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The International Journal of Biostatistics

Ed. by Chambaz, Antoine / Hubbard, Alan E. / van der Laan, Mark J.

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Online
ISSN
1557-4679
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Survival Curve Estimation with Dependent Left Truncated Data Using Cox's Model

Todd Mackenzie
  • Dartmouth College
Published Online: 2012-10-19 | DOI: https://doi.org/10.1515/1557-4679.1312

Abstract

The Kaplan-Meier and closely related Lynden-Bell estimators are used to provide nonparametric estimation of the distribution of a left-truncated random variable. These estimators assume that the left-truncation variable is independent of the time-to-event. This paper proposes a semiparametric method for estimating the marginal distribution of the time-to-event that does not require independence. It models the conditional distribution of the time-to-event given the truncation variable using Cox's model for left truncated data, and uses inverse probability weighting. We report the results of simulations and illustrate the method using a survival study.

Keywords: delayed entry; inverse probability weighting

About the article

Published Online: 2012-10-19


Citation Information: The International Journal of Biostatistics, ISSN (Online) 1557-4679, DOI: https://doi.org/10.1515/1557-4679.1312.

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©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston. Copyright Clearance Center

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