PMU87 MATCHING ADJUSTED INDIRECT COMPARISONS: RECOMMENDATION FOR THE SELECTION OF BASELINE CHARACTERISTICS USED FOR MATCHING

Abstract

Matching-adjusted indirect comparisons (MAICs) offer an alternative in situations where Bucher comparisons are likely to be biased due to differences in trial populations. MAIC applies weights to the individual patient data (IPD) from one trial, matching a set of baseline characteristics to those in the other trial(s). The selection of the baseline characteristics used for adjustment can impact the reliability of an MAIC. We propose a tool to assist in the selection of baseline characteristics used for adjustment in an MAIC. When deciding whether to include a baseline characteristic, the tool trades off its impact on effective sample size and its impact on the bias of treatment effects. The metric provided by the tool can be used to rank potential sets of baseline characteristics used for matching. We apply this tool to an anchored MAIC comparing the active treatments in two randomized controlled trials. Starting from a recommended set of baseline characteristics to be used for adjustment, the tool included any variables that did not reduce the effective sample size substantially, and excluded variables that did not change the weighted outcome substantially. Using these heuristics led to adequate effective sample size and did not introduce unnecessary uncertainty without substantially impacting estimated treatment effects. For example, excluding a disease severity index from the adjustment had less than 4% impact on the effect size but reduced the standard error of the outcome by about 30%. A set of baseline characteristics without this disease severity index was ranked higher in the recommendation than the set including the disease severity index, when other characteristics in the two sets were the same. The analyses show that improvements to standard error may be possible without an increase in bias, and suggest that statistical tools to identify such situations may be valuable.