TL09 - Covariate-Adjusted Response-Adaptive Designs for Semiparametric Survival Models

Covariate-adjusted response-adaptive (CARA) designs use the available responses to skew the treatment allocation towards the treatment found to be best at an interim stage of a clinical trial, for a given patient's covariate profile. There has recently been extensive research on CARA designs with parametric distributional assumption on the patient responses. However, the range of application for such designs become limited in real clinical trials. Sverdlov,Rosenberger and Ryzenik (2013) has pointed out that irrespective of a specific parametric form of the survival outcomes, their proposed CARA designs based on the exponential model provide valid statistical inference, provided the final analysis is performed using the appropriate accelerated failure time (AFT) model. In real survival trials, however, the planned primary analysis is rarely conducted using an AFT model. The proposed CARA designs are developed obviating any distributional assumptions about the survival responses, relying only on the proportional hazards assumption between the two treatment arms. To meet the multiple experimental objectives of a clinical trial, the proposed designs are developed based on optimal allocation approach. The covariate-adjusted doubly-adaptive biased coin design and the covariate-adjusted efficient randomised adaptive design are used to randomise the patients to achieve the derived targets on expectation. These expected targets are functions of the Cox regression coefficients that are estimated sequentially with the arrival of every new patient into the trial. The merits of the proposed designs are validated using extensive simulation studies assessing their operating characteristics and has also been implemented to re-design a real-life confirmatory clinical trial.

Key Questions for Discussion :

1) Which Phases are such designs applicable in real clinical trials ?
2) How can this be extended for multi-arm platform trials for a phase 2 clinical trial ?
3) How are the derived optimal design methods are comparable to the Bayesian Optimal methods that are being used in designing adaptive phase 2 trials.
4) How well does the proposed methodology fits into the FDA's Complex Innovative Design Programme ?
5) How does the proposed designs help align with the primary analysis methods in survival trials and how it fits to the ICH E9 guidelines ?
6) Can the Estimand framework be implemented given that such a design is for confirmatory trials ?
7) Is an optimal design necessary for a real-life pivotal trial ?

Censored response

Optimal allocation

Power

Proportional Hazard

Unbalanced randomization

Cox Model 

Clinical Trial Design (e.g., Innovative/Complex Design, Estimands, Master Protocol)

ASA Biopharmaceutical Section Regulatory-Industry Statistics Workshop 2023