A SAS macro for estimation of direct adjusted survival curves based on a stratified Cox regression model

被引:240
|
作者
Zhang, Xu
Loberiza, Fausto R.
Klein, John P.
Zhang, Mei-Jie [1 ]
机构
[1] Georgia State Univ, Dept Math & Stat, Atlanta, GA 30303 USA
[2] Univ Nebraska, Dept Internal Med, Omaha, NE 68198 USA
[3] Med Coll Wisconsin, Dept Populat Hlth, Div Biostat, Milwaukee, WI 53226 USA
关键词
cox regression; adjusted survival probability; treatment comparison;
D O I
10.1016/j.cmpb.2007.07.010
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Often in biomedical research the aim of a study is to compare the outcomes of several treatment arms while adjusting for multiple clinical prognostic factors. in this paper we focus on computation of the direct adjusted survival curves for different treatment groups based on an unstratified or a stratified Cox model. The estimators are constructed by taking the average of the individual predicted survival curves. The method of direct adjustment controls for possible confounders due to an imbalance of patient characteristics between treatment groups. This adjustment is especially useful for non-randomized studies. We have written a SAS macro to estimate and compare the direct adjusted survival curves. We illustrate the SAS macro through the examples analyzing stem cell transplant data and Ewing's sarcoma data. (c) 2007 Elsevier Ireland Ltd. All rights reserved.
引用
收藏
页码:95 / 101
页数:7
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