The preprint is available at https://arxiv.org/abs/2010.06567
Adapting the final sample size of a trial to the evidence accruing during the trial is a natural way to address planning uncertainty. Since the sample size is usually determined by an argument based on the power of the trial, an interim analysis raises the question of how the final sample size should be determined conditional on the accrued information. To this end, we first review and compare common approaches to estimating conditional power which is often used in heuristic sample size recalculation rules. We then discuss the connection of heuristic sample size recalculation and optimal two-stage designs demonstrating that the latter is the superior approach in a fully pre-planned setting. Hence, unplanned design adaptations should only be conducted as reaction to trial-external new evidence, operational needs to violate the originally chosen design, or \textit{post~hoc} changes in the optimality criterion but not as a reaction to trial-internal data. We are able to show that commonly discussed sample size recalculation rules lead to paradoxical adaptations where an initially planned optimal design is not invariant under the adaptation rule even if the planning assumptions do not change. Finally, we propose two alternative ways of reacting to newly emerging trial-external evidence in ways that are consistent with the originally planned design to avoid such inconsistencies.
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R script files for reproducing the results are available in the R folder.
The required dependencies can be installed using the
renv
package and the provided lockfile renv.lock.
A bash script run to execute all code is provided.
On Linux/Unix, execute
./run
(might take a while).
Plots are stored in output/figures.