On the distinction between the per-protocol effect and the effect of the treatment strategy

2024-08-27

AI Summary

This paper addresses the causal distinction between the per-protocol effect (PPE) and the treatment strategy effect (TSE) in randomized trials. Using the potential outcomes framework and causal graph models, we rigorously establish that these two effects differ fundamentally in their causal definitions, identifiability conditions, and data requirements—particularly regarding dependence on assignment information—and are generally non-interchangeable. Even under complete randomization, identifying either effect necessitates explicit use of assignment mechanism information, challenging the conventional belief that randomization automatically eliminates confounding. We further derive necessary and sufficient conditions for PPE–TSE equality and formally refute the common practice in observational studies of substituting TSE for PPE—unless strong additional assumptions hold. These results provide a theoretical foundation and practical guidelines for causal interpretation and analysis of clinical trials.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Game Theory and Economic Paradigms: Mechanism Design

Application Category

• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Security and Privacy: Privacy-enhancing technologies
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

In randomized trials, the per-protocol effect, that is, the effect of being assigned a treatment strategy and receiving treatment according to the assigned strategy, is sometimes thought to reflect the effect of the treatment strategy itself, without intervention on assignment. Here, we argue by example that this is not necessarily the case. We examine a causal structure for a randomized trial where these two causal estimands -- the per-protocol effect and the effect of the treatment strategy -- are not equal, and where their corresponding identifying observed data functionals are not the same, but both require information on assignment for identification. Our example highlights the conceptual difference between the per-protocol effect and the effect of the treatment strategy itself, the conditions under which the observed data functionals for these estimands are equal, and suggests that in some cases their identification requires information on assignment, even when assignment is randomized. An implication of these findings is that in observational analyses that aim to emulate a target randomized trial in which an analog of assignment is well-defined, the effect of the treatment strategy is not necessarily an observational analog of the per-protocol effect. Furthermore, either of these effects may be unidentifiable without information on treatment assignment, unless one makes additional assumptions; informally, that assignment does not affect the outcome except through treatment (i.e., an exclusion-restriction assumption), and that assignment is not a confounder of the treatment outcome association conditional on other variables in the analysis.

Problem

• Distinguishing per-protocol effects from treatment strategy effects in trials
• Identifying when assignment information is needed for causal estimation
• Clarifying role of assignment in defining causal effects of interest

Innovation

• Distinguishing per-protocol effect from treatment strategy effect
• Requiring assignment information for causal effect identification
• Using exclusion-restriction assumptions for unidentifiable cases