In developing countries, higher infant mortality is partially caused by poor maternal and fetal nutrition. Randomized community trials of micronutrient supplementation are aimed at reducing the risk of infant mortality by increasing birth weight. Because infant mortality is greatest among the low birth weight infants (LBW) (<2500 grams), an effective intervention must increase the birth weight among the smallest babies.
Although it has been demonstrated that supplementation increases the birth weight in a trial conducted in Nepal, there is inconclusive evidence that the supplementation improves survival. It has been hypothesized that a potential benefit of the treatment on survival among the LBW is partly compensated by a null or even harmful effects among the largest infants. Thus, two key scientific questions are whether the effect of the treatment on survival differs across the birth weight distribution and whether the effect of the treatment on survival is mediated wholly or in part by increases in birthweight. This talk will define and estimate population and causal parameters that describe the treatment effects on birth weight and on survival as functions of the percentiles of the birth weight distribution. We develop a model with potential outcomes and implement principal stratification for estimating and comparing the causal effects of the treatment on mortality. We implement a Bayesian approach with data augmentation to approximate the posterior distributions of the parameters taking into account uncertainty associated with the imputation of the counterfactuals. This approach is particularly suitable for exploring the sensitivity of the results to modelling assumptions and other prior beliefs.
Work with : Francesca Dominici, Giovanni Parmigiani, Joanne Katz and Christian Parul