An overall strategy based on regression models to estimate relative survival and model the effects of prognostic factors in cancer survival studies

被引:115
作者
Remontet, L. [1 ]
Bossard, N.
Belot, A.
Esteve, J.
机构
[1] Ctr Hosp Lyon Sud, Hospices Civils Lyon, Serv Biostat, F-69495 Pierre Benite, France
[2] CNRS, UMR 5558, Lab Biostat Sante, F-69373 Lyon, France
[3] Univ Lyon 1, F-69622 Lyon, France
[4] Fac Med Toulouse, Reseau FRANCIM, F-31062 Toulouse, France
关键词
relative survival; regression splines; non-proportional hazards; Akaike Information Criterion; cancer; registry;
D O I
10.1002/sim.2656
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Relative survival provides a measure of the proportion of patients dying from the disease under study without requiring the knowledge of the cause of death. We propose an overall strategy based on regression models to estimate the relative survival and model the effects of potential prognostic factors. The baseline hazard was modelled until 10 years follow-up using parametric continuous functions. Six models including cubic regression splines were considered and the Akaike Information Criterion was used to select the final model. This approach yielded smooth and reliable estimates of mortality hazard and allowed us to deal with sparse data taking into account all the available information. Splines were also used to model simultaneously non-linear effects of continuous covariates and time-dependent hazard ratios. This led to a graphical representation of the hazard ratio that can be useful for clinical interpretation. Estimates of these models were obtained by likelihood maximization. We showed that these estimates could be also obtained using standard algorithms for Poisson regression. Copyright (c) 2006 John Wiley & Sons, Ltd.
引用
收藏
页码:2214 / 2228
页数:15
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