Flexible parametric proportional-hazards and proportional-odds models for censored survival data, with application to prognostic modelling and estimation of treatment effects

被引:1098
作者
Royston, P [1 ]
Parmar, MKB [1 ]
机构
[1] MRC, Clin Trials Unit, Canc Div, London NW1 2DA, England
关键词
survival analysis; proportional hazards; proportional odds; splines; parametric models;
D O I
10.1002/sim.1203
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Modelling of censored survival data is almost always done by Cox proportional-hazards regression. However, use of parametric models for such data may have some advantages. For example, nonproportional hazards, a potential difficulty with Cox models, may sometimes be handled in a simple way, and visualization of the hazard function is much easier. Extensions of the Weibull and log-logistic models are proposed in which natural cubic splines are used to smooth the baseline log cumulative hazard and log cumulative odds of failure functions. Further extensions to allow non-proportional effects of some or all of the covariates are introduced. A hypothesis test of the appropriateness of the scale chosen for covariate effects (such as of treatment) is proposed. The new models are applied to two data sets in cancer. The results throw interesting light on the behaviour of both the hazard function and the hazard ratio over time. The tools described here may be a step towards providing greater insight into the natural history of the disease and into possible underlying causes of clinical events. We illustrate these aspects by using the two examples in cancer. Copyright (C) 2002 John Wiley Sons, Ltd.
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页码:2175 / 2197
页数:23
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