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

Zhao Yang, Xuezheng Sun and James W. Hardin

Computational Statistics & Data Analysis, 2012, vol. 56, issue 6, 1829-1836

Abstract: 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.

Keywords: Clustered matched-pair binary data; Diagnostic testing; Ratio; Marginal probabilities; Non-inferiority (search for similar items in EconPapers)
Date: 2012
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947311004002
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:56:y:2012:i:6:p:1829-1836

DOI: 10.1016/j.csda.2011.10.025

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().