Optimal flexible sample size design with robust power |
| |
| Authors: | Lanju Zhang Lu Cui Bo Yang |
| |
| Affiliation: | Data and Statistical Sciences, AbbVie Inc, North Chicago, IL, U.S.A. |
| |
| Abstract: | It is well recognized that sample size determination is challenging because of the uncertainty on the treatment effect size. Several remedies are available in the literature. Group sequential designs start with a sample size based on a conservative (smaller) effect size and allow early stop at interim looks. Sample size re‐estimation designs start with a sample size based on an optimistic (larger) effect size and allow sample size increase if the observed effect size is smaller than planned. Different opinions favoring one type over the other exist. We propose an optimal approach using an appropriate optimality criterion to select the best design among all the candidate designs. Our results show that (1) for the same type of designs, for example, group sequential designs, there is room for significant improvement through our optimization approach; (2) optimal promising zone designs appear to have no advantages over optimal group sequential designs; and (3) optimal designs with sample size re‐estimation deliver the best adaptive performance. We conclude that to deal with the challenge of sample size determination due to effect size uncertainty, an optimal approach can help to select the best design that provides most robust power across the effect size range of interest. Copyright © 2016 John Wiley & Sons, Ltd. |
| |
| Keywords: | group sequential designs promising zone designs sample size re‐estimation optimality criterion average performance score |
|
|
