A common challenge for the development of drugs in rare diseases and special populations, eg, paediatrics, is the small numbers of patients that can be recruited into clinical trials. Extrapolation can be used to support development and licensing in paediatrics through the structured integration of available data in adults and prospectively generated data in paediatrics to derive conclusions that support licensing decisions in the target paediatric population. In this context, Bayesian analyses have been proposed to obtain formal proof of efficacy of a new drug or therapeutic principle by using additional information (data, opinion, or expectation), expressed through a prior distribution. However, little is said about the impact of the prior assumptions on the evaluation of outcome and prespecified strategies for decision‐making as required in the regulatory context.On the basis of examples, we explore the use of data‐based Bayesian meta‐analytic–predictive methods and compare these approaches with common frequentist and Bayesian meta‐analysis models. Noninformative efficacy prior distributions usually do not change the conclusions irrespective of the chosen analysis method. However, if heterogeneity is considered, conclusions are highly dependent on the heterogeneity prior. When using informative efficacy priors based on previous study data in combination with heterogeneity priors, these may completely determine conclusions irrespective of the data generated in the target population. Thus, it is important to understand the impact of the prior assumptions and ensure that prospective trial data in the target population have an appropriate chance, to change prior belief to avoid trivial and potentially erroneous conclusions.
The pharmaceutical industry and its global regulators have routinely used frequentist statistical methods, such as null hypothesis significance testing and
p
values, for evaluation and approval of new treatments. The clinical drug development process, however, with its accumulation of data over time, can be well suited for the use of Bayesian statistical approaches that explicitly incorporate existing data into clinical trial design, analysis and decision-making. Such approaches, if used appropriately, have the potential to substantially reduce the time and cost of bringing innovative medicines to patients, as well as to reduce the exposure of patients in clinical trials to ineffective or unsafe treatment regimens. Nevertheless, despite advances in Bayesian methodology, the availability of the necessary computational power and growing amounts of relevant existing data that could be used, Bayesian methods remain underused in the clinical development and regulatory review of new therapies. Here, we highlight the value of Bayesian methods in drug development, discuss barriers to their application and recommend approaches to address them. Our aim is to engage stakeholders in the process of considering when the use of existing data is appropriate and how Bayesian methods can be implemented more routinely as an effective tool for doing so.
The European Agency for the Evaluation of Medicinal Products has recently completed the consultation of a draft guidance on how to implement conditional approval. This route of application is available for orphan drugs, emergency situations and serious debilitating or life-threatening diseases. Although there has been limited experience in implementing conditional approval to date, PSI (Statisticians in the Pharmaceutical Industry) sponsored a meeting of pharmaceutical statisticians with an interest in the area to discuss potential issues. This article outlines the issues raised and resulting discussions, based on the group's interpretation of the legislation. Conditional approval seems to fit well with the accepted regulatory strategy in HIV. In oncology, conditional approval may be most likely when (a) compelling phase II data are available using accepted clinical outcomes (e.g. progression/recurrence-free survival or overall survival) and Phase III has been planned or started, or (b) when data are available using a surrogate endpoint for clinical outcome (e.g. response rate or biochemical measures) from a single-arm study in rare tumours with high response, compared with historical data. The use of interim analyses in Phase III for supporting conditional approval raises some challenging issues regarding dissemination of information, maintenance of blinding, potential introduction of bias, ethics, switching, etc.
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