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Sieve estimation in a Markov illness-death process under dual censoring.
Biostatistics 2016; 17(2):350-63B

Abstract

Semiparametric methods are well established for the analysis of a progressive Markov illness-death process observed up to a noninformative right censoring time. However, often the intermediate and terminal events are censored in different ways, leading to a dual censoring scheme. In such settings, unbiased estimation of the cumulative transition intensity functions cannot be achieved without some degree of smoothing. To overcome this problem, we develop a sieve maximum likelihood approach for inference on the hazard ratio. A simulation study shows that the sieve estimator offers improved finite-sample performance over common imputation-based alternatives and is robust to some forms of dependent censoring. The proposed method is illustrated using data from cancer trials.

Authors+Show Affiliations

Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada N2L 3G1 ajboruvka@uwaterloo.ca.Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada N2L 3G1.

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

26598559

Citation

Boruvka, Audrey, and Richard J. Cook. "Sieve Estimation in a Markov Illness-death Process Under Dual Censoring." Biostatistics (Oxford, England), vol. 17, no. 2, 2016, pp. 350-63.
Boruvka A, Cook RJ. Sieve estimation in a Markov illness-death process under dual censoring. Biostatistics. 2016;17(2):350-63.
Boruvka, A., & Cook, R. J. (2016). Sieve estimation in a Markov illness-death process under dual censoring. Biostatistics (Oxford, England), 17(2), pp. 350-63. doi:10.1093/biostatistics/kxv042.
Boruvka A, Cook RJ. Sieve Estimation in a Markov Illness-death Process Under Dual Censoring. Biostatistics. 2016;17(2):350-63. PubMed PMID: 26598559.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Sieve estimation in a Markov illness-death process under dual censoring. AU - Boruvka,Audrey, AU - Cook,Richard J, Y1 - 2015/11/22/ PY - 2014/08/09/received PY - 2015/10/02/accepted PY - 2015/11/25/entrez PY - 2015/11/26/pubmed PY - 2017/1/4/medline KW - Cox model KW - Interval censoring KW - Method of sieves KW - Profile likelihood KW - Progression-free survival SP - 350 EP - 63 JF - Biostatistics (Oxford, England) JO - Biostatistics VL - 17 IS - 2 N2 - Semiparametric methods are well established for the analysis of a progressive Markov illness-death process observed up to a noninformative right censoring time. However, often the intermediate and terminal events are censored in different ways, leading to a dual censoring scheme. In such settings, unbiased estimation of the cumulative transition intensity functions cannot be achieved without some degree of smoothing. To overcome this problem, we develop a sieve maximum likelihood approach for inference on the hazard ratio. A simulation study shows that the sieve estimator offers improved finite-sample performance over common imputation-based alternatives and is robust to some forms of dependent censoring. The proposed method is illustrated using data from cancer trials. SN - 1468-4357 UR - https://www.unboundmedicine.com/medline/citation/26598559/Sieve_estimation_in_a_Markov_illness_death_process_under_dual_censoring_ L2 - https://academic.oup.com/biostatistics/article-lookup/doi/10.1093/biostatistics/kxv042 DB - PRIME DP - Unbound Medicine ER -