title: Semi-Competing Risks Data Analysis: Accounting for Death as a Competing Risk When the Outcome of Interest Is Nonterminal.
abstract: 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.
authors.affiliation: From the Department of Biostatistics, Harvard Chan School of Public Health, Boston, MA (S.H.); and Epidemiology and Biostatistics Core, The Forsyth Institute, Cambridge, MA (K.H.L.). shaneuse@hsph.harvard.edu.
authors.forename: Sebastien
authors.initials: S
authors.lastname: Haneuse
authors.affiliation: From the Department of Biostatistics, Harvard Chan School of Public Health, Boston, MA (S.H.); and Epidemiology and Biostatistics Core, The Forsyth Institute, Cambridge, MA (K.H.L.).
authors.forename: Kyu Ha
authors.initials: KH
authors.lastname: Lee
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D002423:Cause of Death; D003627:Data Interpretation, Statistical; D023381:Endpoint Determination / Q000706:statistics & numerical data*; D006333:Heart Failure / Q000175:diagnosis / Q000401:mortality / Q000628:therapy*; D006801:Humans; D016015:Logistic Models; D006278:Medicare / Q000706:statistics & numerical data; D016017:Odds Ratio; D010359:Patient Readmission / Q000706:statistics & numerical data*; D012107:Research Design / Q000706:statistics & numerical data*; D018570:Risk Assessment; D012307:Risk Factors; D016019:Survival Analysis; D013997:Time Factors; D014481:United States
keywords: death; heart failure; readmission; risk assessment; survival analysis