ArticleJournal of applied statistics2019
Propensity score prediction for electronic healthcare databases using Super Learner and High-dimensional Propensity Score Methods.
Article in Journal of applied statistics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Self-Expandable Metal Stent as a Bridge to Surgery for Left-Sided Acute Malignant Colorectal Obstruction: Optimal Timing for Elective Surgery.Computational and mathematical methods in medicine · 2022Trial
- Outcome adaptive propensity score methods for handling censoring and high-dimensionality: Application to insurance claims.Statistical methods in medical research · 2025Article
- Ensemble-learning approach improves fracture prediction using genomic and phenotypic data.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2025Article
- How Effective Are Machine Learning and Doubly Robust Estimators in Incorporating High-Dimensional Proxies to Reduce Residual Confounding?Pharmacoepidemiology and drug safety · 2025Article
- Article
- Developing predictive precision medicine models by exploiting real-world data using machine learning methods.Journal of applied statistics · 2024Article
- Association of pulmonary artery catheter with in-hospital outcomes after cardiac surgery in the United States: National Inpatient Sample 1999-2019.Scientific reports · 2023Article
- Propensity score analysis with missing data using a multi-task neural network.BMC medical research methodology · 2023Article
- A two-stage super learner for healthcare expenditures.Health services & outcomes research methodology · 2022Article
- Machine learning for improving high-dimensional proxy confounder adjustment in healthcare database studies: An overview of the current literature.Pharmacoepidemiology and drug safety · 2022Review
- Synthetic Negative Controls: Using Simulation to Screen Large-scale Propensity Score Analyses.Epidemiology (Cambridge, Mass.) · 2022Article
- A Tree-based Model Averaging Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources.Proceedings of machine learning research · 2022Article
- Applying Machine Learning in Distributed Data Networks for Pharmacoepidemiologic and Pharmacovigilance Studies: Opportunities, Challenges, and Considerations.Drug safety · 2022Review
- A comparison of confounder selection and adjustment methods for estimating causal effects using large healthcare databases.Pharmacoepidemiology and drug safety · 2022Article
- Deep Ensemble Machine Learning Framework for the Estimation ofEnvironmental health perspectives · 2022Article
- Hospitalization outcomes among brain metastasis patients receiving radiation therapy with or without stereotactic radiosurgery from the 2005-2014 Nationwide Inpatient Sample.Scientific reports · 2021Article
- A machine learning compatible method for ordinal propensity score stratification and matching.Statistics in medicine · 2021Article
- A novel approach for propensity score matching and stratification for multiple treatments: Application to an electronic health record-derived study.Statistics in medicine · 2020Article
- Identifying Drugs Inducing Prematurity by Mining Claims Data with High-Dimensional Confounder Score Strategies.Drug safety · 2020Article
- Interim monitoring of nonrandomized prospective studies that invoke propensity scoring for decision making.The journal of trauma and acute care surgery · 2020Article
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Authors and funding
7 authors.
Funding
Abstract
The optimal learner for prediction modeling varies depending on the underlying data-generating distribution. Super Learner (SL) is a generic ensemble learning algorithm that uses cross-validation to select among a "library" of candidate prediction models. While SL has been widely studied in a number of settings, it has not been thoroughly evaluated in large electronic healthcare databases that are common in pharmacoepidemiology and comparative effectiveness research. In this study, we applied and evaluated the performance of SL in its ability to predict the propensity score (PS), the conditional probability of treatment assignment given baseline covariates, using three electronic healthcare databases. We considered a library of algorithms that consisted of both nonparametric and parametric models. We also proposed a novel strategy for prediction modeling that combines SL with the high-dimensional propensity score (hdPS) variable selection algorithm. Predictive performance was assessed using three metrics: the negative log-likelihood, area under the curve (AUC), and time complexity. Results showed that the best individual algorithm, in terms of predictive performance, varied across datasets. The SL was able to adapt to the given dataset and optimize predictive performance relative to any individual learner. Combining the SL with the hdPS was the most consistent prediction method and may be promising for PS estimation and prediction modeling in electronic healthcare databases.
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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.