DOI: 10.1101/19007062Sep 19, 2019Paper

A structured approach to evaluating life course hypotheses: Moving beyond analyses of exposed versus unexposed in the omics context

MedRxiv : the Preprint Server for Health Sciences
Y. ZhuAndrew D.A.C. Smith

Abstract

Background: Life course epidemiology provides a framework for studying the effects of time-varying exposures on health outcomes. The structured life course modeling approach (SLCMA) is a theory-driven analytic method that empirically compares multiple prespecified life course hypotheses characterizing time-dependent exposure-outcome relationships to determine which theory best fits the observed data. However, the statistical properties of inference methods used with the SLCMA have not been investigated with high-dimensional omics outcomes. Methods: We performed simulations and empirical analyses to evaluate the performance of the SLCMA when applied to genome-wide DNA methylation (DNAm). In the simulations, we compared five statistical inference tests used by SLCMA (n=700). For each, we assessed the familywise error rate (FWER), statistical power, and confidence interval coverage to determine whether inference based on these tests was valid in the presence of substantial multiple testing and small effect sizes, two hallmark challenges of inference from omics data. In the empirical analyses, we applied the SLCMA to evaluate the time-dependent relationship of childhood abuse with genome-wide DNAm (n=703). Results: In the simulatio...Continue Reading

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