Simultaneously assessing intended and unintended treatment effects of multiple treatment options: a pragmatic "matrix design"

Abstract

Purpose: A key aspect of comparative effectiveness research is the assessment of competing treatment options and multiple outcomes rather than a single treatment option and a single benefit or harm. In this commentary, we describe a methodological framework that supports the simultaneous examination of a "matrix" of treatments and outcomes in non-randomized data.

Methods: We outline the methodological challenges to a matrix-type study (matrix design). We consider propensity score matching with multiple treatment groups, statistical analysis, and choice of association measure when evaluating multiple outcomes. We also discuss multiple testing, use of high-dimensional propensity scores for covariate balancing in light of multiple outcomes, and suitability of available software.

Conclusion: The matrix design study methods facilitate examination of the comparative benefits and harms of competing treatment choices, and also provides the input required for calculating the numbers needed to treat and for a broader benefit/harm assessment that weighs endpoints of varying severity.