Inferring Comprehensive Cohort Causal Effects in the Presence of Unmeasured Confounding and Missing Outcomes

2026-06-27

AI Summary

This study addresses the challenges of unmeasured confounding and arbitrarily missing outcomes in estimating the composite cohort causal effect (CCCE) within hybrid clinical trial designs that integrate randomized controlled trial (RCT) and observational study (OBS) data. The authors propose a semiparametric sensitivity analysis framework that incorporates sensitivity parameters to characterize unmeasured confounding and, for the first time, derives the efficient influence function for CCCE. Building on this, they construct a √n-consistent one-step bias-corrected estimator. The method is successfully applied to the TOIB study to evaluate the efficacy and safety of oral versus topical ibuprofen for chronic knee pain in older adults, and extensive simulations demonstrate its robustness and superior performance under complex real-world conditions.

Abstract

This paper presents a methodological framework for estimating the comprehensive cohort causal effect (CCCE) in mixed-design clinical studies that combine randomized controlled trials (RCTs) and parallel observational study (OBS). Our approach is designed to evaluate robustness against unmeasured confounding in the OBS arm and to handle outcomes that are missing at random in either the RCT or OBS arm. By employing a semiparametric theory-based sensitivity analysis framework, we derive the efficient influence function for the CCCE, parameterized by sensitivity parameters. We propose a one-step bias-corrected estimator that allows for flexible modeling and establish conditions under which our CCCE estimator is $\sqrt{n}$-consistent. To illustrate our methods, we apply them to the TOIB study, which evaluates the efficacy and safety of oral versus topical ibuprofen in managing chronic knee pain among older adults. We also evaluate the performance of the proposed methodology in a realistic simulation study.

Problem

• unmeasured confounding
• missing outcomes
• causal inference
• mixed-design studies
• comprehensive cohort causal effect

Innovation

• comprehensive cohort causal effect
• unmeasured confounding
• missing outcomes
• semiparametric sensitivity analysis
• one-step bias-corrected estimator