title: Evaluating treatment effectiveness under model misspecification: A comparison of targeted maximum likelihood estimation with bias-corrected matching.
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.
authors: Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK noemi.kreif@lshtm.ac.uk.
authors: Noémi
authors: N
authors: Kreif
authors: Department of Epidemiology, Harvard School of Public Health, Boston, MA, USA.
authors: Susan
authors: S
authors: Gruber
authors: Birkbeck, University of London, London, UK.
authors: Rosalba
authors: R
authors: Radice
authors: Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK.
authors: Richard
authors: R
authors: Grieve
authors: Birkbeck, University of London, London, UK.
authors: Jasjeet S
authors: JS
authors: Sekhon
mesh_terms: D000368:Aged; D015982:Bias; D003198:Computer Simulation; D016001:Confidence Intervals; D003627:Data Interpretation, Statistical; D006622:Hip Prosthesis; D006801:Humans; D016013:Likelihood Functions; D000069550:Machine Learning; D008297:Male; D015233:Models, Statistical; D010003:Osteoarthritis / Q000453:epidemiology / Q000601:surgery; D011788:Quality of Life; D016896:Treatment Outcome
keywords: bias-corrected matching; double robustness; machine learning; model misspecification; targeted maximum likelihood estimation; treatment effectiveness