Wolfe R A, Strawderman R L
Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, USA.
Am J Kidney Dis. 1996 Jan;27(1):124-9. doi: 10.1016/s0272-6386(96)90039-6.
Time-dependent covariates are an essential data analysis tool for modeling the effect of a study factor whose value changes during follow-up. However, survival analysis models can yield conclusions that are contrary to the truth if such time-dependent factors are not defined and used carefully. We outline some of the biases that can occur when time-dependent covariates are used improperly in a Cox regression model. For example, we discuss why one should almost never use a covariate that has been averaged over a patient's entire follow-up time as a baseline covariate. Instead, the baseline value should be used as a covariate, or the cumulative average up to each point in time should be used as a time-dependent covariate. We also document why one should use time-dependent covariates with great caution in analyses when the evaluation of a baseline factor is the primary objective. Several simulated examples are given to illustrate the direction and magnitude of the biases that can result from not adhering to some basic assumptions that underlie all survival analysis methodologies.
随时间变化的协变量是一种重要的数据分析工具，用于对研究因素在随访期间其值发生变化时的效应进行建模。然而，如果此类随时间变化的因素未得到仔细定义和使用，生存分析模型可能会得出与事实相反的结论。我们概述了在Cox回归模型中不当使用随时间变化的协变量时可能出现的一些偏差。例如，我们讨论了为什么几乎永远不应将在患者整个随访期间进行平均的协变量用作基线协变量。相反，应将基线值用作协变量，或者将截至每个时间点的累积平均值用作随时间变化的协变量。我们还记录了为什么在以评估基线因素为主要目标的分析中应极其谨慎地使用随时间变化的协变量。给出了几个模拟示例来说明不遵循所有生存分析方法所基于的一些基本假设可能导致的偏差方向和大小。
