BS in Statistics
Adaptive design methodologies use prior information to develop a clinical trial design. The goal of an adaptive design is to maintain the integrity and validity of the study while giving the researcher flexibility in identifying the optimal treatment. An example of an adaptive design can be seen in a basic pharmaceutical trial. There are three phases of the overall trial to compare treatments and experimenters use the information from the previous phase to make changes to the subsequent phase before it begins.
Adaptive design methods have been in practice since the 1970s, but have become increasingly complex ever since. One type of adaptive design is adaptive randomization. This is where the researcher makes changes in the way patients are randomized to treatment groups based on information gathered so far in the trial. Adaptations can be made to either trial procedures (eligibility criteria, study dose, treatment duration, study endpoints, laboratory testing procedures, diagnostic procedures, criteria for evaluation, and assessment of clinical responses) or statistical procedures (randomization, study design, study objectives/hypotheses, sample size, data monitoring and interim analysis, statistical analysis plan, and methods for data analysis).
The biased coin design, adaptive biased coin design and covariate adaptive randomization are specific types of adaptive randomization designs which are aimed at balancing certain aspects of a clinical trial. The biased coin design and the adaptive biased coin design aim to balance treatment group sample sizes by assigning the next patient to the group with the smaller sample size with higher probability. The biased coin design uses a fixed probability to achieve this whereas the adaptive biased coin design determines the severity of the imbalance between treatment groups using the total sample size to scale the difference in treatment group sample sizes. The probability of assignment to a specific group in the adaptive biased coin design thus depends on the ratio of difference in sample sizes between treatment groups to total sample size. Covariate randomization designs aim to balance the covariates across the treatment groups by assigning the next patient to the group that causes the smallest maximum imbalance across the covariate groups.
SAS® Macros to perform the adaptive biased coin design and covariate adaptive randomization are available upon request.
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