ArticleScientific reports2016
Optimizing matching and analysis combinations for estimating causal effects.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed.
- Resection of recurrent glioblastoma multiforme in elderly patients: a pseudo-randomized analysis revealed clinical benefit.Journal of neuro-oncology · 2020Trial
- Hospital Readmission After Traumatic Brain Injury Hospitalization in Community-Dwelling Older Adults.Annals of clinical and translational neurology · 2026Article
- Universal same-day discharge after colonic resection: a matched cohort study.Updates in surgery · 2025Article
- Anxiety symptoms during pregnancy and risk of adverse birth outcomes in Gondar Town Ethiopia.Scientific reports · 2025Article
- Automated generation of comparator patients in the electronic medical record.Learning health systems · 2024Article
- The effect of carbon fertilization on naturally regenerated and planted US forests.Nature communications · 2022Article
- Frequency and Consequences of Cervical Lymph Node Overstaging in Head and Neck Carcinoma.Diagnostics (Basel, Switzerland) · 2022Article
- Matching Methods for Confounder Adjustment: An Addition to the Epidemiologist's Toolbox.Epidemiologic reviews · 2022Review
- Recurrent glioblastoma in elderly: Options and decision for the treatment.Surgical neurology international · 2022Review
- Heterogeneous treatment effect analysis based on machine-learning methodology.CPT: pharmacometrics & systems pharmacology · 2021Article
- Effect of perinatal depression on risk of adverse infant health outcomes in mother-infant dyads in Gondar town: a causal analysis.BMC pregnancy and childbirth · 2021Article
- G-computation and machine learning for estimating the causal effects of binary exposure statuses on binary outcomes.Scientific reports · 2021Article
- Redlines and Greenspace: The Relationship between Historical Redlining and 2010 Greenspace across the United States.Environmental health perspectives · 2021Article
- G-computation, propensity score-based methods, and targeted maximum likelihood estimator for causal inference with different covariates sets: a comparative simulation study.Scientific reports · 2020Article
- Evaluation of opioid discontinuation after non-orthopaedic surgery among chronic opioid users: a population-based cohort study.British journal of anaesthesia · 2020Article
- Environmental noise and sleep and mental health outcomes in a nationally representative sample of urban US adolescents.Environmental epidemiology (Philadelphia, Pa.) · 2019Article
- Performance of Matching Methods as Compared With Unmatched Ordinary Least Squares Regression Under Constant Effects.American journal of epidemiology · 2019Article
- An educational intervention to improve knowledge about prevention against occupational asthma and allergies using targeted maximum likelihood estimation.International archives of occupational and environmental health · 2019Article
- Evaluation of Complementary and Integrative Health Approaches Among US Veterans with Musculoskeletal Pain Using Propensity Score Methods.Pain medicine (Malden, Mass.) · 2019Article
- Performing an Informatics Consult: Methods and Challenges.Journal of the American College of Radiology : JACR · 2018Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Matching methods are common in studies across many disciplines. However, there is limited evidence on how to optimally combine matching with subsequent analysis approaches to minimize bias and maximize efficiency for the quantity of interest. We conducted simulations to compare the performance of a wide variety of matching methods and analysis approaches in terms of bias, variance, and mean squared error (MSE). We then compared these approaches in an applied example of an employment training program. The results indicate that combining full matching with double robust analysis performed best in both the simulations and the applied example, particularly when combined with machine learning estimation methods. To reduce bias, current guidelines advise researchers to select the technique with the best post-matching covariate balance, but this work finds that such an approach does not always minimize mean squared error (MSE). These findings have important implications for future research utilizing matching. To minimize MSE, investigators should consider additional diagnostics, and use of simulations tailored to the study of interest to identify the optimal matching and analysis combination.
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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.