User Guide

87
Chapter
9
Life Tables
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 cases for
which the second event isn’t recorded (for example, people still working for the
company at the end of the study). This can happen for several reasons: for some cases,
the event simply doesn’t occur before the end of the study; for other cases, we lose
track of their status sometime before the end of the study; still other cases may be
unable to continue for reasons unrelated to the study (such as an employee becoming
ill and taking a leave of absence). Collectively, such cases are known as censored
cases, and they make this kind of study inappropriate for traditional techniques such
as t tests or linear regression.
A statistical technique useful for this type of data is called a follow-up life table.
The basic idea of the life table is to subdivide the period of observation into smaller
time intervals. For each interval, all people who have been observed at least that long
are used to calculate the probability of a terminal event occurring in that interval. The
probabilities estimated from each of the intervals are then used to estimate the overall
probability of the event occurring at different time points.
Example. Is a new nicotine patch therapy better than traditional patch therapy in
helping people to quit smoking? You could conduct a study using two groups of
smokers, one of which received the traditional therapy and the other of which
received the experimental therapy. Constructing life tables from the data would
allow you to compare overall abstinence rates between the two groups to determine
if 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.