© John Wiley & Sons, Inc.

FIGURE 21-6: Kaplan-Meier calculations.

Figure 21-7 shows graphs of the K-M hazard and survival estimates from Figure 21-6. These charts

were created using the R statistical software. Most software that performs survival analysis can create

graphs similar to this. The K-M 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.

© John Wiley & Sons, Inc.

FIGURE 21-7: Kaplan-Meier estimates of the hazard (a) and survival (b) functions.

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 participant 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 participant, 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 surprisingly 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