Semiparametric Methods for Semi-competing Risks Problem with Censoring and Truncation

Hongyu Jiang, Jason Fine and Richard Chappell
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Hongyu Jiang: Harvard University
Jason Fine: University of Wisconsin-Madison
Richard Chappell: University of Wisconsin-Madison

No 1015, Harvard University Biostatistics Working Paper Series from Berkeley Electronic Press

Abstract: Studies of chronic life-threatening diseases often involve both mortality and morbidity. In observational studies, the data may also be subject to administrative left truncation and right censoring. Since mortality and morbidity may be correlated and mortality may censor morbidity, the Lynden-Bell estimator for left truncated and right censored data may be biased for estimating the marginal survival function of the non-terminal event. We propose a semiparametric estimator for this survival function based on a joint model for the two time-to-event variables, which utilizes the gamma frailty specification in the region of the observable data. Firstly, we develop a novel estimator for the gamma frailty parameter under left truncation. Using this estimator, we then derive a closed form estimator for the marginal distribution of the non-terminal event. The large sample properties of the estimators are established via asymptotic theory. The methodology performs well with moderate sample sizes, both in simulations and in an analysis of data from a diabetes registry.

Keywords: Bivariate survival function; Concordance probability; Copula; Semi-competing risks; truncation (search for similar items in EconPapers)
Date: 2004-10-12
New Economics Papers: this item is included in nep-ecm
Note: oai:bepress.com:harvardbiostat-1015
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Persistent link: https://EconPapers.repec.org/RePEc:bep:hvdbio:1015

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