title: Developing points-based risk-scoring systems in the presence of competing risks.
abstract: Predicting the occurrence of an adverse event over time is an important issue in clinical medicine. Clinical prediction models and associated points-based risk-scoring systems are popular statistical methods for summarizing the relationship between a multivariable set of patient risk factors and the risk of the occurrence of an adverse event. Points-based risk-scoring systems are popular amongst physicians as they permit a rapid assessment of patient risk without the use of computers or other electronic devices. The use of such points-based risk-scoring systems facilitates evidence-based clinical decision making. There is a growing interest in cause-specific mortality and in non-fatal outcomes. However, when considering these types of outcomes, one must account for competing risks whose occurrence precludes the occurrence of the event of interest. We describe how points-based risk-scoring systems can be developed in the presence of competing events. We illustrate the application of these methods by developing risk-scoring systems for predicting cardiovascular mortality in patients hospitalized with acute myocardial infarction. Code in the R statistical programming language is provided for the implementation of the described methods. © 2016 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
authors.affiliation: Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada.|Institute of Health Management, Policy, and Evaluation, University of Toronto, Toronto, Canada.|Schulich Heart Research Program, Sunnybrook Research Institute, Toronto, Canada.
authors.forename: Peter C
authors.initials: PC
authors.lastname: Austin
authors.affiliation: Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada.|Institute of Health Management, Policy, and Evaluation, University of Toronto, Toronto, Canada.|Department of Medicine, University of Toronto, Toronto, Canada.|Division of Cardiology, Department of Medicine, University Health Network, Toronto, Canada.
authors.forename: Douglas S
authors.initials: DS
authors.lastname: Lee
authors.affiliation: Department of Mathematics and Statistics, Boston University, Boston, MA, U.S.A.|Harvard Clinical Research Institute, Harvard University, Boston, MA, U.S.A.
authors.forename: Ralph B
authors.initials: RB
authors.lastname: D'Agostino
authors.affiliation: Department of Biostatistics, University of North Carolina, Chapel Hill, NC, U.S.A.|Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, NC, U.S.A.
authors.forename: Jason P
authors.initials: JP
authors.lastname: Fine
mesh_terms: D006801:Humans; D009203:Myocardial Infarction / Q000453:epidemiology*; D016016:Proportional Hazards Models; D012016:Reference Values; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors
keywords: Cox proportional hazards model; clinical prediction models; competing risks; risk-scoring systems; survival analysis