The attractiveness of the concept of a prospectively designed two-stage clinical trial

Doubly randomized delayed-start design for enrichment studies with responders or nonresponders

High placebo response has been a major source of bias and is difficult to deal with in many central nervous system (CNS) clinical trials. This bias has led to a high failure rate in mood disorder trials even with known effective drugs. For cancer trials, the traditional parallel group design biases the inference on the maintenance effect of the new drug with the traditional time-to-treatment failure analysis. To minimize bias, we propose a doubly randomized delayed-start design for clinical trials with enrichment. The design consists of two periods. In the first period, patients can be randomized to receive several doses of a new drug or a control. In the second period, control patients of the first period of an enriched population can be rerandomized to receive the same or fewer doses of the new drug or to continue on the control. Depending on the clinical needs, different randomization ratios can be applied to the two periods. The essential feature is that the design is naturally adaptive because of the randomization for the second period. As a result, other aspects of the second period, such as the sample size, can be modified adaptively when an interim analysis is set up for the first period. At the end of the trial, response data from both randomizations are combined in an integrated analysis. Because of the enrichment in the second period, the design increases the probability of trial success and, in addition, reduces the required sample size. Thus, for clinical development, the design offers greater efficiency.

Understanding the FDA guidance on adaptive designs: historical, legal, and statistical perspectives

The recent Food and Drug Administration (FDA) guidance for industry on adaptive designs is perhaps one of the important undertakings by CDER/CBER Office of Biostatistics. Undoubtedly, adaptive designs may affect almost all phases of clinical development and impact nearly all aspects of clinical trial planning, execution and statistical inference. Thus, it is a significant accomplishment for the Office of Biostatistics to develop this well-thought-out and all-encompassing guidance document. In this paper, we discuss some critical topical issues of adaptive designs with supporting methodological work from either existing literature, additional technical notes, or accompanying papers. In particular, we provide numerous sources of design, conduct, analysis, and interpretation bias that arise from statistical procedures. We illustrate, as a result, and caution that substantial research is necessary for many adaptive designs to meet required scientific standards prior to their applications in clinical trials.

Optimal Conditional Error Functions for the Control of Conditional Power

Ethical considerations and the competitive environment of clinical trials usually require that any given trial have sufficient power to detect a treatment advance. If at an interim analysis the available data are used to decide whether the trial is promising enough to be continued, investigators and sponsors often wish to have a high conditional power, which is the probability to reject the null hypothesis given the interim data and the alternative of interest. Under this requirement a design with interim sample size recalculation, which keeps the overall and conditional power at a prespecified value and preserves the overall type I error rate, is a reasonable alternative to a classical group sequential design, in which the conditional power is often too small. In this article two-stage designs with control of overall and conditional power are constructed that minimize the expected sample size, either for a simple point alternative or for a random mixture of alternatives given by a prior density for the efficacy parameter. The presented optimality result applies to trials with and without an interim hypothesis test; in addition, one can account for constraints such as a minimal sample size for the second stage. The optimal designs will be illustrated with an example, and will be compared to the frequently considered method of using the conditional type I error level of a group sequential design.

Mid-course sample size modification in clinical trials based on the observed treatment effect

It is not uncommon to set the sample size in a clinical trial to attain specified power at a value for the treatment effect deemed likely by the experimenters, even though a smaller treatment effect would still be clinically important. Recent papers have addressed the situation where such a study produces only weak evidence of a positive treatment effect at an interim stage and the organizers wish to modify the design in order to increase the power to detect a smaller treatment effect than originally expected. Raising the power at a small treatment effect usually leads to considerably higher power than was first specified at the original alternative. Several authors have proposed methods which are not based on sufficient statistics of the data after the adaptive redesign of the trial. We discuss these proposals and show in an example how the same objectives can be met while maintaining the sufficiency principle, as long as the eventuality that the treatment effect may be small is considered at the design stage. The group sequential designs we suggest are quite standard in many ways but unusual in that they place emphasis on reducing the expected sample size at a parameter value under which extremely high power is to be achieved. Comparisons of power and expected sample size show that our proposed methods can out-perform L. Fisher's 'variance spending' procedure. Although the flexibility to redesign an experiment in mid-course may be appealing, the cost in terms of the number of observations needed to correct an initial design may be substantial.