Proportional Hazards Regression
is one of a series of predictive modeling processes provided by JMP Clinical and JMP Genomics to help you make the best predictions for your system based on the data that you have collected and analyzed.
This process applies a
Cox proportional hazards model
on survival data with time-to-event
variable
and optional censor indicator to estimate survival functions and the corresponding
median
survival time for each row in the input data set. A variety of
model
selection methods are available, including forward, backward, and stepwise.
Please refer to the
SAS/STAT PROC PHREG documentation
for details.
Caution
: This process can be computationally intensive for large data sets.
Note
: This process was called
Survival Predictive Modeling
in prior releases of JMP Life Sciences software.
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 results generated by the
Proportional Hazards Regression
process include two data sets and associated plots. The first data set (identified by a
_spmv
suffix) lists the best
predictors
of survival, as selected by the
predictive model
. The second data set (identified by the
_spmr
suffix) lists the survival functions calculated, based on the predictors listed in the
adsl_dii_spmv.sas7bdat
data set, for each of the patients in the input data set. Patients are listed in columns and survival times are listed in rows. These are accessed from a tabbed
Results
report.
The
Survival Curves
(shown
below
) plot the survival functions listed in the
SurFunc_train.sas7bdat
output data set for each patient. Each curve represents one patient and is colored according to range.
