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Robust dynamic risk prediction with longitudinal studies

Qian M. Zhou ,

Department of Mathematics and Statistics, Mississippi State University, Mississippi, USA

Wei Dai ,

Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA

Yingye Zheng ,

Department of Biostatistics and Biomathematics, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

Tianxi Cai

Department of Mathematics and Statistics, Mississippi State University, Mississippi, USA

tcai@hsph.harvard.edu

Pages 159-170 | Received 21 Mar. 2017, Accepted 31 Oct. 2017, Published online: 27 Nov. 2017,
  • Abstract
  • Full Article
  • References
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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 τ-year age-specific absolute risk directly 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 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.

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