Lagakos S W, Schoenfeld D A
Biometrics. 1984 Dec;40(4):1037-48.
The effects are investigated of misspecifying a proportional-hazards regression model on the associated partial-likelihood score test for comparing two randomized treatments in the presence of covariates. The asymptotic efficiency of the proportional-hazards score test, relative to the optimal partial-likelihood test, declines slowly as the hazard functions for the two treatments deviate from proportionality; the efficiency can be very low when the hazard functions cross or differ only at large survival times. Misspecification of the functional form of the regression portion of a proportional-hazards model introduces a quantitative treatment-covariate interaction. In the situations that we examine, based on a binary covariate, this misspecification usually results in only a minor drop in efficiency. The omission of a covariate that is balanced across treatments has a negligible effect on the size of the score test, but can substantially reduce power when the covariate effect is strong. The loss of power from mismodeling a balanced covariate is usually small.
研究了在存在协变量的情况下，对比例风险回归模型进行错误设定时，对比较两种随机治疗的相关部分似然比分检验的影响。相对于最优部分似然检验，比例风险比分检验的渐近效率随着两种治疗的风险函数偏离比例性而缓慢下降；当风险函数交叉或仅在较大生存时间处不同时，效率可能非常低。比例风险模型回归部分的函数形式错误设定会引入定量的治疗 - 协变量交互作用。在我们基于二元协变量进行研究的情况下，这种错误设定通常只会导致效率略有下降。遗漏在各治疗组间平衡的协变量对比分检验的规模影响可忽略不计，但当协变量效应较强时，会大幅降低检验效能。对平衡协变量进行模型错误设定导致的效能损失通常较小。
