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Statistical methods for comparing test positivity rates between countries: Which method should be used and why?

James B Hittner1, Folorunso O Fasina2

  • 1Department of Psychology, College of Charleston, Charleston, SC, USA.

Methods (San Diego, Calif.)
|March 21, 2021
PubMed
Summary
This summary is machine-generated.

Comparing COVID-19 test positivity rates across countries requires careful statistical methods. Bayesian approaches offer more accurate inferences than frequentist tests, especially when rates are similar, preventing potential public health decision errors.

Keywords:
BayesianCOVID-19FrequentistStatistical methodsTP ratesTest positivity

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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • The test positivity (TP) rate is a key metric for assessing COVID-19 burden.
  • International comparisons of TP rates are crucial for understanding disease morbidity.
  • Statistical methods for comparing TP rates include frequentist and Bayesian approaches.

Purpose of the Study:

  • To compare the performance of frequentist and Bayesian statistical methods for analyzing COVID-19 test positivity rates across countries.
  • To evaluate how different magnitudes and similarities of TP rates influence statistical inference.

Main Methods:

  • Utilized COVID-19 data from Our World in Data.
  • Compared two pairs of countries: Bolivia vs. United States (disparate rates) and South Korea vs. Uruguay (similar small rates).
  • Applied two frequentist tests (asymptotic z-test, 'N-1' chi-square test) and a Bayesian method for comparing two proportions.

Main Results:

  • For disparate TP rates (Bolivia vs. US), both frequentist and Bayesian methods showed significant differences.
  • For very similar small TP rates (South Korea vs. Uruguay), frequentist tests indicated a significant contrast, while the Bayesian method suggested practical equivalence.
  • The Bayesian approach provided results more consistent with the observed trivial differences in TP rates for South Korea and Uruguay.

Conclusions:

  • Frequentist tests can lead to erroneous interpretations when TP rates are highly similar.
  • Bayesian methods provide more accurate inferences for comparing TP rates, especially when they are close in magnitude.
  • Accurate statistical inference is vital to avoid costly public health and policy errors.