Adaptive Informational Design of Confirmatory Phase III Trials With an Uncertain Biomarker Effect to Improve the Probability of Success

Abstract

Oncology drug developers sometimes decide to initiate Phase III randomized confirmatory trials at risk after significant preliminary anti-tumor activities are observed in small Phase I/II single arm studies.There are two clear challenges. First, these investigational drugs may have a greater benefit in a biomarker enriched population. But the limited data from Phase I/II can hardly provide the much-needed information for selecting a biomarker cutpoint or prioritizing a biomarker hypothesis for Phase III testing.Second, the data seldom provides any insight on how the treatment benefit evolves over time. Riskmitigation strategies such as conventional adaptive-designs that rely on interim analyses for modifying the study design are less reliable because the treatment effect observed at an interim analysis may not be the same as in the final analysis. The use of an intermediate endpoint for interim decision makes it even more unreliable because the predictive value of an intermediate endpoint is often unknown for drugs with a new mechanism of action. In this paper, we present an alternative design strategy to mitigate the risks.The idea is to add an analysis of the primary endpoint at end of the Phase III trial in a subgroup of patients representing the overall study population. We call it informational analysis and the corresponding design informational design to emphasize its difference from the conventional event-time or calendar-time driven interim analysis. From a high-level perspective, the subgroup analysis is equivalent to a Phase II trial conducted under the same study design at the same time in the same population at the same sites as the Phase III trial. It provides a more reliable resource of information for inference than a separate Phase II trial or a conventional interim analysis. The strategy is applied to address a wide range of statistical issues encountered in expedited development of personalized medicines, including alpha splitting between a biomarker subpopulation and the overall population and de-selection of non-performing biomarker subpopulations. Applications to hypothetical Phase III trials are illustrated. Although the strategy is motivated by oncology studies, it may be applied to drug development in other therapeutic areas with similar concerns.

A roadmap to using historical controls in clinical trials – by Drug Information Association Adaptive Design Scientific Working Group (DIA-ADSWG)

“…Instead, alternative approaches that involve adaptive clinical trial designs are recommended [21,22]. In some cases, sponsors may have more incentive to address the rare diseases directly instead of conducting a noninterventional study.…”

“…In such cases, it may be preferable to have a randomized therapeutic study than a natural history study in which no one receives therapy. Adaptive designs such as the "informational design" that allow adaptation at the end of the study may be useful for rare diseases where information for designing a therapeutic study may be sparse [19][20][21].…”

Clinical trial risk in leukemia: Biomarkers and trial design

“…In situations where the disease mechanism is well understood and treatments target specific molecular mechanisms, integrating a validated biomarker functions in increasing cumulative success rates 23 . However, in many situations, there is uncertainty as the limited data provided by phase I/II trials are often insufficient in determining if the treatment effect is larger among biomarker positive/negative subpopulations 24 . Methods to address this uncertainty include adaptive trial designs which are more efficient use of resources, may require fewer participants, and are more ethical than trials with traditional fixed designs 25,26 .…”

Empowering phase II clinical trials to reduce phase III failures

“…Several authors argue that wider adoption of adaptive designs could improve the success rate in drug development (see, for example, Hay et al, Chen et al,, and Gokhale). Adaptive designs allow modification of the trial and/or statistical procedures of an ongoing trial .…”