Statistical concepts for research in emergency medical services

Craig D. Newgard, Roger J. Lewis

Research output: Chapter in Book/Report/Conference proceedingChapter


There is a critical need for high-quality, rigorous out-of-hospital research to drive further developments in out-of-hospital medicine and to improve the quality of care and outcomes for patients served by EMS. For this research to be valid and meaningful, it must be built on a foundation of appropriate statistical design and data interpretation. In this chapter, we address classical hypothesis testing, type I and II error, power analysis, commonly used statistical tests and their underlying assumptions, confidence intervals, multiple comparisons, subgroup analysis, intention-to-treat, interim data analysis in clinical trials, multivariable analysis, clustered data, missing data, and strategies for using statistical consultants.

Original languageEnglish (US)
Title of host publicationMedical oversight of EMS
Number of pages10
ISBN (Electronic)9781119756279
ISBN (Print)9781119756248
StatePublished - Aug 18 2021


  • Analysis
  • Confidence intervals
  • Data
  • Intention-to-treat
  • Multivariable analysis
  • P-values
  • Statistics
  • Subgroup analysis
  • Type I error
  • Type II error

ASJC Scopus subject areas

  • Medicine(all)


Dive into the research topics of 'Statistical concepts for research in emergency medical services'. Together they form a unique fingerprint.

Cite this