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Include patient-level information and perspectives in design and decision making throughout the clinical trial process. Validate new methods and propose improvements for regulatory purposes.

ASTERIX worked through six highly interactive and interdependent Work Packages ranging from development of methodology, stakeholder participation to the dissemination of the results. Unique in this project was that patients were directly involved in the research process and their input is taken into account in design and analysis of studies.

Context Six percent of the global population is affected by one of the estimated — rare diseases at some stage in their life [ 1 ].

Evaluating interventions aimed at preventing, diagnosing or treating a rare disease is a challenge, and can lead to slow evaluation and approval of Orphan Medicinal Products OMPs for marketing, and thereby delay access by patients [ 3 ].

To stimulate the development of medicines for rare diseases the EU Orphan Regulation came into effect in This regulation provides an incentive for research, development and marketing of OMPs to target rare diseases [ 4 ].

Although more than orphan drug designations have been granted since , by only OMPs were granted market authorisation [ 5 ]. Hence, although drugs do become available, a treatment still needs to be found for the vast majority of rare diseases.

The main issue that distinguishes medicines development for rare diseases from more common diseases is the challenge of generating robust clinical evidence. The limited recruitment potential calls for an efficient study design, able to estimate the treatment effect in a valid and reliable way with a small number of patients [ 6 ].

There is an abundance of methodology to improve the design and analysis of individual trials, often essentially aimed at increasing efficiency: extract more information from the same trial, increase the probability of success of an individual clinical trial and enable the conduct of smaller trials.

Yet, progress for clinical trials in truly small populations has proven difficult to achieve. Some frameworks have been proposed to guide the choice of the best suited methodology and study designs in drug development for such rare diseases. Other available frameworks typically aimed to propose algorithms or decision processes to arrive at the most suited design for a given clinical trial. These focus either on a specific condition [ 8 , 9 ] , a specific method or group of methods [ 10 , 11 ] , or provide general recommendations [ 12 — 15 ].

However, most of these algorithms or frameworks are guided by items related to only a few characteristics of the condition such as clinical course, timing and reversibility of the outcome, or trial feasibility, and they are not always exhaustive to fit all possible situations. The vision of ASTERIX was that such progress can be best made, by advancing in coherence: 1 statistical methodology for design and analysis, 2 incorporation of the patient perspective in design and outcomes and 3 uptake in practice and regulatory guidance [ 16 ].

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We aimed to evaluate these methods for added value against an appropriate framework to guide application, preferably tailored to characteristics of the medical condition. The limitations of the existing frameworks to provide guidance that directly incorporates characteristics of the medical condition treated are obvious.

Apart from their low prevalence, orphan diseases are a highly heterogeneous group of diseases.

Such heterogeneity makes it very difficult to issue useful regulatory recommendations relevant to all or at least most possible clinical situations in the course of uncommon diseases.

Nevertheless, some groups of conditions share similar clinical characteristics linked to the applicability of certain trial designs and general approaches.

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Extrapolations of evidence from a larger population to the smaller target population is widely used to support decisions in this situation. For the justification of requirements specified in EMA Paediatric Investigation Plans, this paper discusses how to specify the clinical trial design in the target population, when the data from the source population at the time of planning is not available but development in the target population will only start, after a treatment effect in the source population has been demonstrated.

A framework based on prior beliefs is formulated to investigate whether the significance level for the test of the primary endpoint in confirmatory trials can be relaxed, and the sample size reduced, while controlling a certain level of certainty about the effects. The procedure is based on a so called skepticism factor, that quantifies the belief that a treatment effect observed in the larger population can be extrapolated to the target population. When the primary outcome is binary, small sample sizes increase the chance of observing zero events.

The frequentist random-effects model is known to induce bias and to result in improper interval estimation of the overall treatment effect in a meta-analysis with zero events. Bayesian hierarchical modelling could be a promising alternative.Printable science worksheets for teachers. Life Science. File size: Learn more. Research and publish the best content. In addition, they propose a greedy algorithm for nearly optimal tests, which is computationally more efficient.