happen if, for example, death is your outcome and at the end of your study period no individuals

are alive anymore — they all have died in your study. As you may guess, this situation is much

more common in animal studies than human studies. But if you have followed all the individuals

in your data until they all experienced the outcome, and you have two or more groups of numbers

indicating survival times that you want to compare, you can use approaches described in Chapter

11. One option is to use an unpaired Student t test to test whether one group has a statistically

significantly longer mean survival time than the other. If you have three or more groups, you

would use an ANOVA instead. But because survival times are very likely to be non-normally

distributed, you may prefer to use a nonparametric test, such as the Wilcoxon Sum-of-Ranks test

or Mann-Whitney U test, to compare the median survival time between two groups. With more

than two groups, you would use the nonparametric Kruskal-Wallis test.

Suppose that you conduct a toxicity study with laboratory animals of a potential cancer drug. You

obtain 90 experimental mice. The mice are randomly placed in groups such that 60 receive the drug in

their food, and 30 are given control food with no drug. A laboratory worker observes them and

records their vital status every day after the experiment starts, taking note of when each animal dies or

is censored, meaning they are taken out of the study for another reason (such as not eating). You

perform a life-table analysis on each group of mice — the drug compared to control — as described in

Chapter 21, and graph the results. The graph displays the survival curves shown in Figure 22-1. As a

bonus, the two life tables generated to support this display also provide the summary information

needed the log-rank test.

The two survival curves in Figure 22-1 look different. The drug group seems to be showing better

survival than the control group. But is this apparent difference real, or could it be the result of random

fluctuations only? The log-rank test answers this question.

© John Wiley & Sons, Inc.

FIGURE 22-1: Survival curves for two groups of laboratory animals.

Comparing Survival between Two Groups with the