McVittie J H, Addona V
Department of Mathematics and Statistics, McGill University, Montreal, Québec, Canada.
Department of Mathematics, Statistics and Computer Science, Macalester College, Saint Paul, MN, USA.
J Appl Stat. 2021 May 12;49(11):2913-2927. doi: 10.1080/02664763.2021.1928015. eCollection 2022.
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职业生涯可分为两个不同的子样本。这些样本包括在时间间隔开始时其职业生涯就已开始的个体（现患子样本）和在时间间隔期间开始其职业生涯的个体（新发病例子样本），以及各自的个体层面协变量数据，如薪资、身高、体重、表现统计数据、选秀顺位等。在比例风险模型的假设下，当将新发病例队列数据与现患队列数据结合使用时，我们提出一种部分似然估计器，通过调整后的风险集抽样程序来模拟协变量对生存的影响。我们使用模拟的失效时间数据来验证组合队列比例风险方法，并使用一个NBA数据集来说明我们的模型，该数据集用于衡量1990年至2008年期间的职业生涯持续时间。
