CHAPTER 21 Summarizing and Graphing Survival Data 315

survival curve in Figure 21-7b has smaller steps than the life-table survival curve

in Figure 21-5b, so it’s more fine-grained. This is because the step curve now

decreases at every time point at which a participant died. You can tell from the

figures where participant #1 died at 0.74 years, #9 died at 2.27 years, #4 died at

2.34 years, and so on.

While the K-M survival curve tends to be smoother than the life table survival

curve, just the opposite is true for the hazard curve. In Figure 21-7a, each partic-

ipant has their own very thin bar, and the resulting chart isn’t easy to interpret.

Heeding a Few Guidelines for Life-Tables

and the Kaplan-Meier Method

Most of the larger statistical packages (see Chapter 4) can perform life-table and

Kaplan-Meier calculations for you and directly generate survival curves. You have

to identify two variables for the software: one with the survival time for each par-

ticipant, and a binary variable coded 1 if the survival time represents time to death

or the event, and 0 if it represents censored time. It sounds simple, but it’s sur-

prisingly easy to mess up. Here are some pointers for setting up your data and

interpreting the results properly.

Recording survival times correctly

It is important to draw a distinction between data collection and data analysis.

When recording the raw data, it’s best to collect all the relevant dates for the

study. Before the study starts, the dates of interest for data collection should be

specified, which could include date of diagnosis, start of therapy, end of therapy,

FIGURE 21-7:

Kaplan-Meier

estimates of the

hazard (a) and

survival (b)

functions.

© John Wiley & Sons, Inc.