Rethinking the Win Ratio: A Causal Framework for Hierarchical Outcome Analysis

Understand

Quantifying causal effects in the presence of complex and multivariate outcomes remains a key challenge in treatment evaluation.

• For hierarchical multivariate outcomes, the FDA recommends the Win Ratio and Generalized Pairwise Comparisons approaches \citep{Pocock2011winratio,Buyse2010}.
• However, commonly used estimators can yield treatment recommendations that target a population-level estimand (the probability that a randomly sampled patient under treatment fares better than another randomly sampled patient under control), which can contradict conclusions drawn from an ideal estimand (the probability that an individual would fare better with treatment than without), especially in heterogeneous populations.
• This discrepancy arises from the non-identifiability of the latter estimand and underscores both the influence of the chosen causal measure on the resulting conclusions and the necessity of articulating the underlying causal framework with clarity.