Hidden imputations and the kaplan-meier estimator

Stephen R. Cole, Jessie K. Edwards, Ashley I. Naimi, Alvaro Muñoz

Research output: Contribution to journalArticlepeer-review

Abstract

The Kaplan-Meier (KM) estimator of the survival function imputes event times for right-censored and left-truncated observations, but these imputations are hidden and therefore sometimes unrecognized by applied health scientists. Using a simple example data set and the redistribution algorithm, we illustrate how imputations are made by the KM estimator. We also discuss the assumptions necessary for valid analyses of survival data. Illustrating imputations hidden by the KM estimator helps to clarify these assumptions and therefore may reduce inappropriate inferences.

Original languageEnglish (US)
Pages (from-to)1408-1411
Number of pages4
JournalAmerican journal of epidemiology
Volume189
Issue number11
DOIs
StatePublished - Nov 1 2020

Keywords

  • Censoring
  • Imputation
  • Loss to follow-up
  • Survival
  • Truncation

ASJC Scopus subject areas

  • Epidemiology

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