Statistical Considerations in Infectious Disease Randomized Controlled Trials

Matthew J. Hayat ()
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Matthew J. Hayat: Georgia State University, School of Public Health

A chapter in Mathematical and Statistical Modeling for Emerging and Re-emerging Infectious Diseases, 2016, pp 303-312 from Springer

Abstract: Abstract Randomized controlled trials (RCT) provide the highest standard of evidence available for assessing treatment efficacy. Causal inferences are enabled and effects may be directly attributed to a treatment. The nature of infectious disease presents challenges to the design, conduct, and analysis of a trial for a new drug or therapy. Many of these challenges are statistical in nature and can be addressed with modern methods for planning and analyzing RCT data. In this chapter, some of these challenges are described and reviewed. Modern statistical modeling methods for analysis of correlated data are covered. Some challenges with sample size determination are outlined and updated methods for data monitoring, interim, and subgroup analyses detailed. Also, discernment is made between multisite and cluster randomized trials. Recommendations for best practices are included.

Keywords: Randomized controlled trial; Treatment efficacy; Causal inference; Therapy; Cluster randomized trial (search for similar items in EconPapers)
Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-40413-4_18

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DOI: 10.1007/978-3-319-40413-4_18

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