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An exact test for comparing two predictive values in small-size clinical trials.

Kanae Takahashi1, Kouji Yamamoto2

  • 1Department of Medical Statistics, Graduate School of Medicine, Osaka City University, Osaka, Japan.

Pharmaceutical Statistics
|October 24, 2019
PubMed
Summary

This study introduces an exact test for comparing diagnostic test predictive values in small clinical trials. The new method strictly controls the type 1 error rate, ensuring reliable results.

Keywords:
negative predictive valuepermutation testpositive predictive valuesmall-size clinical trial

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Diagnostic Test Evaluation

Background:

  • Predictive values are key metrics for diagnostic test performance.
  • Existing methods for comparing predictive values in paired designs often rely on large sample theory.
  • These methods may lead to inflated type 1 error rates in small clinical trials.

Purpose of the Study:

  • To propose an exact statistical test for assessing the equality of predictive values in paired designs.
  • To ensure strict control of the type 1 error rate in small-size clinical trials.
  • To evaluate the performance of the proposed exact test against existing methods.

Main Methods:

  • Development of an exact statistical test for comparing predictive values.
  • Conducting simulation studies to assess type 1 error rates and statistical power.
  • Comparison of the proposed exact test with methods based on large-sample theory.

Main Results:

  • The proposed exact test accurately calculates P values.
  • Empirical type 1 error rates for the proposed test remained below the significance level across simulations.
  • The empirical power of the proposed test was comparable to existing large-sample methods.

Conclusions:

  • The proposed exact test effectively controls the type 1 error rate in small clinical trials.
  • This method is valuable when strict control of type 1 error is critical.
  • The exact test offers a reliable alternative for small-sample studies evaluating diagnostic test equality.