Dataset Information

Robust Dynamic Risk Prediction with Longitudinal Studies.

ABSTRACT: Providing accurate and dynamic age-specific risk prediction is a crucial step in precision medicine. In this manuscript, we introduce an approach for estimating the ?-year age-specific absolute risk directly via a flexible varying coefficient model. The approach facilitates the utilization of predictors varying over an individual's lifetime. By using a nonparametric inverse probability weighted kernel estimating equation, the age-specific effects of risk factors are estimated without requiring the specification of the functional form. The approach allows borrowing information across individuals 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 proposed estimation and inference procedures with numerical studies, and make comparisons with existing methods in the literature. We illustrate the performance of our proposed approach by developing a dynamic prediction model using data from the Framingham Study.

SUBMITTER: Zhou QM

PROVIDER: S-EPMC5766050 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Robust Dynamic Risk Prediction with Longitudinal Studies.

Zhou Qian M QM Dai Wei W Zheng Yingye Y Cai Tianxi T

Statistical theory and related fields 20171127 2

Providing accurate and dynamic age-specific risk prediction is a crucial step in precision medicine. In this manuscript, we introduce an approach for estimating the <i>τ</i>-year age-specific absolute risk directly via a flexible varying coefficient model. The approach facilitates the utilization of predictors varying over an individual's lifetime. By using a nonparametric inverse probability weighted kernel estimating equation, the age-specific effects of risk factors are estimated without requ ...[more]

PMID: 29335682

Similar Datasets

Project description:Identification of novel biomarkers for risk prediction is important for disease prevention and optimal treatment selection. However, studies aiming to discover which biomarkers are useful for risk prediction often require the use of stored biological samples from large assembled cohorts, and thus the depletion of a finite and precious resource. To make efficient use of such stored samples, two-phase sampling designs are often adopted as resource-efficient sampling strategies, especially when the outcome of interest is rare. Existing methods for analyzing data from two-phase studies focus primarily on single marker analysis or fitting the Cox regression model to combine information from multiple markers. However, the Cox model may not fit the data well. Under model misspecification, the composite score derived from the Cox model may not perform well in predicting the outcome. Under a general two-phase stratified cohort sampling design, we present a novel approach to combining multiple markers to optimize prediction by fitting a flexible nonparametric transformation model. Using inverse probability weighting to account for the outcome-dependent sampling, we propose to estimate the model parameters by maximizing an objective function which can be interpreted as a weighted C-statistic for survival outcomes. Regardless of model adequacy, the proposed procedure yields a sensible composite risk score for prediction. A major obstacle for making inference under two phase studies is due to the correlation induced by the finite population sampling, which prevents standard inference procedures such as the bootstrap from being used for variance estimation. We propose a resampling procedure to derive valid confidence intervals for the model parameters and the C-statistic accuracy measure. We illustrate the new methods with simulation studies and an analysis of a two-phase study of high-density lipoprotein cholesterol (HDL-C) subtypes for predicting the risk of coronary heart disease.

Dataset Information

Robust Dynamic Risk Prediction with Longitudinal Studies.

Publications

Robust Dynamic Risk Prediction with Longitudinal Studies.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets