Using Nested Data to Enhance Causal Inference

There has been an ongoing controversy about whether a mother’s use of antidepressants during pregnancy results in elevated rates of autism in their children. Although much research has focused on this question, it has been limited by the omission of potential confounding variables and the study of just one child per mother. A recent study published in the Journal of the American Medical Association and widely distributed in a CBS News report presents research findings that considers both extensive potential confounders and multiple siblings born to the same mother. First, the study used 500 covariates to estimate propensity scores to compare child autism for mother’s who did and did not use antidepressants during pregnancy. Results indicated that, after balancing on all 500 confounders, there were no differences in autism between the two groups. Although insightful, this method used advanced statistical controls to examine just one child per family. To enhance causal inference, the study then considered mothers with at least two children, one of whom was exposed to antidepressants in utero and one who was not. The use of multiple births nested within mother allowed for an explicit test of within-mother effects of antidepressant exposure while controlling for a host of mother-specific characteristics (e.g., genetic risk) to better isolate the effects of antidepressant exposure while holding mother constant. Again, no differences in autism rates were found. The results of the nested data models may be much more convincing because control of between-mother effects was achieved through the hierarchical design of the study rather than by invoking statistical controls applied to just one child per mother. This is an excellent example of using nested data structures and advanced statistical models to make stronger inferences about group differences.