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Bootstrap corrections of treatment effect estimates following selection

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  • Rosenkranz, Gerd K.

Abstract

Bias of treatment effect estimators can occur when the maximum effect of several treatments is to be determined or the effect of the selected treatment or subgroup has to be estimated. Since those estimates may contribute to the decision as to whether to continue a drug development program, to select a specific dose or a specific subgroup of patients, methods should be applied that ensure a realistic rather than an overoptimistic estimator of a treatment effect following selection. Selection bias is well studied for normally distributed variables and to a lesser extent for other types of distributions. However, many methods developed for bias correction apply primarily to specific distributions. Since there is always uncertainty about the underlying distribution of data, a more generally applicable method is of interest. The bootstrap has been developed among others to estimate the bias under fairly general distributional assumptions. The potential of the bootstrap in reducing estimator bias after selection is investigated.

Suggested Citation

  • Rosenkranz, Gerd K., 2014. "Bootstrap corrections of treatment effect estimates following selection," Computational Statistics & Data Analysis, Elsevier, vol. 69(C), pages 220-227.
    • Handle: RePEc:eee:csdana:v:69:y:2014:i:c:p:220-227
    DOI: 10.1016/j.csda.2013.08.010
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    References listed on IDEAS

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    1. Cohen, Arthur & Sackrowitz, Harold B., 1989. "Two stage conditionally unbiased estimators of the selected mean," Statistics & Probability Letters, Elsevier, vol. 8(3), pages 273-278, August.
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    Cited by:

    1. Riyadh Rustam Al-Mosawi & Shahjahan Khan, 2018. "Estimating moments of a selected Pareto population under asymmetric scale invariant loss function," Statistical Papers, Springer, vol. 59(1), pages 183-198, March.

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