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The International Journal of Biostatistics

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

IMPACT FACTOR 2018: 1.309

CiteScore 2018: 1.11

SCImago Journal Rank (SJR) 2018: 1.325
Source Normalized Impact per Paper (SNIP) 2018: 0.715

Mathematical Citation Quotient (MCQ) 2018: 0.03

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Survival Curve Estimation with Dependent Left Truncated Data Using Cox's Model

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


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, Volume 8, Issue 1, ISSN (Online) 1557-4679, DOI: https://doi.org/10.1515/1557-4679.1312.

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