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I have some data which I've analyzed using Kaplan Meier estimation. However, I have a gut feeling that this estimator is biased due to the high censoring rate in my data (nearly 50% censored at later times). What are some ways to address this in an analysis?

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The Kaplan-Meier estimator is not biased when a large proportion of individuals are censored. One of the problems we often observe is that the majority of power for the log-rank test is derived from early failure times which are difficult to observe in KM curves. It does mean that the median survival time is an unreliable point estimate. However, the hazard ratio from a Cox model serves as a good estimate of the relative risk and is unbiased regardless of the amount of censoring that occurs. Both the log rank and the Cox model are adequate tests of survival that are unbiased in interval, right, and left censored data.

The KM curves are biased when there is informative censoring however.

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  • $\begingroup$ Thanks, after looking back in my textbooks, this is consistent with what I found. $\endgroup$ – stats134711 Jul 4 '13 at 19:24
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K-M does not work well for censoring proportions >50%. If you can analyze the distribution of your data, it is better to use a parametric method such as MLE. In alternative, you can also use imputation methods.

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  • $\begingroup$ this is not true! In a study of over 50,000 subjects, if you observe 50 failures in the treatment arm but only 20 in the control arm, and 40,000 are censored, we still have strong evidence indicating the treatment is harmful for a rare disease! $\endgroup$ – AdamO Jul 3 '13 at 23:37

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