Interim decision making in seamless trial designs: An application in an adaptive dose‐finding study in a rare kidney disease

Author:

Papachristofi Olympia1ORCID,Bornkamp Björn1ORCID,Wright Melanie1ORCID,Friede Tim2ORCID

Affiliation:

1. Clinical Development and Analytics Novartis Pharma AG Basel Switzerland

2. Department of Medical Statistics University Medical Center Göttingen Göttingen Germany

Abstract

AbstractAdaptive seamless trial designs, combining the learning and confirming cycles of drug development in a single trial, have gained popularity in recent years. Adaptations may include dose selection, sample size re‐estimation and enrichment of the study population. Despite methodological advances and recognition of the potential efficiency gains such designs offer, their implementation, including how to enable efficient decision making on the adaptations in interim analyzes, remains a key challenge in their adoption.This manuscript uses a case study of an adaptive seamless proof‐of‐concept (Phase 2a)/dose‐finding (Phase 2b) to showcase potential adaptive features that can be implemented in trial designs at earlier development stages and the role of simulations in assessing the design operating characteristics and specifying the decision rules for the adaptations. It further outlines the elements needed to support successful interim analysis decision making on the adaptations while safeguarding study integrity, including the role of different stakeholders, interactive simulation‐based tools to facilitate decision making and operational aspects requiring preplanning. The benefits of the adaptive Phase 2a/2b design chosen compared to following the traditional two separate studies (2a and 2b) paradigm are discussed.With careful planning and appreciation of their complexity and components needed for their implementation, seamless adaptive designs have the potential to yield significant savings both in terms of time and resources.

Publisher

Wiley

Subject

Pharmacology (medical),Pharmacology,Statistics and Probability

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