title: A SAS macro for estimation of direct adjusted survival curves based on a stratified Cox regression model.
abstract: 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.
authors.affiliation: Department of Mathematics and Statistics, Georgia State University, Atlanta, GA 30303, USA. matxxz@langate.gsu.edu
authors.forename: Xu
authors.initials: X
authors.lastname: Zhang
authors.forename: Fausto R
authors.initials: FR
authors.lastname: Loberiza
authors.forename: John P
authors.initials: JP
authors.lastname: Klein
authors.forename: Mei-Jie
authors.initials: MJ
authors.lastname: Zhang
mesh_terms: D035843:Biomedical Research / Q000706:statistics & numerical data*; D015986:Confounding Factors, Epidemiologic; D006801:Humans; D016016:Proportional Hazards Models; D012512:Sarcoma, Ewing; D016019:Survival Analysis; D014481:United States