Process Description

Life Regression

Life Regression fits various survival distributions to time-to-event data and an optional censoring indicator. The input data set is assumed to be in a wide format. You may apply Predictor Reduction for reducing the initial number of candidate predictor variables before modeling. There are no built-in variable selection methods like stepwise so the number of predictors should be kept small relative to the number of rows.

Please refer to the SAS PROC LIFEREG documentation for details.

What do I need?

One wide Input Data Set is needed to run this process. This data set contains all of the numeric and other data, including mortality statistics, to be analyzed. Data must be in the wide format. Genetic marker data is likely in this form already, but any data that are in tall form must be converted to the wide format. The Transpose Rectangular process can be used to convert the tall data set and its accompanying Experimental Design Data Set (EDDS) to wide form.

The wright_wide_2k_10k_dt_sig6_s.sas7bdat data set used in this example, consists of 165 rows of individuals with 667 columns corresponding to data on these individuals. It is included in the Prostate Cancer Biomarkers study saple data that is included with JMP Clinical.

For detailed information about the files and data sets used or created by JMP Genomics software, see Files and Data Sets.

Output/Results

The output generated by this process is summarized in a Tabbed report. Refer to the Life Regression output documentation for detailed descriptions and guides to interpreting your results.