Semi-blinded design in clinical trials

Abstract

The semi-blinded design can be applied for the adaptive design in clinical trials. According the the FDA guidance, an adaptive clinical design study would modify the study design and hypotheses based on the analysis of accrued data. An independent Data Monitoring Committee analyzes the unblinded data in the middle of a clinical trial and makes recommendations to the sponsor if any action is needed. The unblinded interim analysis could potentially introduce bias and put the integrity of the trial at risk. The proposed methodology is technically blinded for the interim analysis. In fact, actual treatment labels are not released. It is based on a treatment re-labeling approach in which the dummy treatments associated with the actual treatments via a pre-specified transition probability matrix are released in the dataset. The semi-blinded test statistic depends on this transition probability matrix. However, it can be evaluated without knowing the transition probability matrix if this matrix is a Latin square. As the stagnation probability approach to 0.5, the more information we will lose. It will lead to obtain a lower power of testing hypothesis, and may not be able to show the utility or efficacy of study treatment if this probability is close to 0.5. The higher the stagnation probability, the higher disclosure risk we have. The randomization codes may be broken as the stagnation probability approach to 1. The sponsor should strike a balance between the statistical power and disclosure risk.

Keywords:

• Semi-blinded design
• transition probability matrix
• ANOVA model
• adaptive design