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patients with malignancy risk factors (eg, current malignancy or history of malignancy).
Though the efficacy and safety profile of tofacitinib has been established in prior randomized 
controlled trials,3,4,5 the real-world effectiveness of tofacitinib in routine care settings 
compared to frequently used bDMARDs is lacking. The evidence from prior RCTs is limited 
by the relatively small sizes and due to strict eligibility criteria, which reduces the 
generalizability of the results. For example, there is limited evidence on the effectiveness of 
tofacitinib compared to individual agents such as abatacept and within clinically relevant 
subgroups such as patients with prior use of tumor necrosis factor inhibitors (TNFi). The 
majority of the RCT populations required background use of methotrexate and excluded 
patients who were treated with other bDMARDs. Though there are some studies of 
tofacitinib using real-world data,6-9 they tend to be limited by sample size, and primarily 
focused on safety outcomes with limited research on effectiveness outcomes such as clinical 
disease activity.10,11 The purpose of this study is to evaluate the real-world effectiveness of 
patients treated with tofacitinib compared to other select bDMARD in relation to indicated
use according to the FDA12 and EMA labels,13 as well as within relevant subgroups 
harnessing data from the US based OM1 PremiOM™ RA dataset (OM1, Inc., Boston, MA). 
8. RESEARCH QUESTION AND OBJECTIVES
Research Questions:

What is the effectiveness of tofacitinib compared to bDMARDs in RA patients, 
overall and by subgroups based on demographic and clinical characteristics?

What is the effectiveness of tofacitinib compared to other bDMARDs in TNFi-
experienced RA patients?
The objectives of this study are to:
1. To compare the real-world effectiveness of tofacitinib compared to other advanced 
treatments in RA patients utilizing assessments performed in routine clinical care
a.
New initiators (regardless of treatment history, in alignment with FDA and EMA 
labeled indications):
i.
Tofacitinib versus TNFi (as a group, defined as etanercept,
adalimumab, certolizumab, golimumab, infliximab) 
ii.
Tofacitinib versus abatacept
iii.
Tofacitinib versus tocilizumab/sarilumab (as a group)
2. To explore the comparative effectiveness of tofacitinib compared to other advanced 
treatments in RA patients, stratified by demographic and clinical characteristics.
3. To compare the real-world effectiveness of tofacitinib compared to other advanced 
treatments in TNFi-experienced RA patients utilizing assessments performed in 
routine clinical care
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a.
Tofacitinib versus abatacept
b. Tofacitinib versus tocilizumab/sarilumab
9. RESEARCH METHODS 
9.1. Study Design 
This is a retrospective cohort study to evaluate the real-world effectiveness of patients with 
RA treated with tofacitinib compared to other select bDMARDs using data from the U.S. 
based OM1 PremiOM™ RA dataset (OM1, Inc., Boston, MA). A graphical summary of the 
study design is provided below.
9.2. Setting
The study period is from January 2013 through the date of the most recent data available at 
the time of analysis (anticipated to be December 2023). All available data will be included in 
the analysis; the length of follow-up time will vary per patient.
9.2.1. Inclusion Criteria
Eligibility (inclusion and exclusion) criteria will be assessed at baseline using all available 
data prior to and including the cohort entry date. All patients will have at least 180 days of 
baseline information available by design. 
Patients must meet the following inclusion criteria to be eligible for inclusion in the study:
1. Age ≥18 years on the cohort entry date. 
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2. Diagnosed with RA at any time prior to cohort entry date:
a.
At least two RA diagnosis codes at least 30 days apart, each coming from 
an encounter with a rheumatologist;
b. At least one inpatient visit with a RA diagnosis code;
c.
At least two outpatient records with a RA diagnosis code at least 30 days 
apart and within a year, regardless of physician specialty; or
d. At least one outpatient record with an RA diagnosis and a prescription or 
fill for a DMARD from a specified list and does not have any of the non-
RA conditions for which those drugs may also be prescribed.
3. Initiation of specified b/tsDMARDs of interest for treatment of RA (ie, tofacitinib, 
etanercept, adalimumab, certolizumab, golimumab, infliximab, abatacept,
tocilizumab, or sarilumab).
4. At least 180 days of baseline data available prior to and including the cohort entry 
date.
5. At least one Clinical Disease Activity Index (CDAI) score in 45 days prior to and 
including the cohort entry date (baseline).
9.2.2. Exclusion Criteria
Patients meeting any of the following criteria will not be included in the study:
1. Patients diagnosed with concomitant indications for tofacitinib [psoriatic arthritis 
(PsA), UC, and polyarticular course juvenile idiopathic arthritis (pcJIA)] at any time
prior to cohort entry date, determined by at least two (2) diagnosis codes at least 30 
days apart and prior to baseline.
2. Patients with >1 b/tsDMARD (ie, tofacitinib, etanercept, adalimumab, certolizumab, 
golimumab, infliximab, abatacept tocilizumab, or sarilumab) prescribed on index 
date.
9.3. Variables
9.3.1. Exposures
The study cohorts will be constructed using a new-user, active comparator design14 as three 
pairwise comparisons between tofacitinib initiators and initiators of a comparator medication. 
Treatment initiation will be defined as no use of the study medication at any time prior to 
cohort entry (For example, a tofacitinib initiator will be required to have no tofacitinib use 
prior to cohort entry, however they are allowed to use other medications). The start date of a 
patient’s b/tsDMARD will be termed the cohort entry date (also known as index date or time 
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zero). The cohort entry date will be operationalized as the first day of a prescription, fill, or 
administration for an eligible study medication. The pairwise comparisons are summarized 
below.
Table 1.
Cohort Overview
Cohort
Exposure
Comparator
Cohort 1
Tofacitinib
TNFi (as a group, defined as etanercept, adalimumab, certolizumab, 
golimumab, infliximab)
Cohort 2
Tofacitinib
Abatacept
Cohort 3
Tofacitinib
Tocilizumab or sarilumab
9.3.2. Covariates
Potential confounders that will be considered include demographics, baseline RA disease 
severity, comorbidities, lab values and medication use and are detailed in Table 2. 
Confounders will be assessed using all available data prior to and including the cohort entry 
date unless otherwise stated (the baseline covariate assessment period). 
Table 2.
Study Variable Overview
Variable
Timing
Operational Definition
Cohort entry date
The start date of a patient’s b/tsDMARD will be termed the 
cohort entry date (also known as index date or time zero)
Baseline covariate 
assessment period
All available data 
prior to and 
including the 
cohort entry date
All available data prior to and including the cohort entry 
date unless otherwise stated
Follow-up period
1 day after the 
cohort entry date
Patients will be followed up for outcomes within their pre-
defined outcome assessment windows. The primary 
analysis will use an outcome assessment window of 135-
225 days (ie, 180±45) after the cohort entry date to assess 
outcomes. A secondary analysis will use an outcome 
assessment window of 320-410 days (ie, 365±45) after the 
cohort entry date to assess outcomes
Treatment 
discontinuation
To define treatment discontinuation, medication-specific 
eras (referred to as drug eras hereafter) covering the period 
of one or more prescriptions, fills, or administrations for the 
index medication will be collapsed into a single period of 
continuous use if the end date of the prior prescription, fill, 
or administration falls within 90 days of the start date of the 
next. Treatment is defined as discontinued if there is no 
start date within 90 days after the end date of the first drug 
era. If the last encounter date is within 90 days after the first 
drug era end date, the treatment is considered as “ongoing” 
(ie, not discontinued).
Baseline characteristics
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Variable
Timing
Operational Definition
Age (years), continuous
On the cohort 
entry date
Year of the index date minus the year of birth
Age, categories
On the cohort 
entry date
18-24
25-39
40-64
65+
Duration of continuous 
enrollment prior to 
cohort entry date
Year of index date
On the cohort 
entry date
Race
Most recent prior 
to and including 
cohort entry date
White, Black, Asian, Other, Unknown/Not documented
Ethnicity
Most recent prior 
to and including 
cohort entry date
Hispanic, Non-hispanic, Unknown/Not documented
Geography
Most recent prior 
to and including 
cohort entry date
U.S. Census Division

East North Central

East South Central

Middle Atlantic

Mountain

New England

Pacific

South Atlantic

West North Central

West South Central

Unknown
Insurance type
Most recent prior 
to and including 
cohort entry date
Commercial
Medicare
Medicaid
Other
Multiple
Unknown
RA disease duration 
(categorical)
Baseline
Categorical (< 2 years, ≥ 2 years)
BMI Continuous
Most recent prior 
to and including 
cohort entry date
Numeric BMI (in kg/m²)
BMI Categorical
Most recent prior 
to and including 
cohort entry date
Underweight: <18.5
Normal weight: 18.5-<25
Overweight: 25- <30
Obese: ≥30
Unknown
Smoking status
Most recent prior 
to and including 
cohort entry date
Ever smoker
Never smoker
Unknown
RA disease severity
Baseline
Continuous CDAI [mean (SD); median (Q1, Q3)]
Categorical CDAI: 
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Variable
Timing
Operational Definition
1.
Remission: 0.0-2.8
2.
Low activity: 2.9-10.0
3.
Moderate activity: 10.1-22.0
4.
High activity: 22.1-76.0
Joint count:
●
Tender joint count (0-28)
●
Swollen joint count (0-28)
Charlson Comorbidity 
Index
Baseline
Charlson Comorbidity Index score as of the cohort entry 
date using diagnosis codes from claims and EMR data 
during the baseline period.
Charlson Comorbidity 
Index
Baseline
Calculated category on the cohort entry date
●
0-1
●
2-3
●
4-5
●
6+
Comorbidities
Baseline
Binary yes/no variables for present prior to and including 
the cohort entry date, defined based on at least two ICD 
diagnosis codes at least 30 days apart:
●
Cardiovascular disease (coronary artery disease, 
atherosclerosis, peripheral arterial disease, 
hypertension, dyslipidemia)
●
Chronic obstructive pulmonary disease
●
Fibromyalgia
●
Malignancies
●
Osteoporosis
●
Type 1 diabetes
●
Type 2 diabetes
●
Sjӧgren’s syndrome
Laboratory results
Most recent prior 
to and including 
cohort entry date
●
RF (positive (high, low), negative/unknown)
●
Anti-CCP (positive, negative/unknown)
●
CRP (categorical <3 mg/L versus ≥3 mg/L)
Prior cDMARD use
Baseline
A binary (yes/no) variable indicating use of any of the 
following medications
●
cDMARDs (auranofin, azathioprine, 
cyclophosphamide, cyclosporine, gold sodium 
thiomalate, hydroxychloroquine, leflunomide, 
methotrexate, minocycline, penicillamine, 
sulfasalazine)
Prior bDMARD use
Baseline
A binary (yes/no) variable indicating use of any of the 
following medications
●
bDMARDs (abatacept, adalimumab, anakinra, 
certolizumab, etanercept, golimumab, infliximab, 
rituximab, sarilumab, tocilizumab
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Variable
Timing
Operational Definition
Prior tsDMARD use
Baseline
A binary (yes/no) variable indicating use of any of the 
following medications
●
tsDMARDs (baricitinib, tofacitinib, upadacitinib)
Concurrent cDMARD 
use
On the cohort 
entry date
A binary (yes/no) variable indicating use of any of the 
following medications at baseline or at any time while on 
the index medication:
●
cDMARDs (azathioprine, hydroxychloroquine, 
leflunomide, methotrexate, sulfasalazine)
Number of prior 
b/tsDMARD therapies
Baseline
Categorical (≥2, <2; >1, <1)
Corticosteroid use
180 days prior to 
and including the 
cohort entry date
Prescription, fill or administration of oral or injectable 6 
months prior to index, binary yes/no
Any hospitalization
30 days prior to 
and including the
cohort entry date
Any inpatient encounter
Dose of index 
Tofacitinib
On the cohort 
entry date
Dose: 5 mg vs 11 mg, frequency: Once vs twice daily
9.3.3. Patient Follow-Up
Patients will be followed up for outcomes within their pre-defined outcome assessment 
windows. Outcomes will be examined at two timepoints: an outcome assessment window of 
135-225 days (ie, 180±45) after the cohort entry date; and a window of 320-410 days 
(ie, 365±45) after the cohort entry date. Patient follow-up will begin 1 day after cohort entry 
and will end at the occurrence of an outcome or a censoring event: 1) if they discontinue their 
index treatment, 2) switch to a different ts/bDMARD, 3) are lost to follow-up, or 4) on the 
last day of the outcome assessment window for each analysis, whichever comes first.
9.3.4. Outcomes
The primary outcome for this study will be a documented (yes/no) improvement or 
maintenance (depending on the patients’ baseline CDAI score) in disease activity defined as 
a first record of low disease activity/remission which corresponds to a CDAI score lesser 
than or equal to 10.0 during the pre-defined outcome assessment windows. The outcome will 
be analyzed as a time-to-event variable using survival analysis methods. Cox proportional 
hazards regression will be used to analyze the outcome as time-to-event data, which accounts 
for the impact of censoring (eg, due to treatment discontinuation or switch) on the 
relationship between treatment and the event of interest. Further explanation is provided in 
Section 9.7.1. 
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Table 3.
Clinical Outcomes
Outcomes
Clinical Disease Activity 
Index (CDAI)
Follow-up [135-
225 days 
(ie, 180±45) and 
320-410 days 
(ie, 365±45) after 
the cohort entry 
date]
Remission or low disease activity: 0.0-10.0
9.4. Data Sources 
A retrospective design will be used to explore the comparative effectiveness of tofacitinib 
among a cohort of patients with RA in the U.S. using the OM1 PremiOM™ RA dataset 
(OM1, Inc., Boston, MA). This dataset of over 244,000 patients with RA is derived from 
deterministically linked, de-identified, individual-level healthcare claims and EMR data. 
EMR data are derived from several healthcare systems and rheumatologist’s EMR provider 
systems geographically representative of the U.S. population. The EMR data include 
encounters, medication history and prescription information, laboratory results, PROs, and 
clinical observations as documented by a rheumatologist. Additional medical and pharmacy 
claims data containing coding history on inpatient and outpatient encounters from clinics, 
acute care facilities, or ambulatory surgical centers are linked to the clinical data described 
above to fill information gaps in patients’ clinical care. At the time of analysis, the OM1 
PremiOM™ RA dataset covered the time period from 01 January 2013 through 
31 December 2023.
9.5. Study Size
The sample size is not determined based on a formal statistical power analysis. Preliminary 
feasibility analyses indicated a sample size range of approximately 10,705 patients with RA 
with a prescription of tofacitinib in the OM1 dataset, and 44,595 patients with a prescription 
for a b/tsDMARD other than tofacitinib. Sample size will not be fully determined until all 
eligibility criteria are applied.
9.6. Data Management 
To ensure the quality and integrity of research, this study will be conducted under the 
Guidelines for Good Pharmacoepidemiology Practices (GPP) issued by the International 
Society for Pharmacoepidemiology (ISPE), the principles outlined in the Belmont Report 
(Ethical Principles and Guidelines for the Protection of Human Subjects of Research), and 
any applicable national guidelines. Following these best practices, OM1 has standard 
operating procedures including internal quality audits, rules for secure and confidential data, 
methods to maintain and archive project documents, quality-control procedures for 
programming, and standards for writing analysis plans.
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9.7. Data Analysis 
Statistical analysis of this study will be the responsibility of OM1, Inc., with review and 
approval by Pfizer. Any change to the data analysis methods described in this study protocol 
will require an amendment ONLY if it changes a principal feature of the study protocol. Any 
other changes to the data analysis methods described in the study protocol and the 
justification for making the changes will be described in the study report or equivalent 
document. Analysis datasets will be prepared by Structured Query Language (SQL). Data 
analysis will be performed using R or SAS Software (SAS Institute, Cary, NC, U.S.), 
Version 9.4.
Continuous variables will be summarized by mean (standard deviation [SD]) and median 
(Q1, Q3). Categorical variables will be summarized by count and percentage for each 
category. Depending on the distribution of data, very small categories (for example, 
<5 patients) may be combined with a larger category for easier interpretation. The 
denominator for the percentages will be the number of patients with non-missing data. The 
number of patients with missing data will be presented, but these patients will not be 
included in the denominator in the calculation of percentages. 
9.7.1. Objective 1: Real-World Comparative Effectiveness of Tofacitinib in New 
Initiators of Select bDMARDs
Statistical analysis:
Objective 1 will aim to assess the comparative effectiveness of tofacitinib initiators vs 
bDMARDs in achieving low disease activity as assessed by CDAI score. 
We will use inverse probability of treatment weighting (IPTW) using stabilized weights15 to 
adjust for baseline confounders. Patients will be weighted by the inverse probability of the 
treatment they actually received. For tofacitinib initiators the IPTW will be = 1/propensity 
score (PS), whereas, for the comparator medication the IPTW will be = 1/(1-PS). 
Stabilization will be done by incorporating the marginal probability of the treatment they 
actually received in the numerator of the weights.16 Other weighting methods such as overlap 
weights17,18 will be considered as an alternative if IPTW leads to extreme weights despite 
stabilization. Overlap weights are a weighting analogue of PS matching that targets an 
estimand similar to PS matching by standardizing the covariate distribution to those patients 
where there is overlap of the PS distributions. For each patient, the overlap weight will be 
calculated as the probability of receiving the opposite treatment (or the treatment they didn’t 
receive). For tofacitinib initiators the overlap weight will be =(1 - PS), whereas, for a 
comparator medication the overlap weight will be = (PS).
The propensity score will be estimated (separately in the three pairwise cohorts) using 
logistic regression as the predicted probability of initiating tofacitinib vs. the comparator 
medication as a function of the baseline covariates. Baseline patient characteristics before 
and after weighting will be tabulated, and covariate balance will be assessed using 
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standardized differences. Standardized differences <0.1 indicate adequate balance between 
treatment groups.19
Number of events and incidence rates will be reported for each exposure group. Weighted 
hazard ratios (HR) and 95% confidence intervals (CIs) will be estimated in each weighted
cohort using Cox regression with robust standard errors to account for the weighting. Two 
timepoints will be examined: an outcome assessment window of 135-225 days (ie, 180 ± 45) 
after the cohort entry date; and an outcome assessment window of 320-410 days after the 
cohort entry date. 
Reasons for censoring will be reported separately for each exposure group and potential bias 
due to censoring will be evaluated and may be addressed analytically using inverse 
probability of censoring weights (IPCW). Additional sensitivity analyses may be conducted 
to further examine any bias due to censoring/loss-to-follow-up that may be associated with
the exposure. This may include methods such as bias analysis with imputation of extreme 
values for missing outcome data, or intention to treat analyses (ie, not censoring at switch).
9.7.2. Objective 2: Treatment Effect Heterogeneity in Assessing the Comparative
Effectiveness of Tofacitinib vs. bDMARDS
In objective 2, analyses from objective 1 will be stratified to assess treatment effect 
heterogeneity. Crude hazard ratios (95% CIs) will be reported within predefined subgroups 
from the overall weighted population in each of the three cohorts. Thus, it will be assumed 
that the PS generated across the entire cohort is also valid for the subgroups.20 The 
pre-defined subgroups include the following; however, the final subgroups will be 
determined after running feasibility checks to ensure adequate sample size. 

Age (≥65 vs. <65; ≥50 vs. <50)

Sex 

Disease duration (categorical, at least 2 years versus less than 2 years)

Obesity (≥30 kg/m2 vs. <30 kg/m2 at value closest to index)

Baseline disease activity (CDAI >10 versus CDAI<=10)

Number of documented prior b/tsDMARD therapies (≥2 vs. <2, ≥1 vs. <1)

Rheumatoid factor (“RF”) status (positive, negative/unknown), in patients with Rh 
status available, if sample size allows

Anti-CCP status (positive, negative/unknown)

CRP level (closest to baseline, categorical <3 mg/L versus ≥3 mg/L)
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
Corticosteroid use at index date (± 30 days)

Smoking status (ever smoker, never smoker, unknown)

Comorbid Sjӧgren's syndrome (yes, no; if sample size allows)
9.7.3. Objective 3: Real-World Comparative Effectiveness of Tofacitinib versus New 
Initiators of Select bDMARDs in TNFi-experienced Patients
Objective 3 will aim to assess the comparative effectiveness of tofacitinib initiators vs other 
bDMARDs in achieving low disease activity as assessed by CDAI score within Cohorts 2 
& 3 amongst a subgroup of patients with TNFi use in the 365 days prior to the cohort entry 
date. 
The analytic methods used in objective 3 will be identical to that of objective 1. 
9.8. Quality Control
OM1 acquires data from disparate sources through batch transfers of data files. After
receiving data, OM1 performs robust Quality control (QC) checks on the raw data (from the 
sources) as well as the processed data received. As the data goes through a series of 
normalization, harmonization, and enrichment processes, it is subjected to a series of 
industry-standard data quality checks which are further augmented with additional 
OM1-defined data quality checks whereby quality metrics and associated artifacts are 
gathered and reviewed by subject matter experts within OM1’s Clinical Informatics and 
Research groups. Based on the findings within these checks, additional processing and 
resolution steps are implemented, and the steps are repeated. These steps are repeated until 
there are no data quality issues identified during these checks. These QC checks follow 
standard dimensions of quality, such as fidelity of the data through completeness and 
correctness, representativeness, validity, usability, and fit-for-use of the data. Additional 
quality checks are then performed when end points are defined and programmed to ensure 
validity, correctness, accuracy, and conformity to the study requirements. 
OM1 performs deduplication on the data received through disparate data sources as part of 
processing whereby redundant patient records are removed from the OM1’s Real-World Data 
Cloud, preventing fragmented and duplicated data points and records from being 
incorporated into the Cloud and ensuring that the most updated and accurate patient 
information is processed and represented in OM1's data.
9.9. Limitations of the Research Methods
The employment of real-world health history data for analysis may be subject to issues of 
missingness or incomplete data capture, compounded by errors stemming from inaccuracies 
in data recording or transcription which may lead to misclassification. 
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As this is an observational study, residual confounding due to unmeasured confounders 
cannot be ruled out. Data collection will reflect routine clinical practice rather than 
mandatory assessments at prespecified time points, which may have an impact on the amount 
of data and its interpretation. Patients who do not return for an office visit during the 
outcome assessment windows, who may actually have a CDAI<10, will not be captured in 
our study. Some patients may also be lost to follow-up before the pre-defined outcome 
assessment windows which may lead to some under-ascertainment of actual outcomes. If this 
under-ascertainment is differential, then there is the potential that the study results may be 
impacted by a substantial bias either towards or away from the null. If a treatment is 
ineffective in a given patient and that treatment is discontinued prior to measurement of 
outcomes, removal of patients for whom the treatment is ineffective could induce substantial 
bias where only patients who do well on treatment are included in the analysis. It is possible 
that a drug that has no effect on the outcome could appear beneficial because only patients 
who benefit from the drug are included in the analysis of outcomes. Sensitivity analyses 
(such as, but not limited to, intent to treat analysis, where patients are not censored at 
treatment switch or discontinuation) and examination of assumptions of non-differential loss-
to follow-up will be required for the results to have a useful interpretation. 
Another source of bias that may affect results if outcome measurement is related to the 
assessor’s knowledge of the patient’s condition. For example, if a physician believes the 
patient will have a poor CDAI score (or a very good CDAI score), they may be less likely to 
perform that assessment. This phenomenon could bias results either towards or away from 
the null. Further sensitivity analyses will be needed to quantify the potential impact of this 
bias, where the assumptions around lack of measurement at the time point of interest are 
examined. However, if the censoring process is sufficiently captured by the measured 
baseline covariates that also predict outcome occurrence, then such bias can be mitigated 
using IPCW. 
The study population includes patients with CDAI scores ≤10 at baseline who may be more 
likely to experience the study outcome and the proportion of these patients may be different 
across exposure groups. To account for this, baseline CDAI scores will be included as a 
covariate to estimate the PS to ensure balance on this variable. Further, stratified analyses 
will be conducted (sample-size permitting) by baseline CDAI score. 
Finally, treatment effects reported in stratified analyses conducted as part of Objective 2 
should be interpreted with caution as these analyses will likely be limited by sample size. 
9.10. Other Aspects
Not applicable.
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10. PROTECTION OF HUMAN PARTICIPANTS
10.1. Patient Information 
This study involves data that exist in deidentified/anonymized structured format and contain 
no patient personal information. 
10.2. Patient Consent
As this study involves de-identified/anonymized structured data, which according to 
applicable legal requirements do not contain data subject to privacy laws, obtaining informed 
consent from patients by Pfizer is not required.
10.3. Institutional Review Board (IRB)/ Ethnics Committee (EC)
This study will comply with all applicable laws regarding subject privacy. No direct subject 
contact or primary collection of individual human subject data will occur. Study results will 
be in tabular form and aggregate analyses that omit subject identification, therefore informed 
consent, ethics committee or institutional review board (IRB) approval are not required. Any 
publications and reports will not include subject identifiers. 
10.4. Ethical Conduct of the Study
The study will be conducted in accordance with legal and regulatory requirements, as well as
with scientific purpose, value and rigor and follow generally accepted research practices
described in Good Pharmacoepidemiology Practices (GPP) issued by the International
Society for Pharmacoepidemiology (ISPE), the International Society for Pharmacoeconomics
and Outcomes Research (ISPOR) guidance, Pharmaceutical Research and Manufacturers
Association (PhRMA) guidelines and applicable regulatory requirements.
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11. MANAGEMENT AND REPORTING OF ADVERSE EVENTS/ADVERSE 
REACTIONS 
This study involves data that exist as structured data by the time of study start. In these data 
sources, individual patient data are not retrieved or validated, and it is not possible to link (ie, 
identify a potential association between) a particular product and medical event for any 
individual. Thus, the minimum criteria for reporting an adverse event (AE) (ie, identifiable 
patient, identifiable reporter, a suspect product, and event) cannot be met.
12. PLANS FOR DISSEMINATING AND COMMUNICATING STUDY RESULTS
Manuscripts based on specific endpoints of interest may be developed for external 
publication purposes.
In the event of any prohibition or restriction imposed (eg, clinical hold) by an applicable 
competent authority in any area of the world, or if the party responsible for collecting data 
from the participant is aware of any new information which might influence the evaluation of 
the benefits and risks of a Pfizer product, Pfizer will be informed immediately. 
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13. REFERENCES
1.
FDA Approves Xeljanz for Rheumatoid Arthritis. Drugs.com. Accessed February 5, 
2024. https://www.drugs.com/newdrugs/fda-approves-xeljanz-rheumatoid-arthritis-
3558.html.
2.
Ytterberg SR, Bhatt DL, Mikuls TR, et al. Cardiovascular and Cancer Risk with 
Tofacitinib in Rheumatoid Arthritis. N Engl J Med. 2022;386(4):316-326. 
doi:10.1056/NEJMoa2109927.
3.
Wang F, Sun L, Wang S, et al. Efficacy and Safety of Tofacitinib, Baricitinib, and 
Upadacitinib for Rheumatoid Arthritis: A Systematic Review and Meta-Analysis. Mayo 
Clin Proc. 2020;95(7):1404-1419. doi:10.1016/j.mayocp.2020.01.039.
4.
van Vollenhoven RF, Fleischmann R, Cohen S, et al. Tofacitinib or adalimumab versus 
placebo in rheumatoid arthritis. N Engl J Med. 2012;367(6):508-519.
doi:10.1056/NEJMoa1112072.
5.
Fleischmann R, Mysler E, Hall S, et al. Efficacy and safety of tofacitinib monotherapy, 
tofacitinib with methotrexate, and adalimumab with methotrexate in patients with 
rheumatoid arthritis (ORAL Strategy): a phase 3b/4, double-blind, head-to-head, 
randomised controlled trial. Lancet Lond Engl. 2017;390(10093):457-468. 
doi:10.1016/S0140-6736(17)31618-5.
6.
Shouval A, Lidar M, Reitblat T, et al. Real-world effectiveness of tofacitinib in patients 
with rheumatoid arthritis: a prospective observational study. Clin Exp Rheumatol. 
2021;39(6):1378-1384. doi:10.55563/clinexprheumatol/do2uxu.
7.
Harrold LR, Wittstock K, Kelly S, et al. Comparative Effectiveness of Abatacept vs. 
Tofacitinib in Rheumatoid Arthritis Patients who are CCP+. Rheumatol Ther. 
2023;10(3):575-587. doi:10.1007/s40744-022-00523-z.
8.
Pappas DA, St John G, Etzel CJ, et al. Comparative effectiveness of first-line tumour 
necrosis factor inhibitor versus non-tumour necrosis factor inhibitor biologics and 
targeted synthetic agents in patients with rheumatoid arthritis: results from a large US 
registry study. Ann Rheum Dis. 2021;80(1):96-102. doi:10.1136/annrheumdis-2020-
217209.
9.
Mori S, Urata Y, Yoshitama T, Ueki Y. Tofacitinib versus tocilizumab in the treatment 
of biological-naïve or previous biological-failure patients with methotrexate-refractory 
active rheumatoid arthritis. RMD Open. 2021;7(2):e001601. doi:10.1136/rmdopen-
2021-001601.
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10.
Khosrow-Khavar F, Kim SC, Lee H, Lee SB, Desai RJ. Tofacitinib and risk of 
cardiovascular outcomes: results from the Safety of TofAcitinib in Routine care patients 
with Rheumatoid Arthritis (STAR-RA) study. Ann Rheum Dis. 2022;81(6):798-804. 
doi:10.1136/annrheumdis-2021-221915.
11.
Khosrow-Khavar F, Desai RJ, Lee H, Lee SB, Kim SC. Tofacitinib and Risk of 
Malignancy: Results From the Safety of Tofacitinib in Routine Care Patients With 
Rheumatoid Arthritis (STAR-RA) Study. Arthritis Rheumatol Hoboken NJ. 
2022;74(10):1648-1659. doi:10.1002/art.42250.
12.
Xeljanz | U.S. Food and Drug Administration. Accessed March 11, 2024. 
https://www.accessdata.fda.gov/drugsatfda_docs/label/2018/203214s018lbl.pdf.
13.
Xeljanz | European Medicines Agency. Accessed March 11, 2024. 
https://www.ema.europa.eu/en/documents/product-information/xeljanz-epar-product-
information_en.pdf.
14.
Ray WA. Evaluating medication effects outside of clinical trials: new-user designs. Am 
J Epidemiol. 2003;158(9):915-920.
15.
Cole SR, Hernán MA. Constructing inverse probability weights for marginal structural 
models. Am J Epidemiol. 2008;168(6):656-664. doi:10.1093/aje/kwn164.
16.
Desai RJ, Franklin JM. Alternative approaches for confounding adjustment in 
observational studies using weighting based on the propensity score: a primer for 
practitioners. BMJ. 2019;367:l5657. doi:10.1136/bmj.l5657.
17.
Li F, Thomas LE, Li F. Addressing Extreme Propensity Scores via the Overlap 
Weights. Am J Epidemiol. 2019;188(1):250-257. doi:10.1093/aje/kwy201.
18.
Thomas LE, Li F, Pencina MJ. Overlap Weighting: A Propensity Score Method That 
Mimics Attributes of a Randomized Clinical Trial. JAMA. 2020;323(23):2417-2418. 
doi:10.1001/jama.2020.7819.
19.
Austin PC. Balance diagnostics for comparing the distribution of baseline covariates 
between treatment groups in propensity-score matched samples. Stat Med. 
2009;28(25):3083-3107. doi:10.1002/sim.3697.
20.
Wang SV, Jin Y, Fireman B, et al. Relative Performance of Propensity Score Matching 
Strategies for Subgroup Analyses. Am J Epidemiol. 2018;187(8):1799-1807. 
doi:10.1093/aje/kwy049.
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14. LIST OF TABLES 
Table 1.
Cohort Overview
Table 2.
Study Variable Overview
Table 3.
Clinical Outcomes
15. LIST OF FIGURES
NA.
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ANNEX 1. LIST OF STANDALONE DOCUMENTS
Number
Document 
Reference 
Number
Date
Title
1
NA
29 March 2024
A3921445 Pfizer RA Table 
shells_FINAL.xlsx
2
Page 13
29 March 2024
Diagnosis Codes to Define 
RA.xlsx
ANNEX 2. ENCEPP CHECKLIST FOR STUDY PROTOCOLS
Not applicable. 
ANNEX 3. ADDITIONAL INFORMATION
Not applicable.
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Document Approval Record
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