306 PART 6 Analyzing Survival Data

If you exclude all participants who were censored in your analysis, you may be left

with analyzable data on too few participants. In this example, there are only six

uncensored participants, and removing them would weaken the power of the

analysis. Worse, it would also bias the results in subtle and unpredictable ways.

Using the last-seen date in place of the death date for a censored observation may

seem like a legitimate use of LOCF imputation, but because the participant did not

die during the observation period, it is not acceptable. It’s equivalent to assuming

that all censored participants died immediately after the last-contact date. But

this assumption isn’t reasonable, because it would not be unusual for them to live

on many years. This assumption would also bias your results toward artificially

shorter survival times.

In your analytic data set, only include one variable to represent time observed

(such as Time in days, months, or years), and one variable to represent event

status (such as Event or Death), coded as 1 if they are have the event during the

observation period, and 0 if they are censored. Calculate these variables from raw

date variables stored in other parts of the data (such as date of death, date of visit,

and so on).

FIGURE 21-2:

Survival times

from the date of

surgery.

© John Wiley & Sons, Inc.