McVittie James H, Best Ana F, Wolfson David B, Stephens David A, Wolfson Julian, Buckeridge David L, Gadalla Shahinaz M
Department of Mathematics and Statistics, McGill University.
Biostatistics Branch, Biometrics Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health.
Int Stat Rev. 2023 Apr;91(1):72-87. doi: 10.1111/insr.12510. Epub 2022 Jun 16.
Non-parametric estimation of the survival function using observed failure time data depends on the underlying data generating mechanism, including the ways in which the data may be censored and/or truncated. For data arising from a single source or collected from a single cohort, a wide range of estimators have been proposed and compared in the literature. Often, however, it may be possible, and indeed advantageous, to combine and then analyze survival data that have been collected under different study designs. We review non-parametric survival analysis for data obtained by combining the most common types of cohort. We have two main goals: (i) To clarify the differences in the model assumptions, and (ii) to provide a single lens through which some of the proposed estimators may be viewed. Our discussion is relevant to the meta analysis of survival data obtained from different types of study, and to the modern era of electronic health records.
使用观察到的失效时间数据对生存函数进行非参数估计取决于潜在的数据生成机制,包括数据可能被删失和/或截断的方式。对于来自单一来源或从单个队列收集的数据,文献中已经提出并比较了多种估计方法。然而,通常有可能而且实际上也有优势的是,将在不同研究设计下收集的生存数据进行合并,然后进行分析。我们回顾了通过合并最常见类型的队列所获得数据的非参数生存分析。我们有两个主要目标:(i)阐明模型假设中的差异,以及(ii)提供一个统一的视角,通过它可以审视一些已提出的估计方法。我们的讨论与从不同类型研究中获得的生存数据的荟萃分析以及电子健康记录的现代时代相关。