title: 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.
authors.affiliation: Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada N2L 3G1 ajboruvka@uwaterloo.ca.
authors.forename: Audrey
authors.initials: A
authors.lastname: Boruvka
authors.affiliation: Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, ON, Canada N2L 3G1.
authors.forename: Richard J
authors.initials: RJ
authors.lastname: Cook
mesh_terms: D018450:Disease Progression; D018572:Disease-Free Survival; D006801:Humans; D016013:Likelihood Functions; D008390:Markov Chains; D016016:Proportional Hazards Models
keywords: Cox model; Interval censoring; Method of sieves; Profile likelihood; Progression-free survival