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Note on Special Technical Issue on Adaptive Designs for Clinical Trials

The Journal of Biopharmaceutical Statistics published a special issue (Special Issue: Special Theme for Adaptive Clinical Trial Designs, 20(6), 2010) of discussion papers on the draft adaptive design guidance by the U.S. Food and Drug Administration (FDA). In this sequel to the special discussion issue, we present a collection of technical papers that address not only existing problems with adaptive designs as pointed out by the FDA guidance but also introduce new designs for clinical trial settings that have not been adequately tackled in the literature. There are several papers discussing the optimization or efficiency of adaptive designs and data analysis of adaptive trials. The adaptive designs with sample size remain an interesting topic. The other topics include adaptive design with changing patient population, adaptive design for combination drug development, doubly randomized delayed-start designs for enrichment trials, adding a treatment arm mid-study to an ongoing trial, adaptive randomization trial with bivariate ordinal outcomes, and two-stage seamless adaptive design with Weibull-distributed survival endpoint. Adaptive sample size adjustment with unblinded interim data has been very controversial. It was argued that most such designs violate the sufficiency principle of statistical inference and therefore are inefficient compared to traditional group sequential designs. It is shown in this issue that a two-stage design with sample size adjustment can be more efficient than a matching group sequential design when there is substantial overrunning. The authors point out the logical flaws of the literature that has led to the conclusion that adaptive designs are inefficient. Another controversy with adaptive designs has been a lack of a uniform approach to estimations of the effect size. To address this issue, an estimating equation framework based on a marked point process is developed for an adaptive selection design. The framework is in fact very general and can be applied to many other adaptive designs settings, including those with dynamic or adaptive randomizations. It is believed that design changes based on blinded data review do not inflate the type 1 error rates and bias the estimates. It is shown here that even if a trial is modified on the basis of distribution of baseline prognostic factors, without the aid of blinded data, the type 1 error rates can be greatly inflated. It is also believed