Bootstrap corrections of treatment effect estimates following selection

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.