Facultade de Fisioterapia

Estimation in the progressive illness-death model: a non-exhaustive review

Meira-Machado, Luis; Sestelo, Marta
Abstract:
Multistate 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 biomedical 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 will 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 small 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 measures. The proposed methods are illustrated using real data. A comparative simulation study of several estimation approaches is performed and existing software in the form of R packages is discussed.
Year:
2019
Type of Publication:
Article
Keywords:
illness death model; Kaplan Meier; landmark approach; nonparametric estimation; survival analysis
Journal:
Biometrical Journal
Volume:
61
Number:
2 (Special
Pages:
245-263
Month:
March
Note:
Q2 51/123 h-index 1.255 (JCR2018)
DOI:
https://doi.org/10.1002/bimj.201700200
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