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

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  • Mackenzie Todd

    (Dartmouth College)

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.

Suggested Citation

  • Mackenzie Todd, 2012. "Survival Curve Estimation with Dependent Left Truncated Data Using Cox's Model," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-20, October.
    • Handle: RePEc:bpj:ijbist:v:8:y:2012:i:1:n:29
    DOI: 10.1515/1557-4679.1312
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    References listed on IDEAS

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    1. Martin, Emily C. & Betensky, Rebecca A., 2005. "Testing Quasi-Independence of Failure and Truncation Times via Conditional Kendall's Tau," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 484-492, June.
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