ArticleStatistical methods in medical research2016
Evaluating treatment effectiveness under model misspecification: A comparison of targeted maximum likelihood estimation with bias-corrected matching.
Article in Statistical methods in medical research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Maternal Pre-Pregnancy Glycemic Status and Developmental Outcomes in Korean Children Aged 18-36 Months: A Nationwide Population-Based Cohort Study (2014-2021).Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Article
- Estimating average causal effects with incomplete exposure and confounders.Journal of causal inference · 2026Article
- Evaluation of Methods Adjusting for Unmeasured Confounding Using Large Healthcare Databases: An Empirical Study Concerning Drugs Inducing Prematurity.Clinical and translational science · 2025Article
- Using Machine Learning to Improve Control for Confounding in the Dynamic Weighted Ordinary Least Squares Estimator of Optimal Adaptive Treatment Strategies.Biometrical journal. Biometrische Zeitschrift · 2025Article
- Guidelines and Best Practices for the Use of Targeted Maximum Likelihood and Machine Learning When Estimating Causal Effects of Exposures on Time-To-Event Outcomes.Statistics in medicine · 2025Article
- Matching plus regression adjustment for the estimation of the average treatment effect on survival outcomes: a case study with mosunetuzumab in relapsed/refractory follicular lymphoma.BMC medical research methodology · 2025Article
- Artificial intelligence and machine learning as a viable solution for hip implant failure diagnosis-Review of literature and in vitro case study.Medical & biological engineering & computing · 2023Review
- Assessment of Glucose Lowering Medications' Effectiveness for Cardiovascular Clinical Risk Management of Real-World Patients with Type 2 Diabetes: Targeted Maximum Likelihood Estimation under Model Misspecification and Missing Outcomes.International journal of environmental research and public health · 2022Article
- Transfering Targeted Maximum Likelihood Estimation for Causal Inference into Sports Science.Entropy (Basel, Switzerland) · 2022Article
- Evaluating the robustness of targeted maximum likelihood estimators via realistic simulations in nutrition intervention trials.Statistics in medicine · 2022Article
- Confounding adjustment methods for multi-level treatment comparisons under lack of positivity and unknown model specification.Journal of applied statistics · 2022Article
- Simultaneous modeling of detection rate and exposure concentration using semi-continuous models to identify exposure determinants when left-censored data may be a true zero.Journal of exposure science & environmental epidemiology · 2021Article
- Intersections of machine learning and epidemiological methods for health services research.International journal of epidemiology · 2021Article
- Quantifying the bias due to observed individual confounders in causal treatment effect estimates.Statistics in medicine · 2020Article
- Targeted maximum likelihood estimation for a binary treatment: A tutorial.Statistics in medicine · 2018Article
- Data-Adaptive Estimation for Double-Robust Methods in Population-Based Cancer Epidemiology: Risk Differences for Lung Cancer Mortality by Emergency Presentation.American journal of epidemiology · 2018Article
- Machine Learning in Orthopedics: A Literature Review.Frontiers in bioengineering and biotechnology · 2018Review
- Reorganising specialist cancer surgery for the twenty-first century: a mixed methods evaluation (RESPECT-21).Implementation science : IS · 2016Article
- Optimizing matching and analysis combinations for estimating causal effects.Scientific reports · 2016Article
- Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury.Health economics · 2015Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Statistical approaches for estimating treatment effectiveness commonly model the endpoint, or the propensity score, using parametric regressions such as generalised linear models. Misspecification of these models can lead to biased parameter estimates. We compare two approaches that combine the propensity score and the endpoint regression, and can make weaker modelling assumptions, by using machine learning approaches to estimate the regression function and the propensity score. Targeted maximum likelihood estimation is a double-robust method designed to reduce bias in the estimate of the parameter of interest. Bias-corrected matching reduces bias due to covariate imbalance between matched pairs by using regression predictions. We illustrate the methods in an evaluation of different types of hip prosthesis on the health-related quality of life of patients with osteoarthritis. We undertake a simulation study, grounded in the case study, to compare the relative bias, efficiency and confidence interval coverage of the methods. We consider data generating processes with non-linear functional form relationships, normal and non-normal endpoints. We find that across the circumstances considered, bias-corrected matching generally reported less bias, but higher variance than targeted maximum likelihood estimation. When either targeted maximum likelihood estimation or bias-corrected matching incorporated machine learning, bias was much reduced, compared to using misspecified parametric models.
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