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Semiparametric probit models with univariate and bivariate current-status data.
Biometrics 2018; 74(1):68-76B

Abstract

Multivariate current-status data are frequently encountered in biomedical and public health studies. Semiparametric regression models have been extensively studied for univariate current-status data, but most existing estimation procedures are computationally intensive, involving either penalization or smoothing techniques. It becomes more challenging for the analysis of multivariate current-status data. In this article, we study the maximum likelihood estimations for univariate and bivariate current-status data under the semiparametric 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. Asymptotic properties of the maximum likelihood estimators are investigated, including the calculation of the explicit information bound for univariate current-status data, as well as the asymptotic consistency and convergence rate for bivariate current-status data. 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.

Authors+Show Affiliations

Division of Biostatistics, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, Texas 77030, U.S.A.Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases National Institutes of Health, Bethesda, Maryland 20892, U.S.A.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

28437561

Citation

Liu, Hao, and Jing Qin. "Semiparametric Probit Models With Univariate and Bivariate Current-status Data." Biometrics, vol. 74, no. 1, 2018, pp. 68-76.
Liu H, Qin J. Semiparametric probit models with univariate and bivariate current-status data. Biometrics. 2018;74(1):68-76.
Liu, H., & Qin, J. (2018). Semiparametric probit models with univariate and bivariate current-status data. Biometrics, 74(1), pp. 68-76. doi:10.1111/biom.12709.
Liu H, Qin J. Semiparametric Probit Models With Univariate and Bivariate Current-status Data. Biometrics. 2018;74(1):68-76. PubMed PMID: 28437561.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Semiparametric probit models with univariate and bivariate current-status data. AU - Liu,Hao, AU - Qin,Jing, Y1 - 2017/04/24/ PY - 2014/09/01/received PY - 2017/03/01/revised PY - 2017/03/01/accepted PY - 2017/4/25/pubmed PY - 2017/4/25/medline PY - 2017/4/25/entrez KW - EM algorithm KW - Isotonic regression KW - Maximum likelihood estimation KW - Multivariate current-status data KW - Survival analysis SP - 68 EP - 76 JF - Biometrics JO - Biometrics VL - 74 IS - 1 N2 - Multivariate current-status data are frequently encountered in biomedical and public health studies. Semiparametric regression models have been extensively studied for univariate current-status data, but most existing estimation procedures are computationally intensive, involving either penalization or smoothing techniques. It becomes more challenging for the analysis of multivariate current-status data. In this article, we study the maximum likelihood estimations for univariate and bivariate current-status data under the semiparametric 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. Asymptotic properties of the maximum likelihood estimators are investigated, including the calculation of the explicit information bound for univariate current-status data, as well as the asymptotic consistency and convergence rate for bivariate current-status data. 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. SN - 1541-0420 UR - https://www.unboundmedicine.com/medline/citation/28437561/Semiparametric_probit_models_with_univariate_and_bivariate_current_status_data_ L2 - https://doi.org/10.1111/biom.12709 DB - PRIME DP - Unbound Medicine ER -