AbstractWe present a new SAS macro %pshreg that can be used to fit a proportional subdistribution hazards model for survival data subject to competing risks. Our macro first modifies the input data set appropriately and then applies SAS's standard Cox regression procedure, PROC PHREG, using weights and counting-process style of specifying survival times to the modified data set. The modified data set can also be used to estimate cumulative incidence curves for the event of interest. The application of PROC PHREG has several advantages, e.g., it directly enables the user to apply the Firth correction, which has been proposed as a solution to the problem of undefined (infinite) maximum likelihood estimates in Cox regression, frequently encoun...
Survival estimates are an essential compliment to multivariable regression models for time-to-event ...
When conducting time-to-event analyses for prospective clinical studies (where data is collected at ...
Survival estimates are an essential compliment to multivariable regression models for time-to-event ...
AbstractWe present a new SAS macro %pshreg that can be used to fit a proportional subdistribution ha...
Cox proportional hazards model is a commonly used model in providing hazard ratio to compare surviva...
Aims and objectives  For prediction of risk of cardiovascular end points using survival models ...
Aims and objectives: For prediction of risk of cardiovascular end points using survival models the p...
Aims and objectives: For prediction of risk of cardiovascular end points using survival models the p...
Clinical research usually involves time-to-event survival analysis, in which the presence of a compe...
Aims and objectives: For prediction of risk of cardiovascular end points using survival models the p...
Aims and objectives For prediction of risk of cardiovascular end points using survival models the pr...
Competing risks arise in studies in which individuals are subject to a number of potential failure e...
Abstract. Often in biomedical research the aim of a study is to compare the outcomes of several trea...
Clinical research usually involves time-to-event survival analysis, in which the presence of a compe...
Aims and objectives: Computer program for the prediction of survival with respect to time-dependent ...
Survival estimates are an essential compliment to multivariable regression models for time-to-event ...
When conducting time-to-event analyses for prospective clinical studies (where data is collected at ...
Survival estimates are an essential compliment to multivariable regression models for time-to-event ...
AbstractWe present a new SAS macro %pshreg that can be used to fit a proportional subdistribution ha...
Cox proportional hazards model is a commonly used model in providing hazard ratio to compare surviva...
Aims and objectives  For prediction of risk of cardiovascular end points using survival models ...
Aims and objectives: For prediction of risk of cardiovascular end points using survival models the p...
Aims and objectives: For prediction of risk of cardiovascular end points using survival models the p...
Clinical research usually involves time-to-event survival analysis, in which the presence of a compe...
Aims and objectives: For prediction of risk of cardiovascular end points using survival models the p...
Aims and objectives For prediction of risk of cardiovascular end points using survival models the pr...
Competing risks arise in studies in which individuals are subject to a number of potential failure e...
Abstract. Often in biomedical research the aim of a study is to compare the outcomes of several trea...
Clinical research usually involves time-to-event survival analysis, in which the presence of a compe...
Aims and objectives: Computer program for the prediction of survival with respect to time-dependent ...
Survival estimates are an essential compliment to multivariable regression models for time-to-event ...
When conducting time-to-event analyses for prospective clinical studies (where data is collected at ...
Survival estimates are an essential compliment to multivariable regression models for time-to-event ...