RESUMO
While benchmark dose (BMD) methodology is well-established for settings with a single exposure, these methods cannot easily handle multidimensional exposures with nonlinear effects. We propose a framework for BMD analysis to characterize the joint effect of a two-dimensional exposure on a continuous outcome using a generalized additive model while adjusting for potential confounders via propensity scores. This leads to a dose-response surface which can be summarized in two dimensions by a contour plot in which combinations of exposures leading to the same expected effect are identified. In our motivating study of prenatal alcohol exposure, cognitive deficits in children are found to be associated with both the frequency of drinking as well as the amount of alcohol consumed on each drinking day during pregnancy. The general methodological framework is useful for a broad range of settings, including combinations of environmental stressors, such as chemical mixtures, and in explorations of the impact of dose rate rather than simply cumulative exposure on adverse outcomes.
RESUMO
BACKGROUND: Most studies of the effects of prenatal alcohol exposure (PAE) on cognitive function have assumed that the dose-response curve is linear. However, data from a few animal and human studies suggest that there may be an inflection point in the dose-response curve above which PAE effects are markedly stronger and that there may be differences associated with pattern of exposure, assessed in terms of alcohol dose per drinking occasion and drinking frequency. METHODS: We performed second-order confirmatory factor analysis on data obtained at school age, adolescence, and early adulthood from 2227 participants in six US longitudinal cohorts to derive a composite measure of cognitive function. Regression models were constructed to examine effects of PAE on cognitive function, adjusted for propensity scores. Analyses based on a single predictor (absolute alcohol (AA)/day) were compared with analyses based on two predictors (dose/occasion and drinking frequency), using (1) linear models and (2) nonparametric general additive models (GAM) that allow for both linear and nonlinear effects. RESULTS: The single-predictor GAM model showed virtually no nonlinearity in the effect of AA/day on cognitive function. However, the two-predictor GAM model revealed differential effects of maternal drinking pattern. Among offspring of infrequent drinkers, PAE effects on cognitive function were markedly stronger in those whose mothers drank more than ~3 drinks/occasion, and the effect of dose/occasion was strongest among the very frequent drinkers. Frequency of drinking did not appear to alter the PAE effect on cognitive function among participants born to mothers who limited their drinking to ~1 drink/occasion or less. CONCLUSIONS: These findings suggest that linear models based on total AA/day are appropriate for assessing whether PAE affects a given cognitive outcome. However, examination of alcohol dose/occasion and drinking frequency is needed to fully characterize the impact of different levels of alcohol intake on cognitive impairment.
RESUMO
Individual participant data meta-analysis is a commonly used alternative to the traditional aggregate data meta-analysis. It is popular because it avoids relying on published results and enables direct adjustment for relevant covariates. However, a practical challenge is that the studies being combined often vary in terms of the potential confounders that were measured. Furthermore, it will inevitably be the case that some individuals have missing values for some of those covariates. In this paper, we demonstrate how these challenges can be resolved using a propensity score approach, combined with multiple imputation, as a strategy to adjust for covariates in the context of individual participant data meta-analysis. To illustrate, we analyze data from the Bill and Melinda Gates Foundation-funded Healthy Birth, Growth, and Development Knowledge Integration project to investigate the relationship between physical growth rate in the first year of life and cognition measured later during childhood. We found that the overall effect of average growth velocity on cognitive outcome is slightly, but significantly, positive with an estimated effect size of 0.36 (95% CI 0.18, 0.55).