Absolute risk models can also be applied to assess the power of clinical trials by projecting the expected distribution of disease risk from the distribution of risk factors in a population. Assessment of the distribution of risks for individuals in the population allows public health researchers to weigh the risks and benefits of a given intervention, such as a screening regimen, for the entire population. However, there is increasing consensus in the medical community that these decisions should instead be guided directly by individualized estimates of risk, which can be obtained from absolute risk models that include a wider array of environmental and genetic risk factors. Decisions regarding the initiation of screening or preventive intervention are often made on the basis of age and family history, considered proxies for risk. For example, absolute risk models can be used to identify individuals who have a high risk of disease in order to target screening and disease prevention strategies. Assessment of absolute risk of disease is fundamental for developing health intervention strategies to optimize an individual’s risks and benefits. Absolute risk models for cancers and other diseases have important clinical and public health applications. ![]() We provide an illustration of the utility of iCARE for building, validating and applying absolute risk models using breast cancer as an example.Ībsolute risk models estimate disease risk in an upcoming time interval based on known risk factors for healthy individuals in a population, accounting for the presence of competing outcomes, such as death from other causes. The validation component of the software implements the methods for evaluation of model calibration, discrimination and risk-stratification based on independent validation datasets. ![]() The software allows single nucleotide polymorphisms (SNPs) to be incorporated into the model using published odds ratios and allele frequencies. The tool can handle missing information on risk factors for individuals for whom risks are to be predicted using a coherent approach where all estimates are derived from a single model after appropriate model averaging. An attractive feature of the software is that it gives users flexibility to update models rapidly based on new knowledge on risk factors and tailor models to different populations by specifying three input arguments: a model for relative risk, an age-specific disease incidence rate and the distribution of risk factors for the population of interest. This report describes an R package, called the Individualized Coherent Absolute Risk Estimator ( iCARE) tool, that allows researchers to build and evaluate models for absolute risk and apply them to estimate an individual’s risk of developing disease during a specified time interval based on a set of user defined input parameters.
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