ArticleAmerican journal of epidemiology2024
Targeted learning with an undersmoothed LASSO propensity score model for large-scale covariate adjustment in health-care database studies.
Article in American journal of epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 9 citations in OpenAlex.
- Lactylation-based machine algorithm combined with multi-omics analysis to predict prognosis in cervical cancer.Oncology letters · 2026Article
- Tuning LASSO Models for Propensity Score Weighting and Using Synthetic Negative Control Exposures for Residual Bias Detection.Statistics in medicine · 2026Article
- Methodological and regulatory considerations for causal AI in drug development.NPJ digital medicine · 2026Review
- A machine-learning-assisted logistic regression model for predicting post-operative delirium in older adults undergoing total knee arthroplasty.Frontiers in neurology · 2026Article
- Risk factors for progression to bronchitis or pneumonia in hospitalized children with human rhinovirus infection: a retrospective cohort study using machine learning.Frontiers in pediatrics · 2026Article
- Article
- Note on targeted learning with an undersmoothed Lasso propensity score model for large-scale covariate adjustment in health care database studies.American journal of epidemiology · 2025Article
- Association between volatile organic compound co-exposure and the prevalence of rheumatoid arthritis: a nationwide cross-sectional study.Frontiers in public health · 2025Article
- Commentary on ``Nonparametric identification is not enough, but randomized controlled trials are'': Statistical considerations for generating reliable evidence across a spectrum of studies that increasingly involve real-world elements.Observational studies · 2025Article
- Utilizing Machine Learning and causal graph approaches to Address Confounding Factors in Health Science Research: A Scoping Review.F1000Research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 8 institutions in 1 country.
Funding
Abstract
Least absolute shrinkage and selection operator (LASSO) regression is widely used for large-scale propensity score (PS) estimation in health-care database studies. In these settings, previous work has shown that undersmoothing (overfitting) LASSO PS models can improve confounding control, but it can also cause problems of nonoverlap in covariate distributions. It remains unclear how to select the degree of undersmoothing when fitting large-scale LASSO PS models to improve confounding control while avoiding issues that can result from reduced covariate overlap. Here, we used simulations to evaluate the performance of using collaborative-controlled targeted learning to data-adaptively select the degree of undersmoothing when fitting large-scale PS models within both singly and doubly robust frameworks to reduce bias in causal estimators. Simulations showed that collaborative learning can data-adaptively select the degree of undersmoothing to reduce bias in estimated treatment effects. Results further showed that when fitting undersmoothed LASSO PS models, the use of cross-fitting was important for avoiding nonoverlap in covariate distributions and reducing bias in causal estimates.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.