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Activity Number: 409 - Survival Analysis I
Type: Contributed
Date/Time: Tuesday, July 31, 2018 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #329067
Title: Semiparametric Transformation Probit Models with Current-Status Data
Author(s): Jing Qin* and Hao Liu
Companies: National Institute of Allergy and Infectious Diseases, NIH and Indiana University Melvin and Bren Simon Cancer Center
Keywords: Current status data; EM algorithm; Pool adjacent violation algorithm; Transformation Probit model

Univariate and multivariate current-status data are frequently encountered in biomedical and public health studies. In this talk, we study the maximum likelihood estimations for univariate and bivariate current-status data under the semiparametric transformation probit regression models. We present a simple computational procedure combining the expectation-maximization algorithm with the pool-adjacent-violators algorithm for solving the monotone constraint on the baseline function. Extensive simulation studies showed that the proposed computational procedures performed well under small or moderate sample sizes. We demonstrate the estimation procedure with two real data examples in the areas of diabetic and HIV research. This is a joint work with Dr. Hao Liu at Indiana University Melvin and Bren Simon Cancer Center.

Authors who are presenting talks have a * after their name.

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