Online Program

Strategies for Causal Inference in Rare Diseases (306773)

Keywords: causal inference, rare diseases, natural history

This presentation will give an overview of how researchers can measure the impact of medical interventions on patients with rare diseases using natural history of disease registries or electronic healthcare databases. In the united states, rare diseases affect 30 million patients in approximately 7000 disease areas. Causal inference in this area is challenged by several factors: small patient population, variable genotype or phenotype, and uncharacterized or lengthy disease pathways. These challenges are not specific to the rare disease setting as they apply more largely to answering causal inference questions in the era of precision medicine where public health decisions rely on subgroups of sample sizes. Over the past few years, a few algorithms were published to guide researchers to appropriate study design in these settings. In 2019, the Food and Drug Administration also issued multiple rare disease guidance documents. This presentation will give an overview of best design practices and innovative analysis methods, and illustrate their use including a case study evaluating impact of liver transplant on patients with urea cycle disorder.