ArticleAnnals of epidemiology2023
Does heterogeneity underlie differences in treatment effects estimated from SuperLearner versus logistic regression? An application in nutritional epidemiology.
Article in Annals of epidemiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- From single conventional regression to ensemble modelling: relative importance of the Healthy Eating Index-2015 components in relation to adverse pregnancy outcomes.The British journal of nutrition · 2026Article
- Algorithm Selection for Estimating Causal Effects: Nulliparous Pregnancy Outcomes Study: Monitoring Mothers to Be.Epidemiology (Cambridge, Mass.) · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
purposeA strength of SuperLearner is that it may accommodate key interactions between model variables without a priori specification. In prior research, protective associations between fruit intake and preeclampsia were stronger when estimated using SuperLearner with targeted maximum likelihood estimation (TMLE) compared with multivariable logistic regression without any interaction terms. We explored whether heterogeneity (i.e., differences in the effect estimate due to interactions between fruit intake and covariates) may partly explain differences in estimates from these two models.
methodsUsing a U.S. prospective pregnancy cohort (2010-2013, n = 7781), we estimated preeclampsia risk differences (RDs) for higher versus lower fruit density using multivariable logistic regression and included two-way statistical interactions between fruit density and each of the 25 model covariates. We compared the RDs with those from SuperLearner with TMLE (gold standard) and logistic regression with no interaction.
resultsFrom the logistic regression models with two-way statistical interactions, 48% of the preeclampsia RDs were ≤-0.02 (closer to SuperLearner with TMLE estimate); 40% equaled -0.01 (same as logistic regression with no interaction estimate); the minority of RDs were at or crossed the null.
conclusionsOur exploratory analysis provided preliminary evidence that heterogeneity may partly explain differences in estimates from logistic regression versus SuperLearner with TMLE.
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