Sieve estimation in a Markov illness-death process under dual censoring
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Date
2016-02-29
Authors
Cook, Richard J.
Boruvka, Audrey
Advisor
Journal Title
Journal ISSN
Volume Title
Publisher
Oxford Journals
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.
Description
This is a pre-copyedited, author-produced PDF of an article accepted for publication in Bioinformatics following peer review.
The version of record Boruvka, Audrey and Cook, Richard J. (2016). Biostatistics, 17(2): 350-363. DOI: 10.1093/biostatistics/kxv042 is available online at: http://dx.doi.org/10.1093/biostatistics/kxv042
Keywords
Cox model, Interval censoring, Method of sieves, Profile likelihood, Progression-free survival