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The Urea Cycle Disorders Consortium (UCDC) and the European registry and network for Intoxication type Metabolic Diseases (E‐IMD) are the worldwide largest databases for individuals with urea cycle disorders (UCDs) comprising longitudinal data from more than 1100 individuals with an overall long‐term follow‐up of approximately 25 years. However, heterogeneity of the clinical phenotype as well as different diagnostic and therapeutic strategies hamper our understanding on the predictors of phenotypic diversity and the impact of disease‐immanent and interventional variables (e.g., diagnostic and therapeutic interventions) on the long‐term outcome. A new strategy using combined and comparative data analyses helped overcome this challenge. This review presents the mechanisms and relevant principles that are necessary for the identification of meaningful clinical associations by combining data from different data sources, and serves as a blueprint for future analyses of rare disease registries. [ABSTRACT FROM AUTHOR]

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 modeling (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 counseling 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. [ABSTRACT FROM AUTHOR]