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Related Concept Videos

Wilcoxon Signed-Ranks Test for Matched Pairs01:09

Wilcoxon Signed-Ranks Test for Matched Pairs

The Wilcoxon signed-rank test for matched pairs evaluates the null hypothesis by combining the ranks of differences with their signs. It essentially tests whether the median of the differences in a population of matched pairs is zero. Since the test incorporates more information than the sign test, it generally yields more trustable conclusions. This test also does not require the data to follow a normal distribution, but two conditions must be met for it to be applicable: (1) the data must...
Sign Test for Matched Pairs01:17

Sign Test for Matched Pairs

The sign test for matched pairs offers a robust method for comparing two paired samples, often for the effects of an intervention in one of them. This method is very useful in situations where the underlying distribution of the data is unknown. The test compares two related samples—often pre- and post-treatment measurements on the same subjects—to determine if there are significant differences in their median values.
To conduct the sign test, we first calculate the differences in value between...
Test for Homogeneity01:23

Test for Homogeneity

The goodness–of–fit test can be used to decide whether a population fits a given distribution, but it will not suffice to decide whether two populations follow the same unknown distribution. A different test, called the test for homogeneity, can be used to conclude whether two populations have the same distribution. To calculate the test statistic for a test for homogeneity, follow the same procedure as with the test of independence. The hypotheses for the test for homogeneity can be stated as...
Bonferroni Test01:10

Bonferroni Test

The Bonferroni test is a statistical test named after Carlo Emilio Bonferroni, an Italian mathematician best known for Bonferroni inequalities. This statistical test is a type of multiple comparison test to determine which means are different than the rest. Bonferroni test can minimize the Type 1 error by reducing the significance level alpha, which otherwise increases with sample pairs.
The means of different samples are first paired in all possible combinations.
The null hypothesis of the...
McNemar's Test01:23

McNemar's Test

McNemar's Test is a nonparametric statistical test used to determine if there is a significant difference in proportions between two related groups when the outcome is binary (e.g., yes/no, success/failure). It is beneficial when we have paired data, such as pre-test/post-test designs, where the same subjects are measured under two different conditions. The test is named after the statistician Quinn McNemar, who introduced it in 1947. It is commonly used in situations where subjects are...
Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
It would be easy to compare two samples using a significance alpha level of 0.05. In other words, there is only one sample pair to be compared. However, it would be difficult to identify a significantly different sample if the number...

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Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
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Non-inferiority tests for clustered matched-pair data.

Jun-Mo Nam1, Deukwoo Kwon

  • 1Biostatistics Branch, DCEG, National Cancer Institute, National Institutes of Health, DHHS, Rockville, MD 20852-7244, U.S.A. namj@mail.nih.gov

Statistics in Medicine
|March 28, 2009
PubMed
Summary

For clustered matched-pair studies, standard non-inferiority tests may fail. Adjusted score tests and modified Obuchowski

Area of Science:

  • Biostatistics
  • Clinical Trials

Background:

  • Non-inferiority tests for matched-pair data assume pair independence, which is violated in clustered designs.
  • Clustered data introduces intra-cluster correlation, necessitating adjustments to standard statistical tests.

Purpose of the Study:

  • To evaluate the performance of adjusted non-inferiority tests for clustered matched-pair data.
  • To compare adjusted score, Wald-type, and modified Obuchowski's methods against a moments estimate test.
  • To assess Type 1 error rates and statistical power under various correlation structures and small cluster sizes.

Main Methods:

  • Simulations were conducted to compare statistical tests under different correlation structures.
  • Evaluated Type 1 error rates and power for adjusted score, Wald-type, and modified Obuchowski's methods.

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  • Compared these methods against a non-inferiority test based on a method of moments estimate.

    • Main Results:

    • Adjusted score tests and modified Obuchowski's method showed accurate Type 1 error rates, similar to the moments estimate test.
    • The moments estimate test exhibited inaccurate Type 1 error rates with small cluster numbers (≤25) and low response rates (≤20%).
    • Adjusted score, moments estimate, and modified tests demonstrated comparable statistical power, while the adjusted Wald-type test was overly anti-conservative.

    Conclusions:

    • Adjusted score tests and modified Obuchowski's method are reliable for non-inferiority testing in clustered matched-pair studies.
    • The number of clusters significantly impacts the accuracy of Type 1 error rates and power.
    • Sufficiently large cluster numbers are crucial for robust non-inferiority study design in clustered matched-pair settings.