Statistical Methods for Causal Mediation Analysis

Mediation analysis is a popular approach in the social an biomedical sciences to examine the extent to which the effect of an exposure on an outcome is through an intermediate variable (mediator) and the extent to which the effect is direct. We first develop statistical methods and software for the estimation of direct and indirect causal effects in generalized linear models when exposure-mediator interaction may be present. We then study the bias of direct and indirect effects estimators that arise in this context when a continuous mediator is measured with error or a binary mediator is misclassified. We develop methods of correction for measurement error and misclassification coupled with sensitivity analyses for which no auxiliary information on the mediator measured with error is needed. The proposed methods are applied to a lung cancer study to evaluate the effect of genetic variants mediated through smoking on lung cancer risk and to a perinatal epidemiological study on the determinants of preterm birth

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Mother Goose is Alive and Culturally Relevant; Predictable Books in a Middle School Class Writing Program; Computers and the Developmental Learne

Data-Adaptive Estimation for Double-Robust Methods in Population-Based Cancer Epidemiology: Risk Differences for Lung Cancer Mortality by Emergency Presentation.

In this paper, we propose a structural framework for population-based cancer epidemiology and evaluate the performance of double-robust estimators for a binary exposure in cancer mortality. We conduct numerical analyses to study the bias and efficiency of these estimators. Furthermore, we compare 2 different model selection strategies based on 1) Akaike's Information Criterion and the Bayesian Information Criterion and 2) machine learning algorithms, and we illustrate double-robust estimators' performance in a real-world setting. In simulations with correctly specified models and near-positivity violations, all but the naive estimators had relatively good performance. However, the augmented inverse-probability-of-treatment weighting estimator showed the largest relative bias. Under dual model misspecification and near-positivity violations, all double-robust estimators were biased. Nevertheless, the targeted maximum likelihood estimator showed the best bias-variance trade-off, more precise estimates, and appropriate 95% confidence interval coverage, supporting the use of the data-adaptive model selection strategies based on machine learning algorithms. We applied these methods to estimate adjusted 1-year mortality risk differences in 183,426 lung cancer patients diagnosed after admittance to an emergency department versus persons with a nonemergency cancer diagnosis in England (2006-2013). The adjusted mortality risk (for patients diagnosed with lung cancer after admittance to an emergency department) was 16% higher in men and 18% higher in women, suggesting the importance of interventions targeting early detection of lung cancer signs and symptoms

Comparison of methods for analyzing environmental mixtures effects on survival outcomes and application to a population-based cohort study

The estimation of the effect of environmental exposures and overall mixtures on a survival time outcome is common in environmental epidemiological studies. While advanced statistical methods are increasingly being used for mixture analyses, their applicability and performance for survival outcomes has yet to be explored. We identified readily available methods for analyzing an environmental mixture's effect on a survival outcome and assessed their performance via simulations replicating various real-life scenarios. Using prespecified criteria, we selected Bayesian Additive Regression Trees (BART), Cox Elastic Net, Cox Proportional Hazards (PH) with and without penalized splines, Gaussian Process Regression (GPR) and Multivariate Adaptive Regression Splines (MARS) to compare the bias and efficiency produced when estimating individual exposure, overall mixture, and interaction effects on a survival outcome. We illustrate the selected methods in a real-world data application. We estimated the effects of arsenic, cadmium, molybdenum, selenium, tungsten, and zinc on incidence of cardiovascular disease in American Indians using data from the Strong Heart Study (SHS). In the simulation study, there was a consistent bias-variance trade off. The more flexible models (BART, GPR and MARS) were found to be most advantageous in the presence of nonproportional hazards, where the Cox models often did not capture the true effects due to their higher bias and lower variance. In the SHS, estimates of the effect of selenium and the overall mixture indicated negative effects, but the magnitudes of the estimated effects varied across methods. In practice, we recommend evaluating if findings are consistent across methods

Contribution of smoking towards the association between socioeconomic position and dementia : 32-year follow-up of the Whitehall II prospective cohort study

Background There is consistent evidence of social inequalities in dementia but the mechanisms underlying this association remain unclear. We examined the role of smoking in midlife in socioeconomic differences in dementia at older ages.Methods Analyses were based on 9951 (67% men) participants, median age 44.3 [IQR=39.6, 50.3] years at baseline in 1985-1988, from the Whitehall II cohort study. Socioeconomic position (SEP) and smoking (smoking status (cur-rent, ex-, never-smoker), pack years of smoking, and smoking history score (combining status and pack-years)) were measured at baseline. Counterfactual mediation analysis was used to examine the contribution of smoking to the association between SEP and dementia.Findings During a median follow-up of 31.6 (IQR 31.1, 32.6) years, 628 participants were diagnosed with dementia and 2110 died. Analyses adjusted for age, sex, ethnicity, education, and SEP showed smokers (hazard ratio [HR] 1.36 [95% CI 1.10-1.68]) but not ex-smokers (HR 0.95 [95% CI 0.79-1.14]) to have a higher risk of dementia compared to never-smokers; similar results for smoking were obtained for pack-years of smoking and smoking history score. Mediation analysis showed low SEP to be associated with higher risk of dementia (HRs between 1.97 and 2.02, depending on the measure of smoking in the model); estimate for the mediation effect was 16% for smoking status (Indirect Effect HR 1.09 [95% CI 1.03-1.15]), 7% for pack-years of smoking (Indirect Effect HR 1.03 [95% CI 1.01 -1.06]) and 11% for smoking history score (Indirect Effect HR 1.06 [95% CI 1.02-1.10]). Interpretation Our findings suggest that part of the social inequalities in dementia is mediated by smoking.Funding NIHCopyright (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) The Health 2022;23: Published https://doi.org/10.1016/j. lanepe.2022.100516Peer reviewe

DNA Hypomethylation, Ambient Particulate Matter, and Increased Blood Pressure: Findings From Controlled Human Exposure Experiments

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/144592/1/jah3232.pd

Neurodevelopmental outcomes among 2- to 3-year-old children in Bangladesh with elevated blood lead and exposure to arsenic and manganese in drinking water

Background: The people of Bangladesh are currently exposed to high concentrations of arsenic and manganese in drinking water, as well as elevated lead in many regions. The objective of this study was to investigate associations between environmental exposure to these contaminants and neurodevelopmental outcomes among Bangladeshi children. Methods: We evaluated data from 524 children, members of an ongoing prospective birth cohort established to study the effects of prenatal and early childhood arsenic exposure in the Sirajdikhan and Pabna Districts of Bangladesh. Water was collected from the family’s primary drinking source during the first trimester of pregnancy and at ages 1, 12 and 20–40 months. At age 20–40 months, blood lead was measured and neurodevelopmental outcomes were assessed using a translated, culturally-adapted version of the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III). Results: Median blood lead concentrations were higher in Sirajdikhan than Pabna (7.6 vs. <LODμg/dL, p <0.0001) and water arsenic concentrations were lower (1.5 vs 25.7 μg/L, p <0.0001). Increased blood lead was associated with decreased cognitive scores in Sirajdikhan (β = −0.17, SE = 0.09, p = 0.05), whereas increased water arsenic was associated with decreased cognitive scores in Pabna (β = −0.06, SE = 0.03, p = 0.05). Water manganese was associated with fine motor scores in an inverse-U relationship in Pabna. Conclusion: Where blood lead levels are high, lead is associated with decreased cognitive scores on the BSID-III, and effects of other metals are not detected. In the setting of lower lead levels, the adverse effects of arsenic and manganese on neurodevelopment are observed. Electronic supplementary material The online version of this article (doi:10.1186/s12940-016-0127-y) contains supplementary material, which is available to authorized users

Stunting and lead: using causal mediation analysis to better understand how environmental lead exposure affects cognitive outcomes in children

Background Many children in Bangladesh experience poor nutritional status and environmental lead exposure, both of which are associated with lower scores on neurodevelopmental assessments. Recent studies have suggested that part of lead’s adverse effects on neurodevelopment are caused in part by lead’s effect on growth. New statistical methods are now available to evaluate potential causal pathways in observational studies. This study used a novel statistical method to test the hypothesis that stunting, a measure of linear growth related to poor nutrition, is a mediator and/or an effect modifier of the lead exposure’s adverse effect on cognitive development. Methods Participants were 734 children from a longitudinal birth cohort established in rural Bangladesh to study the health effects of prenatal and early childhood environmental metal exposures. Lead exposure was estimated using umbilical cord blood samples obtained at birth and blood obtained via venipuncture at age 20–40 months. Stunting was determined using the World Health Organization’s standards. Neurodevelopment was assessed at age 20–40 months years using the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III). We evaluated the effect of lead on stunting and whether the effect of lead on cognitive scores is modified by stunting status in multivariable regression analyses. We then conducted a novel 4-way mediation analysis that allows for exposure-mediator interaction to assess how much of the effect of lead on cognitive scores is explained by the pathway through stunting (mediation) and how much is explained by the interaction between lead and stunt (effect modification). Results Stunting was not a mediator of the effect of lead in our analyses. Results suggested effect modification by stunting. In an area of Bangladesh with lower lead exposures (median umbilical cord blood lead concentration, 1.7 μg/dL), stunting modified the relationship between prenatal blood lead concentrations and cognitive score at age 2–3 years. A 1-unit increase in natural log cord blood lead concentration in the presence of stunting was associated with a 2.1-unit decrease in cognitive scores (β = − 2.10, SE = 0.71, P = 0.003). This interaction was not found in a second study site where lead exposures were higher (median umbilical cord blood lead concentration, 6.1 μg/dL, β = − 0.45, SE = 0.49, P = 0.360). Conclusions We used a novel method of mediation analysis to test whether stunting mediated the adverse effect of prenatal lead exposure on cognitive outcomes in Bangladesh. While we did not find that stunting acted as mediator of lead’s effect on cognitive development, we found significant effect modification by stunting. Our results suggest that children with stunting are more vulnerable to the adverse effects of low-level lead exposure