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The Kaplan-Meier estimator is a common non-parametric method for survival analysis and for plotting survival graphs. The survival function $S(t)$ calculates the probability of survival past time $t$. It is most useful in comparing the survival of different groups while properly handling censored data.

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Is it possible and how to predict individual survival curve after Cox regression?

Taking the veteran dataset of a two-treatment, randomized trial for lung cancer in the R package survival as an example, where time is the survival time in days status is the censoring status (0 for …
Matthew Hui's user avatar