%0 Journal Article %1 Akazawa:1991:Comput-Methods-Programs-Biomed:1935013 %A Akazawa, K %A Nakamura, T %A Moriguchi, S %A Shimada, M %A Nose, Y %D 1991 %J Comput Methods Programs Biomed %K SurvivalAnalysis sas simulation statistics %N 3 %P 203-212 %R 10.1016/0169-2607(91)90122-A %T Simulation program for estimating statistical power of Cox's proportional hazards model assuming no specific distribution for the survival time %U https://www.ncbi.nlm.nih.gov/pubmed/1935013 %V 35 %X Small sample properties of the maximum partial likelihood estimates for Cox's proportional hazards model depend on the sample size, the true values of regression coefficients, covariate structure, censoring pattern and possibly baseline hazard functions. Therefore, it would be difficult to construct a formula or table to calculate the exact power of a statistical test for the treatment effect in any specific clinical trial. The simulation program, written in SAS/IML, described in this paper uses Monte-Carlo methods to provide estimates of the exact power for Cox's proportional hazards model. For illustrative purposes, the program was applied to real data obtained from a clinical trial performed in Japan. Since the program does not assume any specific function for the baseline hazard, it is, in principle, applicable to any censored survival data as long as they follow Cox's proportional hazards model.

@article{Akazawa:1991:Comput-Methods-Programs-Biomed:1935013, abstract = {Small sample properties of the maximum partial likelihood estimates for Cox's proportional hazards model depend on the sample size, the true values of regression coefficients, covariate structure, censoring pattern and possibly baseline hazard functions. Therefore, it would be difficult to construct a formula or table to calculate the exact power of a statistical test for the treatment effect in any specific clinical trial. The simulation program, written in SAS/IML, described in this paper uses Monte-Carlo methods to provide estimates of the exact power for Cox's proportional hazards model. For illustrative purposes, the program was applied to real data obtained from a clinical trial performed in Japan. Since the program does not assume any specific function for the baseline hazard, it is, in principle, applicable to any censored survival data as long as they follow Cox's proportional hazards model.}, added-at = {2018-10-03T06:30:41.000+0200}, author = {Akazawa, K and Nakamura, T and Moriguchi, S and Shimada, M and Nose, Y}, biburl = {https://www.bibsonomy.org/bibtex/263670bba70dad959aa6614e2af9263e8/jkd}, description = {Simulation program for estimating statistical power of Cox's proportional hazards model assuming no specific distribution for the survival time. - PubMed - NCBI}, doi = {10.1016/0169-2607(91)90122-A}, interhash = {910652ce051f21928270e2b1a722baa0}, intrahash = {63670bba70dad959aa6614e2af9263e8}, journal = {Comput Methods Programs Biomed}, keywords = {SurvivalAnalysis sas simulation statistics}, month = jul, number = 3, pages = {203-212}, pmid = {1935013}, timestamp = {2018-10-03T06:31:40.000+0200}, title = {Simulation program for estimating statistical power of Cox's proportional hazards model assuming no specific distribution for the survival time}, url = {https://www.ncbi.nlm.nih.gov/pubmed/1935013}, volume = 35, year = 1991 }