On the Use of Cause-Specific Failure and Conditional Failure Probabilities: Examples from Clinical Oncology Data

作者：Jeffrey J. Gaynor, Eric J. Feuer, Claire C. Tan, Danny H.Y. Wu, Claudia R. Little, David J. Straus, Bayard D. Clarkson, Murray F. Brennan · 发表于：Journal of the American Statistical Association · 年份：1993 · DOI：10.1080/01621459.1993.10476289 · 被引用次数：479 · 研究领域：Statistical Methods in Clinical Trials、Lymphoma Diagnosis and Treatment、Genetic factors in colorectal cancer

Nonparametric maximum likelihood estimation of the probability of failing from a particular cause by time t in the presence of other acting causes (i.e., the cause-specific failure probability) is discussed. A commonly used incorrect approach is to take 1 minus the Kaplan-Meier (KM) estimator (1 – KM), whereby patients who fail of extraneous causes are treated as censored observations. Examples showing the extent of bias in using the 1-KM approach are presented using clinical oncology data. This bias can be quite large if the data are uncensored or if a large percentage of patients fail from extraneous causes prior to the occurrence of failures from the cause of interest. Each cause-specific failure probability is mathematically defined as a function of all of the cause-specific hazards. Therefore, nonparametric estimates of the cause-specific failure probabilities may not be able to identify categorized covariate effects on the cause-specific hazards. These effects would be correctly identified by cause-specific cumulative hazard or KM plots in which the extraneous causes of failure are treated as censored observations. Examples are provided. Finally, nonparametric graphical representation of the two distinct cause-specific failure components of the mixture model (i.e., the probability of ever failing from a particular cause and the time-to-failure distribution given that a patient will fail of that cause) are presented. The difficulty in extrapolating the nonparametric esti...