THE EFFECT MODIFICATION PROBLEM- USING STRATIFIED MATCHING ADJUSTED INDIRECT COMPARISON TO EVADE MISLEADING INTERPRETATIONS OF THE TREATMENT EFFECT

Author(s)

Rasouliyan L, Martínez D
RTI Health Solutions, Barcelona, Spain

OBJECTIVES: The objective of this research is to understand the influence of different effect modifier (EM) scenarios on indirect treatment comparison within the setting of anchored matching adjusted indirect comparison (MAIC).

METHODS: Patient-level example datasets for two oncology trials were simulated. Trial 1 (individual patient data-accessible trial) comprised Treatment A and Placebo patients; Trial 2 (aggregate level data-accessible trial) comprised Treatment B and Placebo patients. The primary endpoint was progression-free survival. It was assumed that Trial 2 reported subgroup analyses from which EMs were identifiable. The following dichotomous variables were identified as EMs: genetic marker (EM in Trial 1 but not Trial 2), brain metastases (EM in Trial 2 but not Trial 1), late stage (EM in both trials; same direction), and laboratory test positivity (EM in both trials; opposite directions). Per Decision Support Unit recommendations, MAIC was implemented on overall trial populations adjusting for all EMs simultaneously to obtain an adjusted Treatment A versus Treatment B hazard ratio (HR). Stratified analyses using overall MAIC weights were performed for each category of EM to obtain eight distinct EM-stratified adjusted HR estimates. The naive HR was computed as an unadjusted reference point.

RESULTS: The HRs for Trials 1 and 2 were 0.737 and 0.604, respectively, yielding a naive Treatment A versus Treatment B HR of 1.220 (95% CI: 0.911, 1.634). Overall MAIC yielded an adjusted HR of 0.991 (95% CI: 0.736, 1.333). EM-stratified MAICs yielded the following adjusted HRs (yes category; no category): genetic marker (0.652; 1.307), brain metastases (2.292; 0.881), late stage (0.802; 1.107), laboratory positivity (0.586, 1.893).

CONCLUSIONS: In this simulation, overall and EM-stratified MAIC analyses yielded notably different HR estimates in some scenarios. Consideration of reference population characteristics is imperative in the context of treatment effect interpretation. Our recommendation is to report indirect estimates separately by EM category whenever possible.

Conference/Value in Health Info

2019-11, ISPOR Europe 2019, Copenhagen, Denmark

Code

PCN434

Topic

Clinical Outcomes, Methodological & Statistical Research

Topic Subcategory

Artificial Intelligence, Machine Learning, Predictive Analytics, Comparative Effectiveness or Efficacy, Confounding, Selection Bias Correction, Causal Inference, Modeling and simulation

Disease

Oncology

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