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.
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 clinical 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
adsl_dii.sas7bdat
data set consists of 902 rows of individuals with 382 columns corresponding to data on these individuals. It was generated from the original nicardipine
ADSL
data set described in
Nicardipine
and is included with JMP Clinical
. The
Mortality Time to Event
process was run on this modified data set to add five columns (the last five columns shown
below
) listing mortality statistics and thus generate the
adsl_dii_dkm.sas7bdat
data set, used in the following example.
For detailed information about the files and data sets used or created by JMP Life Sciences software, see
Files and Data Sets
.
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.