A two treatment response adaptive design is developed for phase III clinical trials with ordinal categorical treatment outcome using Goodman-Kruskal measure of association. Properties of the proposed design are studied both empirically and theoretically and the acceptability is further illustrated using two real data-sets; one from a clinical trial with trauma patients and the other from a trial with patients having rheumatoid arthritis.

A response adaptive design for ordinal categorical responses.

A number of allocation designs are developed for continuous and binary treatment outcomes to assign a higher number of subjects to the treatment doing better in course of the trial. However, if the response is circular in nature, the definition of a better treatment differs from that under the linear response and hence the already developed designs lack appropriateness. In the current work, redefining the notions of superiority, we develop an allocation function in the context of circular treatment outcomes. Using a response-adaptive route for practical implementation, we study the resulting design both theoretically and numerically and finally illustrate the performance for a real-life example on cataract surgery.

An adaptive allocation design for circular treatment outcome

Randomization Revisited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.4 Response-Adaptive Randomization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141.4.1 Response-Adaptive Randomized Urn Models . . . . . . . . . . . 14
1.4.2 Optimal Response-Adaptive Randomized Designs . . . . . 15
1.4.3 An Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.4.4 Bayesian Adaptive Randomization . . . . . . . . . . . . . . . . . . . . . . 19
1.4.5 Criticism of Response-AdaptiveRandomization Revisited . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
1.5 Covariate-Adjusted Response-Adaptive Randomization . . . . . . . . 221.5.1 Treatment Effect Mapping and Urn-Based CARA
Randomization Designs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231.5.2 Target-Based CARA Randomization Designs . . . . . . . . . . . 23
1.5.3 Utility-Based CARA Randomization Designs . . . . . . . . . . . 24
1.5.4 Bayesian CARA Randomization . . . . . . . . . . . . . . . . . . . . . . . . 241.6 Other Designs with Elements of Adaptive Randomization . . . . . . 25
1.6.1 Randomized Phase I Trial Designs . . . . . . . . . . . . . . . . . . . . . . 251.6.2 Adaptive Optimal Dose-Finding Designs . . . . . . . . . . . . . . . . 25
1.6.3 Randomized Designs with Treatment Selection . . . . . . . . . 26
1.6.4 Group Sequential Adaptive Randomization . . . . . . . . . . . . . 27
1.6.5 Complex Adaptive Design Strategies . . . . . . . . . . . . . . . . . . . . 281.7 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30A randomized, placebo-controlled, double-masked, equal-allocation clinical
trial can be viewed as an exemplary research design to obtain generalizable results on the treatment effect. However, modern clinical trials are increasingly
complex and often require more elaborate designs. A competitive landscape of
pharmaceutical research and development, an enormous number of molecules
that are available as potential drugs, and limited patient resources call for clinical trial designs investigating the effects of multiple treatments within multiple
patient subgroups. Such designs should, in addition, satisfy strict regulatory
requirements such as controlling the chance of making a type I error.

Oleksandr Sverdlov

Papers

An Overview of Adaptive Randomization Designs in Clinical Trials