Survival estimation and testing via multiple imputation
AbstractMultiple imputation is a technique for handling data sets with missing values. The method fills in each missing value several times, creating many augmented data sets. Each augmented data set is analyzed separately and the results combined to give a final result consisting of an estimate and a measure of uncertainty. In this paper we consider nonparametric multiple-imputation methods to handle missing event times for censored observations in the context of nonparametric survival estimation and testing. Two nonparametric imputation schemes are considered. In risk set imputation the censored time is replaced by a random draw of the observed times amongst those at risk after the censoring time. In Kaplan-Meier (KM) imputation the imputed time is a draw from the estimated distribution of event times amongst those at risk after the censoring time. We show that with a large number of imputes the estimates from both methods reproduce the KM estimator. In a simulation study we show that the inclusion of a bootstrap stage in the multiple imputation algorithm gives coverage rates of confidence intervals that are comparable to that from Greenwood's formula. Connections to the redistribute to the right algorithm are discussed.
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Bibliographic InfoArticle provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 58 (2002)
Issue (Month): 3 (July)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Schenker, Nathaniel & Taylor, Jeremy M. G., 1996. "Partially parametric techniques for multiple imputation," Computational Statistics & Data Analysis, Elsevier, vol. 22(4), pages 425-446, August.
- Chiu-Hsieh Hsu & Jeremy Taylor & Susan Murray, 2004. "Survival Analysis USing Auxiliary Variables Via Nonparametric Multiple Imputation," The University of Michigan Department of Biostatistics Working Paper Series 1026, Berkeley Electronic Press.
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