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    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. © The Author(s) 2020. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail:


    Stephen R Cole, Jessie K Edwards, Ashley I Naimi, Alvaro Muñoz. Hidden Imputations and the Kaplan-Meier Estimator. American journal of epidemiology. 2020 Nov 02;189(11):1408-1411

    Expand section icon Mesh Tags

    PMID: 32412079

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