Statistical considerations in infectious disease randomized controlled trials

Matthew J. Hayat

Research output: Chapter in Book/Report/Conference proceedingChapter

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.

Original languageEnglish (US)
Title of host publicationMathematical and Statistical Modeling for Emerging and Re-emerging Infectious Diseases
PublisherSpringer International Publishing
Pages303-312
Number of pages10
ISBN (Electronic)9783319404134
ISBN (Print)9783319404110
DOIs
StatePublished - Jan 1 2016
Externally publishedYes

Keywords

  • Causal inference
  • Cluster randomized trial
  • Randomized controlled trial
  • Therapy
  • Treatment efficacy

ASJC Scopus subject areas

  • Mathematics(all)
  • Medicine(all)

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  • Cite this

    Hayat, M. J. (2016). Statistical considerations in infectious disease randomized controlled trials. In Mathematical and Statistical Modeling for Emerging and Re-emerging Infectious Diseases (pp. 303-312). Springer International Publishing. https://doi.org/10.1007/978-3-319-40413-4_18