Providing accurate and dynamic age-specific risk prediction is a crucial step in precision medicine.In this manuscript,we introduce an approach for estimating theτ-year age-specific absolute riskdirectly via a flexib...Providing accurate and dynamic age-specific risk prediction is a crucial step in precision medicine.In this manuscript,we introduce an approach for estimating theτ-year age-specific absolute riskdirectly via a flexible varying coefficient model.The approach facilitates the utilisation of predictors varying over an individual’s lifetime.By using a nonparametric inverse probability weightedkernel estimating equation,the age-specific effects of risk factors are estimated without requiring the specification of the functional form.The approach allows borrowing information acrossindividuals of similar ages,and therefore provides a practical solution for situations where the longitudinal information is only measured sparsely.We evaluate the performance of the proposedestimation and inference procedures with numerical studies,and make comparisons with existingmethods in the literature.We illustrate the performance of our proposed approach by developinga dynamic prediction model using data from the Framingham Study.展开更多
基金The work is supported by grants from Natural Sciences and Engineering Research Council of Canada[grant number U01-CA86368][grant number P01-CA053996]+1 种基金[grant number R01-GM085047][grant number U54-HG007963][grant number R01-HL089778]from the National Institutes of Health.
文摘Providing accurate and dynamic age-specific risk prediction is a crucial step in precision medicine.In this manuscript,we introduce an approach for estimating theτ-year age-specific absolute riskdirectly via a flexible varying coefficient model.The approach facilitates the utilisation of predictors varying over an individual’s lifetime.By using a nonparametric inverse probability weightedkernel estimating equation,the age-specific effects of risk factors are estimated without requiring the specification of the functional form.The approach allows borrowing information acrossindividuals of similar ages,and therefore provides a practical solution for situations where the longitudinal information is only measured sparsely.We evaluate the performance of the proposedestimation and inference procedures with numerical studies,and make comparisons with existingmethods in the literature.We illustrate the performance of our proposed approach by developinga dynamic prediction model using data from the Framingham Study.
