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Clinical trial design enabling e-optimal treatment rules

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  • Charles F. Manski

    (Institute for Fiscal Studies and Northwestern University)

  • Aleksey Tetenov

    (Institute for Fiscal Studies and University of Geneva)

Abstract

Medical research has evolved conventions for choosing sample size in randomized clinical trials that rest on the theory of hypothesis testing. Bayesians have argued that trials should be designed to maximize subjective expected utility in settings of clinical interest. This perspective is compelling given a credible prior distribution on treatment response, but Bayesians have struggled to provide guidance on specification of priors. We use the frequentist statistical decision theory of Wald (1950) to study design of trials under ambiguity. We show that e-optimal rules exist when trials have large enough sample size. An e-optimal rule has expected welfare within e of the welfare of the best treatment in every state of nature. Equivalently, it has maximum regret no larger than e. We consider trials that draw predetermined numbers of subjects at random within groups stratified by covariates and treatments. The principal analytical findings are simple sufficient conditions on sample sizes that ensure existence of e-optimal treatment rules when outcomes are bounded. These conditions are obtained by application of Hoeffding (1963) large deviations inequalities to evaluate the performance of empirical success rules.

Suggested Citation

  • Charles F. Manski & Aleksey Tetenov, 2015. "Clinical trial design enabling e-optimal treatment rules," CeMMAP working papers CWP60/15, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    • Handle: RePEc:ifs:cemmap:60/15
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    References listed on IDEAS

    as
    1. Tetenov, Aleksey, 2012. "Statistical treatment choice based on asymmetric minimax regret criteria," Journal of Econometrics, Elsevier, vol. 166(1), pages 157-165.
    2. Charles F. Manski, 2004. "Statistical Treatment Rules for Heterogeneous Populations," Econometrica, Econometric Society, vol. 72(4), pages 1221-1246, July.
    3. Manski, Charles F., 2007. "Minimax-regret treatment choice with missing outcome data," Journal of Econometrics, Elsevier, vol. 139(1), pages 105-115, July.
    4. Daniel Ellsberg, 1961. "Risk, Ambiguity, and the Savage Axioms," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 75(4), pages 643-669.
    5. Stoye, Jörg, 2009. "Minimax regret treatment choice with finite samples," Journal of Econometrics, Elsevier, vol. 151(1), pages 70-81, July.
    6. Yi Cheng, 2003. "Choosing sample size for a clinical trial using decision analysis," Biometrika, Biometrika Trust, vol. 90(4), pages 923-936, December.
    7. Karl Schlag, 2006. "ELEVEN - Tests needed for a Recommendation," Economics Working Papers ECO2006/2, European University Institute.
    8. Charles F Manski, 2007. "Adaptive Minimax-Regret Treatment Choice, with Application to Drug Approval," Levine's Working Paper Archive 122247000000001404, David K. Levine.
    9. 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.
    10. Stoye, Jörg, 2012. "Minimax regret treatment choice with covariates or with limited validity of experiments," Journal of Econometrics, Elsevier, vol. 166(1), pages 138-156.
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