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Schnitzer ME, van der Laan MJ, Moodie EE, et al. EFFECT OF BREASTFEEDING ON GASTROINTESTINAL INFECTION IN INFANTS: A TARGETED MAXIMUM LIKELIHOOD APPROACH FOR CLUSTERED LONGITUDINAL DATA. Ann Appl Stat. 2014;8(2):703-725doi: 10.1214/14-aoas727.
Schnitzer, M. E., van der Laan, M. J., Moodie, E. E., & Platt, R. W. (2014). EFFECT OF BREASTFEEDING ON GASTROINTESTINAL INFECTION IN INFANTS: A TARGETED MAXIMUM LIKELIHOOD APPROACH FOR CLUSTERED LONGITUDINAL DATA. The annals of applied statistics, 8(2), 703-725. https://doi.org/10.1214/14-aoas727
Schnitzer, Mireille E, et al. "EFFECT OF BREASTFEEDING ON GASTROINTESTINAL INFECTION IN INFANTS: A TARGETED MAXIMUM LIKELIHOOD APPROACH FOR CLUSTERED LONGITUDINAL DATA." The annals of applied statistics vol. 8,2 (2014): 703-725. doi: https://doi.org/10.1214/14-aoas727
Schnitzer ME, van der Laan MJ, Moodie EE, Platt RW. EFFECT OF BREASTFEEDING ON GASTROINTESTINAL INFECTION IN INFANTS: A TARGETED MAXIMUM LIKELIHOOD APPROACH FOR CLUSTERED LONGITUDINAL DATA. Ann Appl Stat. 2014 Jun;8(2):703-725. doi: 10.1214/14-aoas727. PMID: 25505499; PMCID: PMC4259272.
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Ann Appl Stat. 2014 Jun;8(2):703-725. doi: 10.1214/14-aoas727.

EFFECT OF BREASTFEEDING ON GASTROINTESTINAL INFECTION IN INFANTS: A TARGETED MAXIMUM LIKELIHOOD APPROACH FOR CLUSTERED LONGITUDINAL DATA.

The annals of applied statistics

Mireille E Schnitzer, Mark J van der Laan, Erica E M Moodie, Robert W Platt

Affiliations

  1. Université de Montréal.
  2. University of California, Berkeley.
  3. McGill University.

PMID: 25505499 PMCID: PMC4259272 DOI: 10.1214/14-aoas727

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.

Keywords: Causal inference; G-computation; inverse probability weighting; marginal effects; missing data; pediatrics

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