Summary descriptive statistics about the data. These can include number of censored and

uncensored observations, median survival time, and mean and standard deviation for each

predictor variable in the model

One or more measures of goodness-of-fit for the model

Baseline survival function, which outputs as a table of values and a survival curve

Baseline hazard function values, which output as a table and graph

After you specify all the input to the program, execute the code, retrieve the output, and interpret the

results.

Interpreting the Output of a Survival Regression

Suppose that you have conducted a long-term survival study of 200 Stage 4 cancer patients who were

enrolled from four clinical centers (A, B, C, and D) and were randomized to receive either

chemotherapy or radiation therapy. Participants were followed for up to ten years, after which the

survival data were summarized by treatment (see Figures 23-3a and 23-3b for the two treatments) and

by clinical center.

It would appear, from Figure 23-3, that radiation (compared to chemotherapy) and clinical centers A

and B (compared to C and D) are associated with better survival. But are these apparent effects

statistically significant? PH regression can answer these and other questions.

© John Wiley & Sons, Inc.

FIGURE 23-3: Kaplan-Meier survival curves by treatment and clinical center.

To run a PH regression on the data from this example, you must indicate the following to the software

in your code:

The time-to-event variable. We named this variable Time, and it was coded in years. For

participants who died during the observation period, it was coded as the number of years from

observation beginning until death. For participants who did not die during the observation period,

it contains number of years they were observed.

The event status variable. We named this variable Status, and coded it as 1 if the participant was