This article introduces the R package concrete, which implements a recently developed targeted maximum likelihood estimator (TMLE) for the cause-specific absolute risks of time-to-event outcomes measured in continuous time. Cross-validated Super Learner machine learning ensembles are used to estimate propensity scores and conditional cause-specific hazards, which are then targeted to produce robust and efficient plug-in estimates of the effects of static or dynamic interventions on a binary treatment given at baseline quantified as risk differences or risk ratios. Influence curve-based asymptotic inference is provided for TMLE estimates and simultaneous confidence bands can be computed for target estimands spanning multiple multiple times or events. In this paper we review the one-step continuous-time TMLE methodology as it is situated in an overarching causal inference workflow, describe its implementation, and demonstrate the use of the package on the PBC dataset.

翻译：本文介绍了R包concrete，该包实现了近期开发的针对连续时间测量的事件时间结果的原因特异性绝对风险的定向最大似然估计量。通过交叉验证的超级学习器机器学习集成模型来估计倾向得分和条件原因特异性风险率，随后对其进行定向处理，以产生静态或动态干预效果的稳健且高效的插件估计量，这些干预作用于基线给定的二元治疗，并以风险差或风险比进行量化。为定向最大似然估计提供了基于影响曲线的渐近推断，并可针对跨越多个时间点或事件的目标估计量计算同步置信带。本文回顾了单步连续时间定向最大似然估计方法在总括性因果推断工作流程中的定位，描述了其实现过程，并在原发性胆汁性肝硬化数据集上展示了该包的使用方法。