Use of Propensity Scores to Assess Comparability of Treatment Groups in a Registry Program 1 Kenneth J Rothman On behalf of Menno V Huisman, Gregory Y.H.

Use of Propensity Scores to Assess Comparability of Treatment Groups

in a Registry Program

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Disclosure Information

Kenneth J Rothman is an employee of RTI Health Solutions, an independent non-profit research organization that does work for government agencies and pharmaceutical companies.

GLORIA-AF is sponsored by Boehringer Ingelheim

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GLORIA-AF

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GLORIA-AF is a large, international, observational registry program of patients with newly diagnosed AF at risk of stroke

Study objectives:1. Characterize patients in various regions of

the world newly diagnosed with non-valvular AF at risk for stroke

2. Describe current patterns of antithrombotic treatment

3. Assess effectiveness and safety of antithrombotic treatments

Design of GLORIA-AF

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Patients on dabigatran etexilate

Baseline Visit

Phase ICross-sectional

All patients

Phase IICross-sectional, longitudinal follow-up of dabigatran patients

Phase IIICross-sectional, longitudinal follow-up of all patients

Baseline VisitBaseline Visit

Status:Start after comparability assessment of treatment groups in Phase II

Status:Currently ongoing

Status:Ended Jan 2013

Methods in GLORIA-AF

• In Phase II and III Propensity Score (PS) techniques will be used to assess comparability of the treatment groups (dabigatran compared with VKA). Specifically, we measure the proportion of patients within the range of overlapping PS.

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Figure: Sebastian Schneeweiss, Jerry Avorn A review of uses of health care utilization databases for epidemiologic research on therapeutics Journal of Clinical Epidemiology Volume 58, Issue 4 2005 323 - 337

Methods in GLORIA-AF

• The decision to begin Phase III will be primarily based on the degree of overlap of the PS. If 95% or more of patients are in the region of overlapping PS, we will proceed to Phase III for that region.

• Assessment of comparability is made independently in geographic regions. The first assessment occurred in North America.

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Results

• From November, 2011 through April, 2013, 1672 eligible patients were enrolled in North America.

• DE was initiated in 536 and VKA in 488 patients.

• 15% of patients were prescribed other Novel Oral Anticoagulants (NOACs), and aspirin in 11%.

• 11% of patients did not receive antithrombotic therapy to prevent ischemic stroke.

Results

Missing/unknown data were imputed using Naïve Bayes classifier. DE, dabigatran etexilate; VKA, vitamin K antagonist.

  DE (N = 536) VKA (N = 488)  n (%) n (%)Age class

< 65 years 132 (24.6) 107 (21.9)65 to < 75 years 204 (38.1) 144 (29.5)≥ 75 years 200 (37.3) 237 (48.6)

Gender    Male 316 (59.0) 262 (53.7)Female 220 (41.0) 226 (46.3)

HypertensionYes, uncontrolled 43 (8.0) 48 (9.8)Yes, controlled 381 (71.1) 355 (72.7)No 112 (20.9) 85 (17.4)

Results

Missing/unknown data were imputed using Naïve Bayes classifier. DE, dabigatran etexilate; VKA, vitamin K antagonist.

  DE (N = 536) VKA (N = 488)  n (%) n (%)Diabetes mellitus

No 411 (76.7) 328 (67.2)Yes 125 (23.3) 160 (32.8)

CHADS2 Score

Low (score=0) 39 (7.3) 23 (4.7)Moderate (score=1) 206 (38.4) 133 (27.3)High (score>=2) 291 (54.3) 332 (68.0)

Results – Propensity score distribution by treatment group

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Results

• The proportion of patients in the overlapping region of the PS between the two treatment groups was 99% when the model contained a pre-specified subset of risk factors for stroke and bleeding.

• In a sensitivity analysis that included all collected baseline characteristics, the overlapping region comprised 96%.

• Either result was sufficient to proceed to the comparative Phase III.

Conclusions

• Nearly all patients fell within the overlapping range of propensity scores.

• Mostly overlapping PS ranges does not guarantee an absence of confounding. It does, however, allow for control of confounding in the data analysis for those factors that are included in the PS model.

Back up slides

Variables in main PS model

Age classGenderHypertensionDiabetes mellitusPrevious strokeTIAnon-CNS arterial embolismCongestive heart failureMIPresence of complex aortic plaques

Peripheral artery diseaseAbnormal kidney functionHepatic diseasePrior bleedingAny drug (HASBLED)Alcohol abuseCoronary artery diseaseSmoking statusPsychosocial factors

Variables included in sensitivity analysis

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