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Related Concept Videos

Sign Test for Matched Pairs01:17

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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.
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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Bioequivalence experimental study designs play a pivotal role in testing the effectiveness of various treatments. Key among these are the repeated measures, cross-over, carry-over, and Latin square designs. In the repeated measures design, each subject receives all treatments, allowing for temporal comparisons. This type of design is useful in reducing variability but requires careful planning to avoid bias.The cross-over design, an economical method, involves sequential administration of...
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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...
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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...
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A comparative study of matched pair designs with two binary endpoints.

Yuanyuan Jiang1, Jin Xu1

  • 1Department of Statistics and Actuarial Science, East China Normal University, Shanghai, China.

Statistical Methods in Medical Research
|August 22, 2015
PubMed
Summary
This summary is machine-generated.

This study compares three methods for matched pair designs with two binary endpoints. It offers guidance on selecting the most feasible and robust approach for sample size calculations in clinical trials.

Keywords:
Binary endpointequivalencematched pair designnon-inferiority

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Methods

Background:

  • Matched pair designs are frequently used in clinical research.
  • Analyzing studies with multiple binary endpoints presents statistical challenges.
  • Existing methods for sample size calculation may not be optimal for all scenarios.

Purpose of the Study:

  • To evaluate three distinct statistical approaches for matched pair designs with two binary endpoints.
  • To develop and present methods for power approximation and sample size calculation.
  • To introduce an adaptive design incorporating sample size re-estimation.

Main Methods:

  • Derivation of power approximation and sample size formulas for each approach.
  • Development of R programs to facilitate calculations.
  • Extensive simulation studies to compare method performance.
  • Illustration using a cancer chemotherapy trial dataset.

Main Results:

  • Performance of the three approaches was assessed across various parameter ranges.
  • Guidelines were established for choosing the most feasible and robust method.
  • The adaptive design demonstrated potential for efficient sample size adjustments.

Conclusions:

  • The study provides practical recommendations for selecting appropriate statistical methods in matched pair designs.
  • The developed R programs and guidelines aid researchers in sample size determination.
  • The findings are applicable to the design and analysis of clinical trials, particularly in oncology.