Evaluation of Performance of Adaptive Designs Based on Treatment Effect Intervals

Fang Fang,Weichung Joe Shih,Yong Lin,Guangrui Zhu,Shou-En Lu

doi:10.5539/ijsp.v7n6p81

Abstract

The accuracy of the treatment effect estimation is crucial to the success of Phase 3 studies. The calculation of sample size relies on the treatment effect estimation and cannot be changed during the trial in a fixed sample size design. Oftentimes, with limited efficacy data available from early phase studies and relevant historical studies, the sample size calculation may not accurately reflect the true treatment effect. Several adaptive designs have been proposed to address this uncertainty in the sample size calculation. These adaptive designs provide flexibility of sample size adjustment during the trial by allowing early trial stopping or sample size adjustment at interim look(s). The use of adaptive designs can optimize the trial performance when the treatment effect is an assumed constant value. However in practice, it may be more reasonable to consider the treatment effect within an interval rather than as a point estimate. Because proper selection of adaptive designs may decrease the failure rate of Phase 3 clinical trials and increase the chance for new drug approval, this paper proposes measures and evaluates the performance of different adaptive designs based on treatment effect intervals, and identifies factors that may affect the performance of adaptive designs.

Highlights

It is well-reported that the cost of drug development keeps rising at a high rate while the new drug applications do not keep up with the same rate (Lesko, 2006)
Because proper selection of adaptive designs may decrease the failure rate of Phase 3 clinical trials and increase the chance for new drug approval, this paper proposes measures and evaluates the performance of different adaptive designs based on treatment effect intervals, and identifies factors that may affect the performance of adaptive designs
The most striking result here is that designs with treatment effect following the location-scaled beta(5, 2) perform much better than designs with treatment effect following a location-scaled beta(2, 5) or beta(4, 5), the performance indicators with beta(5, 2) are relatively insensitive to the number of looks, the timing of the sample size adjustment, or the pattern of sample size increment

Summary

Introduction

It is well-reported that the cost of drug development keeps rising at a high rate while the new drug applications do not keep up with the same rate (Lesko, 2006). A poorly designed Phase 3 trial may be a likely reason to account for the high failure rate. It costs both money and patient lives (Thoelke, 2007). The calculation of sample size in fixed sample size (FS) designs relies on the assumption of treatment effect and cannot be changed during the trial. With limited efficacy data available from early phase or other relevant historical studies, the sample size calculation may not accurately reflect the true treatment effect. This lack of knowledge leads to the calculated sample size either too small or too large. The results could be either a waste of finances and patient resources or trial failure

Methods

Results

Conclusion