In developing countries, higher infant mortality is partially caused by poor maternal and fetal nutrition. Clinical 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 may need to 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 their 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 (e.g. is largest among the LBW), and whether the effect of the treatment on survival is mediated wholly or in part by increases in birth weight.

Motivated by a community trial in Nepal, this paper defines population and causal parameters for estimating 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 in sub-populations of babies defined by their birth weights. We use 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.

Our analysis shows that the average causal effect of the treatment on birth weight is equal to 68 grams (95% posterior regions 25 to 110) and that this causal effect is largest among the LBW. Posterior inferences about average causal effects of the treatment on birth weight are robust to modelling assumptions. However inferences about causal effects for babies at the tails of the birth weight distribution can be highly sensitive to the unverifiable assumption about the correlation between the observed and the counterfactuals birth weights. Among the LBW infants who have a large causal effect of the treatment on birth weight, we found that a baby receiving the treatment has 5% to 7% less chance of death if the same baby had received the control. Among the LBW,we found weak evidence supporting an additional beneficial effect of the treatment on mortality independent of birth weight.


Clinical Trials | Epidemiology | Vital and Health Statistics