Exposure enriched outcome dependent designs for longitudinal studies of gene-environment interaction

Statistics in Medicine
Zhichao SunSung Kyun Park

Abstract

Joint effects of genetic and environmental factors have been increasingly recognized in the development of many complex human diseases. Despite the popularity of case-control and case-only designs, longitudinal cohort studies that can capture time-varying outcome and exposure information have long been recommended for gene-environment (G × E) interactions. To date, literature on sampling designs for longitudinal studies of G × E interaction is quite limited. We therefore consider designs that can prioritize a subsample of the existing cohort for retrospective genotyping on the basis of currently available outcome, exposure, and covariate data. In this work, we propose stratified sampling based on summaries of individual exposures and outcome trajectories and develop a full conditional likelihood approach for estimation that adjusts for the biased sample. We compare the performance of our proposed design and analysis with combinations of different sampling designs and estimation approaches via simulation. We observe that the full conditional likelihood provides improved estimates for the G × E interaction and joint exposure effects over uncorrected complete-case analysis, and the exposure enriched outcome trajectory dependent de...Continue Reading

References

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Mar 29, 2008·Biostatistics·Jonathan S Schildcrout, Patrick J Heagerty
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Jun 1, 2015·European Journal of Epidemiology·Peter Kraft, Hugues Aschard
Sep 1, 2015·The Annals of Applied Statistics·Jonathan S SchildcroutPatrick J Heagerty

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Citations

Jun 6, 2019·American Journal of Epidemiology·Jonathan S SchildcroutPatrick J Heagerty

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