(Vienna, 30th June 2014) Six to eight per cent of the EU population – equivalent to as many as 36 million people – suffer from one of the 5,000 to 8,000 known rare diseases. Both for the research of new treatments for rare diseases, as well as personalised medicine and the development of medicines for children, the patient groups available are often only small. At the MedUni Vienna, smaller patient groups are increasingly being focused on, for example in the Centre for Rare and Undiagnosed Diseases (CeRUD), but also at the Institute of Medical Statistics, especially in relation to the development of statistical methods to evaluate clinical studies for small groups.
The Institute of Medical Statistics at the MedUni Vienna is one of the world’s leading institutions for the development of statistical methods used to plan and analyse clinical studies. The MedUni Vienna is also involved in three EU projects on this subject (ASTERIX, IDEAL and INSPIRE), which were set up to develop new methods for the design and analysis of clinical studies in small patient groups.
“The options for evaluating new therapeutic and pharmacological developments using conventional statistical methods are very limited due to the small patient groups, and often fail to lead to any clear statements regarding the effectiveness of innovative treatments,” says Martin Posch, Head of the Institute of Medical Statistics at the Center for Medical Statistics, Informatics and Intelligent Systems. “As a result, we need to develop more efficient statistical methods.”
Adaptive designs for clinical studies have been developed at the MedUni Vienna. For the first time, these allow the study design to be adapted quickly and effectively during an ongoing study. Interim results from a study can be used, for example, to optimise the allocation of medication-based treatment to patients. Says Posch: “The challenge lies in making statistically valid statements during the statistical analysis despite the changes to the ongoing study." Studies can therefore be made more efficient and often even shortened. “This also ensures that patients receive ineffective treatments less often." At the same time, the approval of new forms of treatment and medications can be accelerated for the affected groups thanks to these methods.
Another approach being taken in research is the transfer of research results to new groups of patients (extrapolation). “The development of statistical methods for the controlled extrapolation of findings on the effectiveness and safety of medications from adults to children is essential for the efficient development of new medications and their use in children," says Christoph Male from the MedUni Vienna’s University Department of Paediatrics and Adolescent Medicine. As the Austrian representative to the Paediatric Committee of the European Medicines Agency, Male played a key role in helping to design the new concept document on this subject. “These new methods will help to reduce the number of children required in clinical studies to the necessary level. The cooperation between clinical practice and statistics is crucial for their success.”
Service: Symposium on Small Populations
From 9 a.m. on both the 1st and 2nd of July 2014, the MedUni Vienna will be hosting the Symposium on Small Populations in the auditorium of the University Dental Department of Vienna (Universitätszahnklinik Wien).
MedUni Vienna focuses on smaller patient groups
Six to eight per cent of the EU population – equivalent to as many as 36 million people – suffer from one of the 5,000 to 8,000 known rare diseases. Both for the research of new treatments for rare diseases, as well as personalised medicine and the development of medicines for children, the patient groups available are often only small. At the MedUni Vienna, smaller patient groups are increasingly being focused on, for example in the Centre for Rare and Undiagnosed Diseases (CeRUD), but also at the Institute of Medical Statistics, especially in relation to the development of statistical methods to evaluate clinical studies for small groups.