# What exactly is meant by bias in this context?

I'm working through an example of survival-time analysis with censored and un-censored data. We're given the survival times of 94 patients. Some of these survival times are censored i.e.in this context a censored survival time represents the "last time point when the patient was known to be alive." We don't know whether and if so when a patient actually died. (could be that patient dropped out of the study, death by other causes etc.)

The textbook then goes on to say the following: "we now now consider the $$n=47$$ non-censored survival times and assume they are exponentially distributed. We emphasize that this approach is not generally acceptable as ignoring the censored observations will introduce bias if the distributions of censored and uncensored events differ."

My questions are:

1. What exactly do we mean by bias in this context? (do we simply mean that we might be missing out on getting a better idea of what the true distribution of the survival times might be?)
• I agree with your idea in the parenthesis. The survival time you're analyzing may have different distributions between two events (censored, uncensored), which can lead to bias in any further estimation (e.g. coefficients of covariates, etc). Feb 12, 2020 at 0:57