In Journal of inherited metabolic disease ; h5-index 40.0
The natural history of most rare diseases is incompletely understood and usually relies on studies with low level of evidence. Consistent with the goals for future research of rare disease research set by the International Rare Diseases Research Consortium in 2017, the purpose of this paper is to review the recently developed method of quantitative retrospective natural history modelling (QUARNAM) and to illustrate its usefulness through didactically selected analyses examples in an overall population of 849 patients worldwide with seven (ultra-) rare neurogenetic disorders. A quantitative understanding of the natural history of the disease is fundamental for the development of specific interventions and counselling afflicted families. QUARNAM has a similar relationship to a published case study as a meta-analysis has to an individual published study. QUARNAM relies on sophisticated statistical analyses of published case reports focusing on four research questions: How long does it take to make the diagnosis? How long do patients live? Which factors predict disease severity (eg, genotypes, signs/symptoms, biomarkers)? Where can patients be recruited for studies? Useful statistical techniques include Kaplan-Meier estimates, cluster analysis, regression techniques, binary decisions trees, word clouds, and geographic mapping. In comparison to other natural history study methods (prospective studies or retrospective studies such as chart reviews), QUARNAM can provide fast information on hard clinical endpoints (ie, survival, diagnostic delay) with a lower effort. The choice of method for a particular drug development program may be driven by the research question and may encompass combinatory approaches. This article is protected by copyright. All rights reserved.
Garbade Sven F, Zielonka Matthias, Komatzsuzaki Shoko, Kölker Stefan, Hoffmann Georg F, Hinderhofer Katrin, Mountford William K, Mengel Eugen, Sláma Tomáš, Mechler Konstantin, Ries Markus
International Rare Diseases Research Consortium, artificial intelligence, drug development, innovative statistical techniques, modelling and simulation, natural history, orphan drugs, rare disease