title: Population-based absolute risk estimation with survey data.
abstract: Absolute risk is the probability that a cause-specific event occurs in a given time interval in the presence of competing events. We present methods to estimate population-based absolute risk from a complex survey cohort that can accommodate multiple exposure-specific competing risks. The hazard function for each event type consists of an individualized relative risk multiplied by a baseline hazard function, which is modeled nonparametrically or parametrically with a piecewise exponential model. An influence method is used to derive a Taylor-linearized variance estimate for the absolute risk estimates. We introduce novel measures of the cause-specific influences that can guide modeling choices for the competing event components of the model. To illustrate our methodology, we build and validate cause-specific absolute risk models for cardiovascular and cancer deaths using data from the National Health and Nutrition Examination Survey. Our applications demonstrate the usefulness of survey-based risk prediction models for predicting health outcomes and quantifying the potential impact of disease prevention programs at the population level.
authors.affiliation: Division of Cancer Epidemiology and Genetics, National Cancer Institute, 9609 Medical Center Drive, Bethesda, MD, 20892-9702, USA, kovalchiksa@mail.nih.gov.
authors.forename: Stephanie A
authors.initials: SA
authors.lastname: Kovalchik
authors.forename: Ruth M
authors.initials: RM
authors.lastname: Pfeiffer
mesh_terms: D002318:Cardiovascular Diseases / Q000401:mortality; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D009369:Neoplasms / Q000401:mortality; D009749:Nutrition Surveys / Q000706:statistics & numerical data; D016016:Proportional Hazards Models; D012306:Risk; D018709:Statistics, Nonparametric; D014481:United States / Q000453:epidemiology