Sieve maximum likelihood regression analysis of dependent current status data

Ling Ma, Tao Hu and Jianguo Sun

Biometrika, 2015, vol. 102, issue 3, 731-738

Abstract: Current status data occur in contexts including demographic studies and tumorigenicity experiments. In such cases, each subject is observed only once and the failure time of interest is either left- or right-censored (Kalbfleisch & Prentice, 2002). Many methods have been developed for the analysis of such data (Huang, 1996; Sun, 2006), most of which assume that the failure time and the observation time are independent completely or given covariates. In this paper, we present a sieve maximum likelihood approach for current status data when independence does not hold. A copula model and monotone I-splines are used and the asymptotic properties of the resulting estimators are established. In particular, the estimated regression parameters are shown to be semiparametrically efficient. An illustrative example is provided.

Date: 2015
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Citations: View citations in EconPapers (19)

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