This paper introduces software implemented in the mets R-package for calculating non-parametric and regression estimates of Restricted Mean Survival Time (RMST) and Restricted Mean Time Lost (RMTL), including RMTL due to specific causes. A unique feature is the ability to compute the non-parametric estimates of RMST and RMTL, as well as their standard errors, for all time horizons simultaneously. Regression modeling in mets is based on Inverse Probability of Censoring Weighting (IPCW) methods. The package implements different versions of IPCW adjusted estimating equations. A critical technical contribution is the provision of influence functions for all models, which enables the computation of standard errors and allows the estimates to be used as building blocks for more complex statistics, such as the while-alive estimate in recurrent events settings. To expand capabilities in causal inference, the mets package also implements methods for standardization estimates (G-computation) and the estimation of Average Treatment Effects (ATE) for both RMST and RMTL in the competing risks setting. Importantly, the computations scale linearly with the number of observations, making the software efficient for use with large datasets.

翻译：本文介绍mets R包中实现的软件工具，用于计算受限平均生存时间（RMST）和受限平均时间损失（RMTL）的非参数估计与回归估计，包括特定原因导致的RMTL。其独特功能在于可同时计算所有时间窗下的RMST与RMTL非参数估计及其标准误差。mets包中的回归建模基于逆删失概率加权（IPCW）方法，实现了不同版本的IPCW调整估计方程。关键性技术贡献在于为所有模型提供影响函数，从而支持标准误差计算，并使估计值可作为更复杂统计量的构建模块（如复发事件场景下的存活估计）。为拓展因果推断能力，mets包还实现了标准化估计（G计算）方法，以及在竞争风险设定下对RMST和RMTL进行平均处理效应（ATE）估计。重要的是，计算复杂度与观测数量呈线性关系，这使得该软件适用于大规模数据集的高效处理。