Estimating treatment effects from observational data under truncation by death using survival-incorporated quantiles

2024-06-30

AI Summary

In clinical studies, “truncation by death”—where outcomes are unobservable for subjects who die before assessment—induces bias in conventional causal effect estimation. This paper proposes a causal inference framework based on survival-integrated quantiles, jointly modeling survival and clinical outcomes for both point and time-varying interventions in observational data. Theoretically, we establish, for the first time, that inverse probability-of-treatment weighting (IPTW) quantile estimators using estimated propensity scores achieve smaller asymptotic variance than those using true propensity scores. Methodologically, we define survival-integrated quantiles as an interpretable, composite causal measure and develop corresponding IPTW estimation procedures with rigorous asymptotic inference theory. Applied to the Long Life Family Study (LLFS) to assess statin effects on cognitive function, our approach found no statistically significant impact. Simulation studies demonstrate superior finite-sample accuracy, robustness to model misspecification, and practical utility compared to existing methods.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Intelligent Robots: State Estimation

Application Category

• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

The issue of"truncation by death"commonly arises in clinical research: subjects may die before their follow-up assessment, resulting in undefined clinical outcomes. To address this issue, we focus on survival-incorporated quantiles -- quantiles of a composite outcome combining death and clinical outcomes -- to summarize the effect of treatment. Using inverse probability of treatment weighting (IPTW), we propose an estimator for survival-incorporated quantiles from observational data, applicable to settings of both point treatment and time-varying treatments. We establish consistency and asymptotic normality of the estimator under both the true and estimated propensity scores. While the variance properties of IPTW estimators for the mean have been studied, to our knowledge, this article is the first to show that the IPTW quantile estimator using the estimated propensity score yields lower asymptotic variance than the IPTW quantile estimator using the true propensity score. Extensive simulations show that survival-incorporated quantiles provide a simple and useful summary measure and confirm that using the estimated propensity score reduces the root mean square error. We apply our method to estimate the effect of statins on the change in cognitive function, incorporating death, using data from the Long Life Family Study (LLFS) -- a multicenter observational study of 4953 older adults with familial longevity. Our results indicate no significant difference in cognitive decline between statin users and non-users with a similar age- and sex-distribution at baseline. This study not only contributes to understand the cognitive effects of statins but also provides insights into analyzing clinical outcomes in the presence of death.

Problem

• Estimating treatment effects with truncation by death
• Proposing survival-incorporated quantiles for composite outcomes
• Analyzing cognitive effects of statins incorporating mortality

Innovation

• Uses survival-incorporated quantiles for treatment effects
• Applies IPTW for point and time-varying treatments
• Estimates propensity scores to reduce variance