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Background TNK-S2B, an innovative, randomized, seamless phase II/III trial of tenecteplase versus rt-PA for acute ischemic stroke, terminated for slow enrollment before regulatory approval of use of phase II patients in phase III. Purpose (1) To review the trial design and comprehensive type I error rate simulations and (2) to discuss issues raised during regulatory review, to facilitate future approval of similar designs. Methods In phase II, an early (24-h) outcome and adaptive sequential procedure selected one of three tenecteplase doses for phase III comparison with rt-PA. Decision rules comparing this dose to rt-PA would cause stopping for futility at phase II end, or continuation to phase III. Phase III incorporated two co-primary hypotheses, allowing for a treatment effect at either end of the trichotomized Rankin scale. Assuming no early termination, four interim analyses and one final analysis of 1908 patients provided an experiment-wise type I error rate of Results Over 1,000 distribution scenarios, each involving 40,000 replications, the maximum type I error in phase III was 0.038. Inflation from the dose selection was more than offset by the one-half continuity correction in the test statistics. Inflation from repeated interim analyses was more than offset by the reduction from the clinical stopping rules for futility at the first interim analysis. Limitations Design complexity and evolving regulatory requirements lengthened the review process. Conclusions (1) The design was innovative and efficient. Per protocol, type I error was well controlled for the co-primary phase III hypothesis tests, and experiment-wise. (2a) Time must be allowed for communications with regulatory reviewers from first design stages. (2b) Adequate type I error control must be demonstrated. (2c) Greater clarity is needed on (i) whether this includes demonstration of type I error control if the protocol is violated and (ii) whether simulations of type I error control are acceptable. (2d) Regulatory agency concerns that protocols for futility stopping may not be followed may be allayed by submitting interim analysis results to them as these analyses occur.

Background Many interventions in today's health sciences are multicomponent, and often one or more of the components are behavioral. Two approaches to building behavioral interventions empirically can be identified. The more typically used approach, labeled here the classical approach, consists of constructing a likely best intervention a priori, and then evaluating the intervention in a standard randomized controlled trial (RCT). By contrast, the emergent phased experimental approach involves programmatic phases of empirical research and discovery aimed at identifying individual intervention component effects and the best combination of components and levels. Purpose The purpose of this article is to provide a head-to-head comparison between the classical and phased experimental approaches and thereby highlight the relative advantages and disadvantages of these approaches when they are used to select program components and levels so as to arrive at the most potent intervention. Methods A computer simulation was performed in which the classical and phased experimental approaches to intervention development were applied to the same randomly generated data. Results The phased experimental approach resulted in better mean intervention outcomes when the intervention effect size was medium or large, whereas the classical approach resulted in better mean intervention outcomes when the effect size was small. The phased experimental approach led to identification of the correct set of intervention components and levels at a higher rate than the classical approach across all conditions. Limitations Some potentially important factors were not varied in the simulation, for example the underlying structural model and the number of intervention components. Conclusions The phased experimental approach merits serious consideration, because it has the potential to enable intervention scientists to develop more efficacious behavioral interventions. Clinical Trials 2009; 6: 5—15. http://ctj.sagepub.com