Mediation Analysis for Sparse and Irregularly Spaced Longitudinal Outcomes with Application to the MrOS Sleep Study

TL;DR

This paper develops a novel mediation analysis method for high-dimensional mediators and sparse, irregular longitudinal data, applied to understanding cognitive decline in older men through sleep and lipid pathways.

Contribution

It introduces a new approach for mediation analysis that handles high-dimensional mediators and irregular longitudinal outcomes, addressing key correlation and dimensionality challenges.

Findings

Identified significant lipid mediators linking sleep rhythms to cognitive decline.

Revealed potential biological pathways involving lipid metabolites.

Validated the method on the MrOS Sleep Study data.

Abstract

Mediation analysis has become a widely used method for identifying the pathways through which an independent variable influences a dependent variable via intermediate mediators. However, limited research addresses the case where mediators are high-dimensional and the outcome is represented by sparse, irregularly spaced longitudinal data. To address these challenges, we propose a mediation analysis approach for scalar exposures, high-dimensional mediators, and sparse longitudinal outcomes. This approach effectively identifies significant mediators by addressing two key issues: (i) the underlying correlation structure within the sparse and irregular cognitive measurements, and (ii) adjusting mediation effects to handle the high-dimensional set of candidate mediators. In the MrOS Sleep study, our primary objective is to explore lipid pathways that may mediate the relationship between rest-activity rhythms and longitudinal cognitive decline in older men. Our findings suggest a potential mechanism involving rest-activity rhythms, lipid metabolites, and cognitive decline, and highlight significant mediators identified through multiple testing procedures.