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Mathematical and Statistical Modeling for Emerging and Re-emerging Infectious Diseases pp 303–312Cite as

Statistical Considerations in Infectious Disease Randomized Controlled Trials

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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.

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Author information

Authors and Affiliations

  1. School of Public Health, Georgia State University, Atlanta, GA, USA

    Matthew J. Hayat

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  1. Matthew J. Hayat
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Correspondence to Matthew J. Hayat .

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Editors and Affiliations

  1. School of Public Health, Georgia State University, Atlanta, Georgia, USA

    Gerardo Chowell

  2. Department of Mathematics, Tulane University, New Orleans, Louisiana, USA

    James M. Hyman

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Hayat, M.J. (2016). Statistical Considerations in Infectious Disease Randomized Controlled Trials. In: Chowell, G., Hyman, J. (eds) Mathematical and Statistical Modeling for Emerging and Re-emerging Infectious Diseases. Springer, Cham. https://doi.org/10.1007/978-3-319-40413-4_18

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