Quantile Mediation Models: A Comparison of Methods for Assessing Mediation Across the Outcome Distribution

High-dimensional quantile mediation analysis with application to a birth cohort study of mother-newborn pairs

MOTIVATION

There has been substantial recent interest in developing methodology for high-dimensional mediation analysis. Yet, the majority of mediation statistical methods lean heavily on mean regression, which limits their ability to fully capture the complex mediating effects across the outcome distribution. To bridge this gap, we propose a novel approach for selecting and testing mediators throughout the full range of the outcome distribution spectrum.

RESULTS

The proposed high-dimensional quantile mediation model provides a comprehensive insight into how potential mediators impact outcomes via their mediation pathways. This method's efficacy is demonstrated through extensive simulations. The study presents a real-world data application examining the mediating effects of DNA methylation on the relationship between maternal smoking and offspring birthweight.

AVAILABILITY AND IMPLEMENTATION

Our method offers a publicly available and user-friendly function qHIMA(), which can be accessed through the R package HIMA at https://CRAN.R-project.org/package=HIMA.

The role of body mass index at diagnosis of colorectal cancer on Black-White disparities in survival: a density regression mediation approach

The study of racial/ethnic inequalities in health is important to reduce the uneven burden of disease. In the case of colorectal cancer (CRC), disparities in survival among non-Hispanic Whites and Blacks are well documented, and mechanisms leading to these disparities need to be studied formally. It has also been established that body mass index (BMI) is a risk factor for developing CRC, and recent literature shows BMI at diagnosis of CRC is associated with survival. Since BMI varies by racial/ethnic group, a question that arises is whether differences in BMI are partially responsible for observed racial/ethnic disparities in survival for CRC patients. This article presents new methodology to quantify the impact of the hypothetical intervention that matches the BMI distribution in the Black population to a potentially complex distributional form observed in the White population on racial/ethnic disparities in survival. Our density mediation approach can be utilized to estimate natural direct and indirect effects in the general causal mediation setting under stronger assumptions. We perform a simulation study that shows our proposed Bayesian density regression approach performs as well as or better than current methodology allowing for a shift in the mean of the distribution only, and that standard practice of categorizing BMI leads to large biases when BMI is a mediator variable. When applied to motivating data from the Cancer Care Outcomes Research and Surveillance (CanCORS) Consortium, our approach suggests the proposed intervention is potentially beneficial for elderly and low-income Black patients, yet harmful for young or high-income Black populations.

Quantile causal mediation analysis allowing longitudinal data

Mediation analysis has mostly been conducted with mean regression models. With this approach modeling means, formulae for direct and indirect effects are based on changes in means, which may not capture effects that occur in units at the tails of mediator and outcome distributions. Individuals with extreme values of medical endpoints are often more susceptible to disease and can be missed if one investigates mean changes only. We derive the controlled direct and indirect effects of an exposure along percentiles of the mediator and outcome using quantile regression models and a causal framework. The quantile regression models can accommodate an exposure-mediator interaction and random intercepts to allow for longitudinal mediator and outcome. Because DNA methylation acts as a complex "switch" to control gene expression and fibrinogen is a cardiovascular factor, individuals with extreme levels of these markers may be more susceptible to air pollution. We therefore apply this methodology to environmental data to estimate the effect of air pollution, as measured by particle number, on fibrinogen levels through a change in interferon-gamma (IFN-γ) methylation. We estimate the controlled direct effect of air pollution on the qth percentile of fibrinogen and its indirect effect through a change in the pth percentile of IFN-γ methylation. We found evidence of a direct effect of particle number on the upper tail of the fibrinogen distribution. We observed a suggestive indirect effect of particle number on the upper tail of the fibrinogen distribution through a change in the lower percentiles of the IFN-γ methylation distribution.

Investigating causal relation between prenatal arsenic exposure and birthweight: Are smaller infants more susceptible?

BACKGROUND

Shortening of gestation and intrauterine growth restriction (IUGR) are the two main determinants of birthweight. Low birthweight has been linked with prenatal arsenic exposure, but the causal relation between arsenic and birthweight is not well understood.

OBJECTIVES

We applied a quantile causal mediation analysis approach to determine the association between prenatal arsenic exposure and birthweight in relation to shortening of gestation and IUGR, and whether the susceptibility of arsenic exposure varies by infant birth sizes.

METHODS

In a longitudinal birth cohort in Bangladesh, we measured arsenic in drinking water (n=1182) collected at enrollment and maternal toenails (n=1104) collected ≤1-month postpartum using inductively coupled plasma mass spectrometry. Gestational age was determined using ultrasound at ≤16weeks' gestation. Demographic information was collected using a structured questionnaire.

RESULTS

Of 1184 singleton livebirths, 16.4% (n=194) were low birthweight (<2500g), 21.9% (n=259) preterm (<37weeks' gestation), and 9.2% (n=109) both low birthweight and preterm. The median concentrations of arsenic in drinking water and maternal toenails were 2.2μg/L (range: below the level of detection [LOD]-1400) and 1.2μg/g (range: <LOD-46.6), respectively. Prenatal arsenic exposure was negatively associated with birthweight, where the magnitude of the association varied across birthweight percentiles. The effect of arsenic on birthweight mediated via shortening of gestation affected all infants irrespective of birth sizes (β range: 10th percentile=-19.7g [95% CI: -26.7, -13.3] to 90th percentile=-10.9g [95% CI: -18.5, -5.9] per natural log water arsenic increase), whereas the effect via pathways independent of gestational age affected only the smaller infants (β range: 10th percentile=-28.0g [95% CI: -43.8, -9.9] to 20th percentile=-14.9g [95% CI: -30.3, -1.7] per natural log water arsenic increase). Similar pattern was observed for maternal toenail arsenic.

CONCLUSIONS

The susceptibility of prenatal arsenic exposure varied by infant birth sizes, placing smaller infants at greater risk of lower birthweight by shortening of gestation and possibly growth restriction. It is important to mitigate prenatal arsenic exposure to improve perinatal outcomes in Bangladesh.