Targeted estimation of state occupation probabilities for the non-Markov illness-death model

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We use semi-parametric efficiency theory to derive a class of estimators for the state occupation probabilities of the continuous-time irreversible illness-death model. We consider both the setting with and without additional baseline information available, where we impose no specific functional form on the intensity functions of the model. We show that any estimator in the class is asymptotically linear under suitable assumptions about the estimators of the intensity functions. In particular, the assumptions are weak enough to allow the use of data-adaptive methods, which is important for making the identifying assumption of coarsening at random plausible in realistic settings. We suggest a flexible method for estimating the transition intensity functions of the illness-death model based on penalized Poisson regression. We apply this method to estimate the nuisance parameters of an illness-death model in a simulation study and a real-world application.

Original languageEnglish
JournalScandinavian Journal of Statistics
Issue number3
Pages (from-to)1532-1551
Number of pages20
Publication statusPublished - 2023

    Research areas

  • data-adaptive methods, efficient estimation, illness-death model, state-dependent censoring, targeted learning, INTEGRATED TRANSITION HAZARDS, NONPARAMETRIC-ESTIMATION

ID: 346199151