Estimation in the progressive illness ‐death model: A nonexhaustive review

AbstractMultistate models can be successfully used for describing complex event history data, for example, describing stages in the disease progression of a patient. The so ‐called “illness‐death” model plays a central role in the theory and practice of these models. Many time‐to‐event datasets from medical studies with multiple end points can be reduced to this generic structure. In these models one important goal is the modeling of transition rates but bi omedical researchers are also interested in reporting interpretable results in a simple and summarized manner. These include estimates of predictive probabilities, such as the transition probabilities, occupation probabilities, cumulative incidence functions, and the sojourn time distributions. We w ill give a review of some of the available methods for estimating such quantities in the progressive illness‐death model conditionally (or not) on covariate measures. For some of these quantities estimators based on subsampling are employed. Subsampling, also referred to as landmarking, leads to s mall sample sizes and usually to heavily censored data leading to estimators with higher variability. To overcome this issue estimators based on a preliminary estimation (presmoothing) of the probability of censoring may be used. Among these, the presmoothed estimators for the cumulative incidences are new. We also introduce feasible estimation methods for the cumulative incidence function conditionally on covariate meas...

Source: Biometrical Journal - November 20, 2018 Category: Biotechnology Authors: Lu ís Meira‐Machado, Marta Sestelo Tags: RESEARCH PAPER Source Type: research