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Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
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Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
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Hypothesis testing and sample size considerations for the test-negative design.

Yanan Huo1, Yang Yang2, M Elizabeth Halloran3,4

  • 1Gilead Sciences, Inc, Foster City, CA, USA.

BMC Medical Research Methodology
|July 16, 2024
PubMed
Summary
This summary is machine-generated.

The score test offers superior power for vaccine effectiveness (VE) studies using the test-negative design (TND) compared to Wald tests, especially when vaccines are highly effective. This method enhances sample size calculations for both TND and case-control studies.

Keywords:
Case-control studyContinuity correctionSample sizeScore testTest-negative designVaccines

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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • The test-negative design (TND) is an observational study method for evaluating vaccine effectiveness (VE).
  • TND enrolls individuals undergoing diagnostic testing for a specific disease during routine care.
  • VE in TND is calculated as 1 minus the adjusted odds ratio of testing positive versus negative between vaccinated and unvaccinated individuals.

Purpose of the Study:

  • To evaluate the statistical power and sample size calculations for the test-negative design (TND).
  • To compare the performance of different hypothesis-testing procedures within TND and case-control studies, particularly under high vaccine effectiveness.
  • To recommend optimal statistical approaches for designing and analyzing VE studies using TND.

Main Methods:

  • Simulation studies were conducted to explore three hypothesis-testing procedures: standard Wald test, continuity-corrected Wald test, and score test.
  • These tests were analyzed within the framework of logistic regression models for both case-control and TND studies.
  • Sample size calculations were investigated for each testing procedure.

Main Results:

  • The standard Wald test demonstrated poor performance in both TND and case-control studies when vaccine effectiveness was high, due to low or zero vaccinated test-positive cases.
  • Continuity corrections improved variance stability but introduced bias.
  • The score test exhibited superior performance by pooling variance under the null hypothesis of no group differences.

Conclusions:

  • A score-based approach is recommended for designing and analyzing both case-control and TND studies.
  • A modified TND score sample size calculation is proposed to address variability in the control-to-case ratio.
  • This research clarifies the distinct data-generating mechanisms of TND compared to case-control studies, emphasizing passive control recruitment.