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Testing ratio of marginal probabilities in clustered matched-pair binary data

Listed author(s):
  • Yang, Zhao
  • Sun, Xuezheng
  • Hardin, James W.
Registered author(s):

    In diagnostic methods evaluation, analysts commonly focus on the relative size of the treatment difference (ratio of marginal probabilities) between a new and an existing procedures. To assess non-inferiority (a new procedure is, to a pre-specified amount, no worse than an existing procedure) via a ratio of marginal probabilities between two procedures using clustered matched-pair binary data, four ICC-adjusted test statistics are investigated. The calculation of corresponding confidence intervals is also proposed. None of the tests considered require structural within-cluster correlation or distributional assumptions. Results of an extensive Monte Carlo simulation study illustrate that the new approaches effectively maintain the nominal Type I error even for small numbers of clusters. Thus, to design and evaluate non-inferiority via a ratio of marginal probabilities, researchers are suggested to utilize designs that have small cluster-size variability (e.g., nk≤5). Finally, to illustrate the practical application of the tests and recommendations, a real clustered matched-pair collection of data is used to illustrate testing non-inferiority.

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    Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 56 (2012)
    Issue (Month): 6 ()
    Pages: 1829-1836

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    Handle: RePEc:eee:csdana:v:56:y:2012:i:6:p:1829-1836
    DOI: 10.1016/j.csda.2011.10.025
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