Fleming T R, Lin D Y
Department of Biostatistics, Box 357232, University of Washington, Seattle, Washington 98195, USA.
Biometrics. 2000 Dec;56(4):971-83. doi: 10.1111/j.0006-341x.2000.0971.x.
The field of survival analysis emerged in the 20th century and experienced tremendous growth during the latter half of the century. The developments in this field that have had the most profound impact on clinical trials are the Kaplan-Meier (1958, Journal of the American Statistical Association 53, 457-481) method for estimating the survival function, the log-rank statistic (Mantel, 1966, Cancer Chemotherapy Report 50, 163-170) for comparing two survival distributions, and the Cox (1972, Journal of the Royal Statistical Society, Series B 34, 187-220) proportional hazards model for quantifying the effects of covariates on the survival time. The counting-process martingale theory pioneered by Aalen (1975, Statistical inference for a family of counting processes, Ph.D. dissertation, University of California, Berkeley) provides a unified framework for studying the small- and large-sample properties of survival analysis statistics. Significant progress has been achieved and further developments are expected in many other areas, including the accelerated failure time model, multivariate failure time data, interval-censored data, dependent censoring, dynamic treatment regimes and causal inference, joint modeling of failure time and longitudinal data, and Baysian methods.
生存分析领域兴起于20世纪，并在该世纪后半叶经历了巨大的发展。该领域中对临床试验产生最深远影响的进展包括用于估计生存函数的Kaplan-Meier方法（1958年，《美国统计协会杂志》53卷，457 - 481页）、用于比较两种生存分布的对数秩统计量（Mantel，1966年，《癌症化疗报告》50卷，163 - 170页）以及用于量化协变量对生存时间影响的Cox比例风险模型（1972年，《皇家统计学会杂志》，B辑34卷，187 - 220页）。由Aalen开创的计数过程鞅理论（1975年，关于一类计数过程的统计推断，博士论文，加利福尼亚大学伯克利分校）为研究生存分析统计量的小样本和大样本性质提供了一个统一的框架。在许多其他领域也取得了显著进展并有望进一步发展，包括加速失效时间模型、多变量失效时间数据、区间删失数据、相依删失、动态治疗方案和因果推断、失效时间与纵向数据的联合建模以及贝叶斯方法。
