Evaluation of Clinical Trial Design Quality Using Desirability Functions
The design phase of a randomized controlled clinical trial is critical to its success. With many non-adaptive designs and an explosive number of adaptive designs introduced to the research community, the number of designs from which a statistician can select has the potential to be overwhelming. At times, a statistician may be uncertain how a newer adaptive design will perform in a particular setting of interest. While regulatory agencies have originally treated adaptive designs with resistance, recent years have seen more acceptance if there is extensive simulation work that shows good control of Type I error.
There are many adaptive designs, and it is important to understand and compare characteristics of competing designs before implementation. However, the overall lack of understanding of the performance of adaptive designs with regard to several design characteristics and the lack of an effective tool to measure overall design quality may have led to clinical trial statisticians implementing traditional designs rather than adopting more innovative methods. Yet adaptive designs have many appealing features that can benefit both the clinical trial sponsor, who funds the trial, and the clinical trial subjects. These strengths include early completion of a trial due to overwhelming efficacy and minimizing the number of subjects assigned to an inferior treatment arm.
The aim of this dissertation is to introduce methodology that provides statisticians and other clinical trial stakeholders with a tool that can measure the overall quality of a design and thereby facilitate comparison across competing designs. The methodology utilizes desirability functions to measure various statistical and non-statistical features that contribute to the quality of a design. Specifically, individual desirability functions evaluate a library of components including statistical considerations, such as treatment group size imbalance, probability of covariate imbalance, accidental bias, control for chronological bias, Type I error and power, and ethical considerations, such as minimizing the expected number of failures and total sample size needed in the whole trial. The proposed strategy is to compute an overall desirability score for each design, use it to rank the clinical trial designs of interest, and select the most relevant and efficient design for the trial's various objectives. To facilitate use of the proposed methodology, the project includes the development of an online interactive tool for the user to incorporate input before desirability functions are generated to help the user select the most appropriate design for the trial.