Innovative research methods for studying treatments for rare diseases: methodological review. (24th November 2014)

• Record Type: Journal Article
• Authors: Gagne, Joshua J; Thompson, Lauren; O'Keefe, Kelly; Kesselheim, Aaron S
• Is Part Of: BMJ. Volume 349(2014)
• Publication Date: 2014-11-24
• Subjects: Medicine -- Periodicals; Medicine -- Periodicals; Medicine; Periodicals; 610
• DOI: 10.1136/bmj.g6802
• Languages: English
• ISSNs: 0007-1447

Abstract

Abstract : Objective To examine methods for generating evidence on health outcomes in patients with rare diseases. Design Methodological review of existing literature. Setting PubMed, Embase, and Academic Search Premier searched for articles describing innovative approaches to randomized trial design and analysis methods and methods for conducting observational research in patients with rare diseases. Main outcome measures We assessed information related to the proposed methods, the specific rare disease being studied, and outcomes from the application of the methods. We summarize methods with respect to their advantages in studying health outcomes in rare diseases and provide examples of their application. Results We identified 46 articles that proposed or described methods for studying patient health outcomes in rare diseases. Articles covered a wide range of rare diseases and most (72%) were published in 2008 or later. We identified 16 research strategies for studying rare disease. Innovative clinical trial methods minimize sample size requirements (n=4) and maximize the proportion of patients who receive active treatment (n=2), strategies crucial to studying small populations of patients with limited treatment choices. No studies describing unique methods for conducting observational studies in patients with rare diseases were identified. Conclusions Though numerous studies apply unique clinical trial designs and considerations to assess patient health outcomes in rare diseases, less attention has been paid to innovative methods for studying rare diseases using observational data.