Tags

Type your tag names separated by a space and hit enter

A pairwise likelihood augmented Cox estimator for left-truncated data.
Biometrics 2018; 74(1):100-108B

Abstract

Survival data collected from a prevalent cohort are subject to left truncation and the analysis is challenging. Conditional approaches for left-truncated data could be inefficient as they ignore the information in the marginal likelihood of the truncation times. Length-biased sampling methods may improve the estimation efficiency but only when the underlying truncation time is uniform; otherwise, they may generate biased estimates. We propose a semiparametric method for left-truncated data under the Cox model with no parametric distributional assumption about the truncation times. Our approach is to make inference based on the conditional likelihood augmented with a pairwise likelihood, which eliminates the truncation distribution, yet retains the information about the regression coefficients and the baseline hazard function in the marginal likelihood. An iterative algorithm is provided to solve for the regression coefficients and the baseline hazard function simultaneously. By empirical process and U-process theories, it has been shown that the proposed estimator is consistent and asymptotically normal with a closed-form consistent variance estimator. Simulation studies show substantial efficiency gain of our estimator in both the regression coefficients and the cumulative baseline hazard function over the conditional approach estimator. When the uniform truncation assumption holds, our estimator enjoys smaller biases and efficiency comparable to that of the full maximum likelihood estimator. An application to the analysis of a chronic kidney disease cohort study illustrates the utility of the method.

Authors+Show Affiliations

Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A.Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A.Biostatistics Research Branch, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, Maryland 20892, U.S.A.Kidney Epidemiology and Cost Center, University of Michigan, Ann Arbor, Michigan 48109, U.S.A.Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

28853158

Citation

Wu, Fan, et al. "A Pairwise Likelihood Augmented Cox Estimator for Left-truncated Data." Biometrics, vol. 74, no. 1, 2018, pp. 100-108.
Wu F, Kim S, Qin J, et al. A pairwise likelihood augmented Cox estimator for left-truncated data. Biometrics. 2018;74(1):100-108.
Wu, F., Kim, S., Qin, J., Saran, R., & Li, Y. (2018). A pairwise likelihood augmented Cox estimator for left-truncated data. Biometrics, 74(1), pp. 100-108. doi:10.1111/biom.12746.
Wu F, et al. A Pairwise Likelihood Augmented Cox Estimator for Left-truncated Data. Biometrics. 2018;74(1):100-108. PubMed PMID: 28853158.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A pairwise likelihood augmented Cox estimator for left-truncated data. AU - Wu,Fan, AU - Kim,Sehee, AU - Qin,Jing, AU - Saran,Rajiv, AU - Li,Yi, Y1 - 2017/08/29/ PY - 2016/10/01/received PY - 2017/06/01/revised PY - 2017/06/01/accepted PY - 2017/8/31/pubmed PY - 2019/2/23/medline PY - 2017/8/31/entrez KW - Chronic kidney disease KW - Composite likelihood KW - Empirical process KW - Self-consistency KW - U-process SP - 100 EP - 108 JF - Biometrics JO - Biometrics VL - 74 IS - 1 N2 - Survival data collected from a prevalent cohort are subject to left truncation and the analysis is challenging. Conditional approaches for left-truncated data could be inefficient as they ignore the information in the marginal likelihood of the truncation times. Length-biased sampling methods may improve the estimation efficiency but only when the underlying truncation time is uniform; otherwise, they may generate biased estimates. We propose a semiparametric method for left-truncated data under the Cox model with no parametric distributional assumption about the truncation times. Our approach is to make inference based on the conditional likelihood augmented with a pairwise likelihood, which eliminates the truncation distribution, yet retains the information about the regression coefficients and the baseline hazard function in the marginal likelihood. An iterative algorithm is provided to solve for the regression coefficients and the baseline hazard function simultaneously. By empirical process and U-process theories, it has been shown that the proposed estimator is consistent and asymptotically normal with a closed-form consistent variance estimator. Simulation studies show substantial efficiency gain of our estimator in both the regression coefficients and the cumulative baseline hazard function over the conditional approach estimator. When the uniform truncation assumption holds, our estimator enjoys smaller biases and efficiency comparable to that of the full maximum likelihood estimator. An application to the analysis of a chronic kidney disease cohort study illustrates the utility of the method. SN - 1541-0420 UR - https://www.unboundmedicine.com/medline/citation/28853158/A_pairwise_likelihood_augmented_Cox_estimator_for_left_truncated_data_ L2 - https://doi.org/10.1111/biom.12746 DB - PRIME DP - Unbound Medicine ER -