# Kaplan-Meier Survival Analysis

There are many situations in which you would want to examine the distribution of times between two events, such as length of employment (time between being hired and leaving the company). However, this kind of data usually includes some censored cases. Censored cases are cases for which the second event isn't recorded (for example, people still working for the company at the end of the study). The Kaplan-Meier procedure is a method of estimating time-to-event models in the presence of censored cases. The Kaplan-Meier model is based on estimating conditional probabilities at each time point when an event occurs and taking the product limit of those probabilities to estimate the survival rate at each point in time.

**Example.** Does a new treatment for AIDS have any therapeutic
benefit in extending life? You could conduct a study using two groups
of AIDS patients, one receiving traditional therapy and the other
receiving the experimental treatment. Constructing a Kaplan-Meier
model from the data would allow you to compare overall survival rates
between the two groups to determine whether the experimental treatment
is an improvement over the traditional therapy. You can also plot
the survival or hazard functions and compare them visually for more
detailed information.

**Statistics.** Survival table, including time, status, cumulative
survival and standard error, cumulative events, and number remaining;
and mean and median survival time, with standard error and 95% confidence
interval. Plots: survival, hazard, log survival, and one minus survival.

The Kaplan-Meier procedure is available only if you have installed the Advanced Analyze option.

Kaplan-Meier Data Considerations

**Data.** The time variable should be continuous, the status
variable can be categorical or continuous, and the factor and strata
variables should be categorical.

**Assumptions.** Probabilities for the event of interest should
depend only on time after the initial event--they are assumed to be
stable with respect to absolute time. That is, cases that enter the
study at different times (for example, patients who begin treatment
at different times) should behave similarly. There should also be
no systematic differences between censored and uncensored cases. If,
for example, many of the censored cases are patients with more serious
conditions, your results may be biased.

**Related procedures.** The Kaplan-Meier procedure uses a method
of calculating life tables that estimates the survival or hazard function
at the time of each event. The Life Tables procedure uses an actuarial
approach to survival analysis that relies on partitioning the observation
period into smaller time intervals and may be useful for dealing with
large samples. If you have variables that you suspect are related
to survival time or variables that you want to control for (covariates),
use the Cox Regression procedure. If your covariates can have different
values at different points in time for the same case, use Cox Regression
with Time-Dependent Covariates.

Obtaining a Kaplan-Meier Survival Analysis

This feature requires SPSS® Statistics Standard Edition or the Advanced Statistics Option.

- From the menus choose:
- Select a time variable.
- Select a status variable to identify cases for which the terminal event has occurred. This variable can be numeric or short string. Then click Define Event.

Optionally, you can select a factor variable to examine group differences. You can also select a strata variable, which will produce separate analyses for each level (stratum) of the variable.

This procedure pastes KM command syntax.