One-step targeted maximum likelihood estimation for targeting cause-specific absolute risks and survival curves
本文提出一步靶向最大似然估计方法，用于在生存和竞争风险数据中同时推断多个绝对风险和完整生存曲线，适用于基线治疗效应估计。
Summary This paper considers the one-step targeted maximum likelihood estimation methodology for multi-dimensional causal parameters in general survival and competing risk settings where event times take place on the positive real line and are subject to right censoring. We focus on effects of baseline treatment decisions possibly confounded by pretreatment covariates, but remark that our work generalizes to settings with time-varying treatment regimes and time-dependent confounding. We point out two overall contributions of our work. First, our methods can be used to obtain simultaneous inference for treatment effects on multiple absolute risks in competing risk settings. Second, our methods can be used to achieve inference for the full survival curve, or a full absolute risk curve, across time. The one-step targeted maximum likelihood procedure is based on a one-dimensional universal least favourable submodel for each cause-specific hazard that we implement in recursive steps along a corresponding nonuniversal multivariate least favourable submodel. Our empirical study demonstrates the practical use of the methods.