Illness-death models provide a robust statistical framework for analyzing hospital readmissions by appropriately accounting for the competing risk of death.
Answers are grounded in this paper and cite their sources.
Illness-death models may improve HF readmission analyses by accounting for death; extends semi-competing risks methods to large cohorts.
Hospital readmission is a key marker of quality of health care. Notwithstanding its widespread use, however, it remains controversial in part because statistical methods used to analyze readmission, primarily logistic regression and related models, may not appropriately account for patients who die before experiencing a readmission event within the time frame of interest. Toward resolving this, we describe and illustrate the semi-competing risks framework, which refers to the general setting where scientific interest lies with some nonterminal event (eg, readmission), the occurrence of which is subject to a terminal event (eg, death). Although several statistical analysis methods have been proposed for semi-competing risks data, we describe in detail the use of illness-death models primarily because of their relation to well-known methods for survival analysis and the availability of software. We also describe and consider in detail several existing approaches that could, in principle, be used to analyze semi-competing risks data, including composite end point and competing risks analyses. Throughout we illustrate the ideas and methods using data on N=49 763 Medicare beneficiaries hospitalized between 2011 and 2013 with a principle discharge diagnosis of heart failure.
No takes yet. Share an insight, caveat, or question.
Haneuse et al. (2016) studied this question.
Synapse has enriched 10 related papers. Consider them for comparative context: