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Comparing the Performance of Statistical Adjustment Methods by Recovering the Experimental Benchmark from the REFLUX Trial

Author

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  • Luke Keele

    (University of Pennsylvania, Philadelphia, PA, USA)

  • Stephen O’Neill

    (Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK
    Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK)

  • Richard Grieve

    (Department of Health Services Research and Policy, London School of Hygiene and Tropical Medicine, London, UK)

Abstract

Much evidence in comparative effectiveness research is based on observational studies. Researchers who conduct observational studies typically assume that there are no unobservable differences between the treatment groups under comparison. Treatment effectiveness is estimated after adjusting for observed differences between comparison groups. However, estimates of treatment effectiveness may be biased because of misspecification of the statistical model. That is, if the method of treatment effect estimation imposes unduly strong functional form assumptions, treatment effect estimates may be inaccurate, leading to inappropriate recommendations about treatment decisions. We compare the performance of a wide variety of treatment effect estimation methods for the average treatment effect. We do so within the context of the REFLUX study from the United Kingdom. In REFLUX, participants were enrolled in either an randomized controlled trial (RCT) or an observational study arm. In the RCT, patients were randomly assigned to either surgery or medical management. In the patient preference arm, participants selected to either have surgery or medical management. We attempt to recover the treatment effect estimate from the RCT using the data from the patient preference arms of the study. We vary the method of treatment effect estimation and record which methods are successful and which are not. We apply more than 20 different methods, including standard regression models as well as advanced machine learning methods. We find that simple propensity score matching methods provide the least accurate estimates versus the RCT benchmark. We find variation in performance across the other methods, with some, but not all recovering the experimental benchmark. We conclude that future studies should use multiple methods of estimation to fully represent uncertainty according to the choice of estimation approach.

Suggested Citation

  • Luke Keele & Stephen O’Neill & Richard Grieve, 2021. "Comparing the Performance of Statistical Adjustment Methods by Recovering the Experimental Benchmark from the REFLUX Trial," Medical Decision Making, , vol. 41(3), pages 340-353, April.
    • Handle: RePEc:sae:medema:v:41:y:2021:i:3:p:340-353
    DOI: 10.1177/0272989X20986545
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    References listed on IDEAS

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    1. Rosenbaum, Paul R., 2010. "Design Sensitivity and Efficiency in Observational Studies," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 692-702.
    2. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
    3. King, Gary & Nielsen, Richard, 2019. "Why Propensity Scores Should Not Be Used for Matching," Political Analysis, Cambridge University Press, vol. 27(4), pages 435-454, October.
    4. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, January.
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