Sieve estimation in a Markov illness-death process under dual censoring
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.
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Cite this version of the work
Richard J. Cook, Audrey Boruvka
(2016).
Sieve estimation in a Markov illness-death process under dual censoring. UWSpace.
http://hdl.handle.net/10012/10528
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