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.

翻译：在临床研究中，估计个体在度过初始高风险期后的预后情况至关重要。然而，传统的风险比等指标往往难以解释，而基于均值的统计量则对异常值和删失数据敏感。本文提出一个正式的因果框架，用于估计存活至标志性时间 $t_0$ 的个体的残存寿命分位数。我们的主要估计目标——“观察幸存者分位数对比”（OSQC）——旨在量化观察到的幸存者群体内部的实际预后差异。为估计 OSQC，我们开发了一种双重稳健估计器，该估计器结合了倾向得分、结局回归和逆概率删失加权方法，确保在存在混杂因素和信息性删失的情况下，只要删失模型正确设定且至少一个额外的干扰模型正确设定，估计结果具有一致性。认识到 OSQC 混淆了因果效应与构成性选择，我们在更强假设下进一步提出一种基于再加权的辅助估计器——“主要幸存者分位数对比”（PSQC），以区分这两种机制。大量模拟实验证明了所提估计器的稳健性，并阐明了治疗后选择的作用。我们通过 SUPPORT 研究数据评估右心导管插入术对重症监护病房幸存者残存寿命的影响，并利用 NSABP B-14 试验数据考察辅助他莫昔芬治疗在不同标志性时间点下的术后预后情况，以此展示该框架的实际应用。