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Conditional maximum likelihood estimation in semiparametric transformation model with LTRC data.
Lifetime Data Anal 2018; 24(2):250-272LD

Abstract

Left-truncated data often arise in epidemiology and individual follow-up studies due to a biased sampling plan since subjects with shorter survival times tend to be excluded from the sample. Moreover, the survival time of recruited subjects are often subject to right censoring. In this article, a general class of semiparametric transformation models that include proportional hazards model and proportional odds model as special cases is studied for the analysis of left-truncated and right-censored data. We propose a conditional likelihood approach and develop the conditional maximum likelihood estimators (cMLE) for the regression parameters and cumulative hazard function of these models. The derived score equations for regression parameter and infinite-dimensional function suggest an iterative algorithm for cMLE. The cMLE is shown to be consistent and asymptotically normal. The limiting variances for the estimators can be consistently estimated using the inverse of negative Hessian matrix. Intensive simulation studies are conducted to investigate the performance of the cMLE. An application to the Channing House data is given to illustrate the methodology.

Authors+Show Affiliations

Institute of Public Health, School of Medicine, National Yang-Ming University, Taipei, Taiwan, ROC.Department of Statistics, Tunghai University, Xitun District, Taichung, 40704, Taiwan, ROC. psshen@thu.edu.tw.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

28168333

Citation

Chen, Chyong-Mei, and Pao-Sheng Shen. "Conditional Maximum Likelihood Estimation in Semiparametric Transformation Model With LTRC Data." Lifetime Data Analysis, vol. 24, no. 2, 2018, pp. 250-272.
Chen CM, Shen PS. Conditional maximum likelihood estimation in semiparametric transformation model with LTRC data. Lifetime Data Anal. 2018;24(2):250-272.
Chen, C. M., & Shen, P. S. (2018). Conditional maximum likelihood estimation in semiparametric transformation model with LTRC data. Lifetime Data Analysis, 24(2), pp. 250-272. doi:10.1007/s10985-016-9385-9.
Chen CM, Shen PS. Conditional Maximum Likelihood Estimation in Semiparametric Transformation Model With LTRC Data. Lifetime Data Anal. 2018;24(2):250-272. PubMed PMID: 28168333.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Conditional maximum likelihood estimation in semiparametric transformation model with LTRC data. AU - Chen,Chyong-Mei, AU - Shen,Pao-Sheng, Y1 - 2017/02/06/ PY - 2015/04/15/received PY - 2016/11/04/accepted PY - 2017/2/9/pubmed PY - 2019/5/10/medline PY - 2017/2/8/entrez KW - Maximum conditional likelihood KW - Proportional hazards model KW - Proportional odds model KW - Semiparametric transformation model KW - Truncation data SP - 250 EP - 272 JF - Lifetime data analysis JO - Lifetime Data Anal VL - 24 IS - 2 N2 - Left-truncated data often arise in epidemiology and individual follow-up studies due to a biased sampling plan since subjects with shorter survival times tend to be excluded from the sample. Moreover, the survival time of recruited subjects are often subject to right censoring. In this article, a general class of semiparametric transformation models that include proportional hazards model and proportional odds model as special cases is studied for the analysis of left-truncated and right-censored data. We propose a conditional likelihood approach and develop the conditional maximum likelihood estimators (cMLE) for the regression parameters and cumulative hazard function of these models. The derived score equations for regression parameter and infinite-dimensional function suggest an iterative algorithm for cMLE. The cMLE is shown to be consistent and asymptotically normal. The limiting variances for the estimators can be consistently estimated using the inverse of negative Hessian matrix. Intensive simulation studies are conducted to investigate the performance of the cMLE. An application to the Channing House data is given to illustrate the methodology. SN - 1572-9249 UR - https://www.unboundmedicine.com/medline/citation/28168333/Conditional_maximum_likelihood_estimation_in_semiparametric_transformation_model_with_LTRC_data_ L2 - https://doi.org/10.1007/s10985-016-9385-9 DB - PRIME DP - Unbound Medicine ER -