Examining survival test results to detect assumption violations

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The individual observations can be examined for signs of lack of independence or lack of uniformity in the censoring. When examining survival test results, you should keep these potential problems in mind, along with the possibility of implicit factors not surfaced in the data (unless you used stratification to control for such a factor).

If the results include a Kaplan-Meier plot, you should also consider those results. You can also examine the Kaplan-Meier plot for signs of crossing survival functions. If the results include a life table analysis, you should also examine those results.

Examining results for a survival test:


  • Lack of independence of censoring:
  • You should be alert to the possibility of systematic patterns in the censoring, For example, if there are many values censored earlier in the experiment rather than later, there may have been a change of conditions during the experiment. (For example, one physician may have withdrawn referred patients early on while other doctors did not.) If there were a relatively large number of censored values in a short span of time, then the censorings may be related. (For example, a physician transfers to another hospital, and all referred patients suddenly leave the study.) A common problem with a survival analysis experiment studying medical treatments is that patients who do not do well one or more of the treatments must be withdrawn from the study, so that sicker patients may be more likely to have censored survival times.
  • Many censored values:
  • If there are many censored values, the effective sample size becomes smaller and the survival test results become less reliable. If many subjects are censored at approximately the same time, the possibility of a common cause should be considered. This would violate the assumption of independence of censoring and survival times. If many subjects are left alive at the end of the study, the study may simply not have continued long enough to provide a reliable comparison of survival functions.
  • Small sample sizes:
  • Small sample sizes tend to violate the asymptotic estimation assumptions that the Gehan-Breslow, Mantel-Cox, and Tarone-Ware survival tests rely on. High censoring rates also reduce the effective sample size for subsequent intervals.
  • Patterns in plots of data:
  • If the assumptions for the censoring and survival distributions are correct, then a plot of either the censored or the noncensored values (or both together) against time should show no particular patterns.

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