ArticleJournal of the Royal Statistical Society. Series C, Applied statistics2025
Synergistic self-learning approach to establishing individualized treatment rules from multiple benefit outcomes in a calcium supplementation trial.
Article in Journal of the Royal Statistical Society. Series C, Applied statistics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
In utero lead exposure poses risks to children's neurobehavioral development. The Early Life Exposure in Mexico to ENvironmental Toxicants' calcium supplementation trial studies the effect of calcium supplement in reducing maternal lead exposure to infants during pregnancy. An individualized treatment rule (ITR) is needed to guide pregnant women on taking calcium supplement. This article introduces a statistical learning method, synergistic self-learning (SS-learning), to tackle two challenges in deriving ITR with multiple outcomes, including heterogeneous multidimensional outcomes and complex missing data patterns. Applying SS-learning to the trial, important covariates were identified to form an ITR, expected to lead to higher lead reduction if implemented across the study population.
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