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Systemic Methodological Dysfunction in Statistical Research for Clinical Decisions

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  • Charles F. Manski

Abstract

I critique a set of entrenched methodological conventions that collectively create systemic dysfunction in statistical research for clinical decisions. These include: (1) the prevalent use of hypothesis tests to compare treatments, (2) remoteness from patient care of the methods used to evaluate the accuracy of predictions of patient outcomes, (3) poor practice of meta-analysis to combine findings across studies, and (4) widespread research with incredible certitude. It appears that the dysfunction is held in place by three factors: (i) rudimentary instruction in statistical methodology received by medical students and residents, (ii) reliance of clinical researchers on consulting biostatisticians, wo act as statistical gatekeepers in evaluation of grant proposals and paper submissions, and (iii) institutional practices of research funding agencies, medical journals, and governmental bodies that regulate medical treatment. I conjecture that systemic changes are necessary to break the existing impasse, moving statistical research to a better equilibrium.

Suggested Citation

  • Charles F. Manski, 2026. "Systemic Methodological Dysfunction in Statistical Research for Clinical Decisions," Papers 2607.23660, arXiv.org.
  • Handle: RePEc:arx:papers:2607.23660
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    References listed on IDEAS

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    1. Molinari, Francesca, 2020. "Microeconometrics with partial identification," Handbook of Econometrics, in: Steven N. Durlauf & Lars Peter Hansen & James J. Heckman & Rosa L. Matzkin (ed.), Handbook of Econometrics, edition 1, volume 7, chapter 0, pages 355-486, Elsevier.
    2. Charles F. Manski, 2019. "Treatment Choice With Trial Data: Statistical Decision Theory Should Supplant Hypothesis Testing," The American Statistician, Taylor & Francis Journals, vol. 73(S1), pages 296-304, March.
    3. Mullahy, John, 2018. "Individual results may vary: Inequality-probability bounds for some health-outcome treatment effects," Journal of Health Economics, Elsevier, vol. 61(C), pages 151-162.
    4. Bhattacharya, Jay & Shaikh, Azeem M. & Vytlacil, Edward, 2012. "Treatment effect bounds: An application to Swan–Ganz catheterization," Journal of Econometrics, Elsevier, vol. 168(2), pages 223-243.
    5. Charles F. Manski, 2021. "Econometrics for Decision Making: Building Foundations Sketched by Haavelmo and Wald," Econometrica, Econometric Society, vol. 89(6), pages 2827-2853, November.
    6. Manski, Charles F., 2023. "Probabilistic prediction for binary treatment choice: With focus on personalized medicine," Journal of Econometrics, Elsevier, vol. 234(2), pages 647-663.
    7. David J. Spiegelhalter & Laurence S. Freedman & Mahesh K. B. Parmar, 1994. "Bayesian Approaches to Randomized Trials," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 157(3), pages 357-387, May.
    8. Charles F. Manski, 2018. "Credible ecological inference for medical decisions with personalized risk assessment," Quantitative Economics, Econometric Society, vol. 9(2), pages 541-569, July.
    9. Charles E. Phelps & Alvin I. Mushlin, 1988. "Focusing Technology Assessment Using Medical Decision Theory," Medical Decision Making, , vol. 8(4), pages 279-289, December.
    10. Charles F. Manski, 2004. "Statistical Treatment Rules for Heterogeneous Populations," Econometrica, Econometric Society, vol. 72(4), pages 1221-1246, July.
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