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センサリングの問題は生存分析を用いることの最も重要な理由ではない

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 Mario Cleves et al. (2010) An Introduction to Survival Analysis Using Stata, Third Editionのp.2より。

   Perhaps, if you were already familiar with survival analysis, when asked, "why not linear regression?" you offered the excuse of right-censoring—that in real data we often do not observe subjects long enough for all of them to fail. In our data there was no censoring, but in reality, censoring is just a nuisance. We can fix linear regression easily enough to deal with right-censoring. It goes under the name censored-normal regression, (中略)The real problem with linear regression in survival applications is with the assumed normality.

   Being unfamiliar with survival analysis, you might be temped to use linear regression in the face of nonnormality. Linear regression is known, after all, to be remarkably robust to deviations from normality, so why not just use it anyway? The problem is that the distributions for time to an event might be dissimilar from the normal—they are almost certainly nonsymmertric, they might be bimmodal, and linear regression is not robust to these violations.