![]() Risk projections in a target population of US White non-Hispanic women aged 50-70 years assessed potential improvements in risk stratification by adding mammographic breast density (MD) and polygenic risk score (PRS). Performance of two recently developed models, iCARE-BPC3 and iCARE-Lit, were compared with two established models (BCRAT, IBIS) based on classical risk factors in a UK-based cohort of 64,874 White non-Hispanic women (863 cases) aged 35-74 years. We used the Individualized Coherent Absolute Risk Estimation (iCARE) as a flexible tool for risk model development, comparative model validation, and to make projections for population risk stratification. We provide an illustration of the utility of iCARE for building, validating and applying absolute risk models using breast cancer as an example.Įxternal validation of risk models is critical for risk stratified breast cancer prevention. #Icare pack softwareThe 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. #Icare pack updateAn 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: (1) a model for relative risk, (2) an age-specific disease incidence rate, (3) the distribution of risk factors for the population of interest. This report describes a R package, called the Individualized Coherent Absolute Risk Estimation ( 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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