Sporting careers observed over a preset time interval can be partitioned into two distinct subsamples. These samples consist of individuals whose careers had already commenced at the start of the time interval (prevalent subsample) and individuals whose careers began during the time interval (incident subsample) as well the respective individual-level covariate data such as salary, height, weight, performance statistics, draft position, etc. Under the assumption of a proportional hazards model, we propose a partial likelihood estimator to model the effect of covariates on survival via an adjusted risk set sampling procedure for when the incident cohort data is used in conjunction with the prevalent cohort data. We use simulated failure time data to validate the combined cohort proportional hazards methodology and illustrate our model using an NBA data set for career durations measured between 1990 and 2008.

在预设时间间隔内观察到的体育生涯可以分为两个不同的子样本。这些样本包括在时间间隔开始时已经开始职业生涯的个人（普遍子样本）和在时间间隔内开始职业生涯的个人（事件子样本）以及各自的个人水平协变量数据，例如薪水、身高、体重，性能统计，草案位置等。在比例风险模型的假设下，我们提出了一个部分似然估计，通过调整风险集抽样程序来模拟协变量对生存的影响，当事件队列数据与流行的队列数据。