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Related Concept Videos

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...
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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.
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The t-test is a statistical method used to compare the sample mean with a population mean or compare two means from two data sets. The test statistic is calculated from the standard deviation, mean, and number of measurements in the data set at a selected confidence interval and then compared to a table of critical values at this confidence level. If the test statistic is smaller than the critical value, the null hypothesis is accepted. In this case, we state that the difference between the...
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Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
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Noninferiority testing beyond simple two-sample comparison.

Yi Tsong1, Wen-Jen Chen

  • 1Office of Biostatistics/Office of Translational Sciences, CDER, US FDA, Silver Spring, MD 20993-0002, USA. yi.tong@fda.hhs.gov

Journal of Biopharmaceutical Statistics
|March 17, 2007
PubMed
Summary

This study evaluates two noninferiority trial approaches for new drug applications. The generalized historical control method offers more flexibility for complex trial designs compared to the cross-trial comparison approach.

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Area of Science:

  • Clinical Trials
  • Biostatistics
  • Drug Development

Background:

  • New drug applications often require complex clinical trials beyond simple two-sample studies.
  • Noninferiority trials commonly use generalized historical control or cross-trial comparison approaches.
  • Existing literature often focuses on the statistical properties of these approaches in simple comparative settings.

Purpose of the Study:

  • To evaluate the limitations of generalized historical control and cross-trial comparison approaches in clinical trials.
  • To assess the suitability of these methods for complex trial designs, including superiority switching, group sequential designs, and multi-center trials.
  • To compare the flexibility and applicability of the two noninferiority testing strategies.

Main Methods:

  • Comparative analysis of two noninferiority trial design approaches: generalized historical control (delta-margin) and cross-trial comparison (lambda-margin).
  • Evaluation of statistical properties concerning superiority/noninferiority switching, group sequential designs, constancy assumptions, and multi-center trial analyses.
  • Assessment of data transformation and historical analysis method changes.

Main Results:

  • The cross-trial comparison approach demonstrates significant limitations with complex trial designs and analyses, often restricted to simple two-sample comparisons with normal approximation.
  • The generalized historical control approach exhibits greater flexibility for more complicated trial designs and analyses, particularly when the variability of the noninferiority margin (delta) is minimal.
  • Specific limitations of both approaches were identified regarding constancy assumptions, test dependency, and homogeneity analysis in multi-center trials.

Conclusions:

  • The generalized historical control approach is more adaptable for complex clinical trial designs compared to the cross-trial comparison method.
  • Researchers should carefully consider the chosen noninferiority approach based on the specific clinical trial design and statistical analysis requirements.
  • Further investigation into the properties of these methods under various complex scenarios is warranted for robust drug development.