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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.
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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
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Published on: January 8, 2020

Sample size determination and re-estimation for matched pair designs with multiple binary endpoints.

Jin Xu1, Menggang Yu

  • 1Department of Statistics and Actuarial Science, East China Normal University, Shanghai, 200241, China. jxu@stat.ecnu.edu.cn

Biometrical Journal. Biometrische Zeitschrift
|April 5, 2013
PubMed
Summary
This summary is machine-generated.

This study extends the McNemar test for multiple outcomes in cancer chemotherapy trials. It provides methods for noninferiority testing, sample size calculation, and blinded parameter estimation in clinical trials.

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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Biostatistics
  • Clinical Trials
  • Cancer Research

Background:

  • Simultaneous assessment of multiple binary endpoints is crucial in cancer chemotherapy trials.
  • Existing univariate McNemar tests are insufficient for multivariate endpoint analysis in matched-pair, doubly blinded studies.

Purpose of the Study:

  • To extend the univariate McNemar test to multivariate cases for doubly blinded clinical trials with matched pairs.
  • To develop a general method for testing noninferiority or equivalence of multiple endpoints.
  • To provide tools for sample size calculation and blinded parameter estimation.

Main Methods:

  • The proposed method utilizes the intersection-union principle on marginal score statistics for an asymptotic alpha-level test.
  • Power formulas and sample size calculations are derived using a numerical method accounting for endpoint correlations.
  • A blinded approach for nuisance parameter estimation is developed to maintain blinding in doubly blinded trials.

Main Results:

  • The developed multivariate method effectively tests noninferiority or equivalence for multiple binary endpoints.
  • Simulation studies demonstrate the effectiveness of the proposed statistical methods.
  • The approach facilitates accurate sample size determination and re-estimation, including through internal pilot studies.

Conclusions:

  • The extended multivariate McNemar test offers a robust framework for analyzing multiple endpoints in cancer chemotherapy trials.
  • The proposed methods support rigorous statistical evaluation while preserving the integrity of doubly blinded clinical trials.
  • This work provides essential statistical tools for designing and conducting complex clinical trials with multiple outcomes.