Mediation Analysis with Random Distribution as Mediator with an Application to iCOMPARE Trial

Jingru Zhang, Mathias Basner, Christopher W. Jones, David F. Dinges, Haochang Shou and Hongzhe Li ()
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Jingru Zhang: University of Pennsylvania Perelman School of Medicine
Mathias Basner: University of Pennsylvania Perelman School of Medicine
Christopher W. Jones: University of Pennsylvania Perelman School of Medicine
David F. Dinges: University of Pennsylvania Perelman School of Medicine
Haochang Shou: University of Pennsylvania Perelman School of Medicine
Hongzhe Li: University of Pennsylvania Perelman School of Medicine

Statistics in Biosciences, 2024, vol. 16, issue 1, No 6, 107-128

Abstract: Abstract Physical activity has long been shown to be associated with biological and physiological performance and risk of diseases. It is of great interest to assess whether the effect of an exposure or intervention on an outcome is mediated through physical activity measured by modern wearable devices such as actigraphy. However, existing methods for mediation analysis focus almost exclusively on mediation variable that is in the Euclidean space, which cannot be applied directly to physical activity measured by wearable devices. Such data is best summarized in the form of a random distribution. In this paper, we develop a structural equation model to the setting where a random distribution is treated as the mediator. We provide sufficient conditions for identifying the average causal effects of a distribution mediator and present methods for estimating the direct and mediating effects of a random distribution mediator on the outcome. We apply our method to analyze the iCOMPARE data set that compares flexible duty-hour policies and standard duty-hour policies on interns’ sleep related outcomes and to investigate the mediation effect of physical activity on the causal path between flexible duty-hour policies and sleep related outcomes.

Keywords: Distribution; Indirect effect; Quantile function; Wasserstein metric; Wearable device data (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s12561-023-09383-9

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