Improving Evidence Generation From Rare Disease Registries: Performing Variable Gap Analyses

OBJECTIVES: Rare disease registries represent an important source of knowledge to better understand the natural history and clinical trial endpoints for diseases that are often phenotypically and genetically diverse. However, registries for the same disease indication often differ in (1) scope and objectives, (2) recruitment criteria, (3) data elements, and (4) length of follow-up. Efforts to standardize the type and definitions of data elements across rare disease registries has been minimal, related in part to their geographic variability. This research explores an approach called a Variable Gap Analysis (VGA) that enables a detailed comparison of data elements and other registry characteristics across multinational rare disease registries.

METHODS: The VGA consists of four steps. Step 1: A literature review is conducted to identify international registries for the disease of interest. Step 2: A customized Feasibility Questionnaire (FQ) is developed to better understand patient characteristics, variables captured, population size, data quality, and data governance and data sharing policies. Step 3: Outreach and engagement are performed to request FQ completion and essential source documents. Step 4: The VGA is conducted, i.e., registry variables are compared with critical variables for clinical trials implementation and anticipated post authorisation study requirements.

RESULTS: Results include a detailed comparison between individual registry data elements and pre-determined variables that are critical for clinical development. Metrics of variable alignment are presented that display indication of the commonality and/or variance of data elements (i.e., exact match, logical match, partial match, missing).

CONCLUSIONS: VGA for rare-disease registries can play an important role to prepare for harmonization of data to address important clinical questions for developing new therapies to treat patients with unmet medical needs. Importantly, harmonization of data across multiple registries, despite challenges, can address research questions that require more generalizable clinical information and larger sample sizes than are available in a single rare disease registry.