A Causal Framework for Quantile Residual Lifetime

Estimating prognosis conditional on surviving an initial high-risk period is crucial in clinical research. Yet, standard metrics such as hazard ratios are often difficult to interpret, while mean-based summaries are sensitive to outliers and censoring. We propose a formal causal framework for estimating quantiles of residual lifetime among individuals surviving to a landmark time $t_0$ . Our primary estimand, the "Observed Survivor Quantile Contrast" (OSQC), targets pragmatic prognostic differences within the observed survivor population. To estimate the OSQC, we develop a doubly robust estimator that combines propensity scores, outcome regression, and inverse probability of censoring weights, ensuring consistency under confounding and informative censoring provided that the censoring model is correctly specified and at least one additional nuisance model is correctly specified. Recognizing that the OSQC conflates causal efficacy and compositional selection, we also introduce a reweighting-based supplementary estimator for the "Principal Survivor Quantile Contrast" (PSQC) to disentangle these mechanisms under stronger assumptions. Extensive simulations demonstrate the robustness of the proposed estimators and clarify the role of post-treatment selection. We illustrate the framework using data from the SUPPORT study to assess the impact of right heart catheterization on residual lifetime among intensive care unit survivors, and from the NSABP B-14 trial to examine post-surgical prognosis under adjuvant tamoxifen therapy across multiple landmark times.Estimating prognosis conditional on surviving an initial high-risk period is crucial in clinical research. Yet, standard metrics such as hazard ratios are often difficult to interpret, while mean-based summaries are sensitive to outliers and censoring. We propose a formal causal framework for estimating quantiles of residual lifetime among individuals surviving to a landmark time $t_0$. Our primary estimand, the "Observed Survivor Quantile Contrast" (OSQC), targets pragmatic prognostic differences within the observed survivor population. To estimate the OSQC, we develop a doubly robust estimator that combines propensity scores, outcome regression, and inverse probability of censoring weights, ensuring consistency under confounding and informative censoring provided that the censoring model is correctly specified and at least one additional nuisance model is correctly specified. Recognizing that the OSQC conflates causal efficacy and compositional selection, we also introduce a reweighting-based supplementary estimator for the "Principal Survivor Quantile Contrast" (PSQC) to disentangle these mechanisms under stronger assumptions. Extensive simulations demonstrate the robustness of the proposed estimators and clarify the role of post-treatment selection. We illustrate the framework using data from the SUPPORT study to assess the impact of right heart catheterization on residual lifetime among intensive care unit survivors, and from the NSABP B-14 trial to examine post-surgical prognosis under adjuvant tamoxifen therapy across multiple landmark times.

摘要来源：arXiv

笔记与分组

分类依据与纠错

A Causal Framework for Quantile Residual Lifetime

作者的期刊发表依据

代码、数据与补充材料

剩余寿命分位数的因果分析框架

原文摘要

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