Effect of breastfeeding on gastrointestinal infection in infants: A targeted maximum likelihood approach for clustered longitudinal data

TL;DR

This study applies targeted maximum likelihood estimation (TMLE) to assess how breastfeeding duration causally affects gastrointestinal infections in infants, accounting for complex confounding and clustering in a cluster-randomized trial.

Contribution

It demonstrates the use of TMLE with data-adaptive algorithms to estimate causal effects in clustered longitudinal data, advancing methods for breastfeeding and infection research.

Findings

TMLE effectively estimates causal effects in clustered data.

Data-adaptive TMLE improves estimation accuracy.

Controlling for clustering enhances causal inference.

Abstract

The PROmotion of Breastfeeding Intervention Trial (PROBIT) cluster-randomized a program encouraging breastfeeding to new mothers in hospital centers. The original studies indicated that this intervention successfully increased duration of breastfeeding and lowered rates of gastrointestinal tract infections in newborns. Additional scientific and popular interest lies in determining the causal effect of longer breastfeeding on gastrointestinal infection. In this study, we estimate the expected infection count under various lengths of breastfeeding in order to estimate the effect of breastfeeding duration on infection. Due to the presence of baseline and time-dependent confounding, specialized "causal" estimation methods are required. We demonstrate the double-robust method of Targeted Maximum Likelihood Estimation (TMLE) in the context of this application and review some related methods and the adjustments required to account for clustering. We compare TMLE (implemented both parametrically and using a data-adaptive algorithm) to other causal methods for this example. In addition, we conduct a simulation study to determine (1) the effectiveness of controlling for clustering indicators when cluster-specific confounders are unmeasured and (2) the importance of using data-adaptive TMLE.