%0 Journal Article %1 Zhang:2007:Comput-Methods-Programs-Biomed:17850917 %A Zhang, X %A Loberiza, F R %A Klein, J P %A Zhang, M J %D 2007 %J Comput Methods Programs Biomed %K SurvivalAnalysis sas statistics %N 2 %P 95-101 %R 10.1016/j.cmpb.2007.07.010 %T A SAS macro for estimation of direct adjusted survival curves based on a stratified Cox regression model %U https://www.ncbi.nlm.nih.gov/pubmed/17850917 %V 88 %X 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.

@article{Zhang:2007:Comput-Methods-Programs-Biomed:17850917, 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.}, added-at = {2019-11-18T07:48:16.000+0100}, author = {Zhang, X and Loberiza, F R and Klein, J P and Zhang, M J}, biburl = {https://www.bibsonomy.org/bibtex/233545dc8b662179a3343a35b73d44481/jkd}, description = {A SAS macro for estimation of direct adjusted survival curves based on a stratified Cox regression model. - PubMed - NCBI}, doi = {10.1016/j.cmpb.2007.07.010}, interhash = {7e65740fb1fb1ca485b3d34b3a29a5d6}, intrahash = {33545dc8b662179a3343a35b73d44481}, journal = {Comput Methods Programs Biomed}, keywords = {SurvivalAnalysis sas statistics}, month = nov, number = 2, pages = {95-101}, pmid = {17850917}, timestamp = {2019-11-18T07:48:16.000+0100}, title = {A SAS macro for estimation of direct adjusted survival curves based on a stratified Cox regression model}, url = {https://www.ncbi.nlm.nih.gov/pubmed/17850917}, volume = 88, year = 2007 }