gms | German Medical Science

Introduction: The conduct of phase II and III programs is costly, time consuming and, due to high failure rates in late development stages, risky. As the go/no-go decision and the sample size chosen for phase III are based on the results observed in phase II, there is a strong connection between phase II and III trials. An integrated planning of phase II and III is therefore reasonable. The performance of phase II/III programs crucially depends on the allocation of the resources to phase II and III in terms of sample size and the rule applied to decide whether to stop or to proceed to phase III.

Methods: Following the concept of assurance [1], a utility-based approach was recently proposed, where optimal planning of phase II/III programs is achieved by taking into account fixed and variable costs of the drug development program and potential gains after a successful launch [2]. However, this method is restricted to programs with a single endpoint and a single phase III trial, while studies with multiple endpoints are common and regulatory authorities generally require statistical significance in two or more phase III trials. We present a generalization of this procedure to programs where two or more phase III trials with multiple endpoints are performed. Optimal phase II sample sizes and go/no-go decision rules are provided for time-to-event outcomes and scenarios, where at least one or two endpoints in one or two phase III trials need to be successful. We investigate the consequences of the biased treatment effect estimate [3] induced by the go/no-go decision and the effects of different strengths of correlation between the phase III trials, which are due to the common information emerging from the phase II trial.

Results: In general, larger assumed benefits result in larger phase II sample size and more liberal go/no-go decision criteria for the optimal design. Independent of the scenario slight deviation from the optimal sample size implies a small decrease of the expected utility, whereas a change in the decision rule has more impact.

Discussion: The proposed method takes into account costs of the program (fixed and per-patient costs), benefit and development risk (success probability) to provide ideas on how optimal design parameters change with changing benefit in relation to the per-patient and fixed costs. We illustrated the proposed method by application to different settings typically met in oncology drug development. However, our approach has broader application, where the challenging part is the determination of realistic values for the utility function parameters, which differ between research areas.

Die Autoren geben an, dass kein Interessenkonflikt besteht.

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