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Estimation of response probability in correlated binary data: A new approach

Publication ,  Journal Article
Jung, SH; Ahn, C
Published in: Drug Information Journal
2000

We consider analysis of binary observations from multiple sites of each subject. In this case, observations from the same subject tend to be correlated. In estimating the common response probability in correlated binary data, two weighting systems have been most popular: equal weights to sites, and equal weights to subjects. When the number of sites varies subject by subject, performance of these two weighting systems depends on the extent of correlation among sites within each subject. In this paper, we describe anew weighting method that minimizes the variance of the estimator. We apply these methods to data from a study involving an enzymatic diagnostic test to illustrate the estimation of the sensitivity and the specificity of periodontal diagnostic tests. Simulation studies were conducted to compare the performance of the new estimator with that of other estimators.

Duke Scholars

Published In

Drug Information Journal

Publication Date

2000

Volume

34

Issue

2

Start / End Page

599 / 604

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3214 Pharmacology and pharmaceutical sciences
  • 1117 Public Health and Health Services
  • 0104 Statistics
 

Citation

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Jung, S. H., & Ahn, C. (2000). Estimation of response probability in correlated binary data: A new approach. Drug Information Journal, 34(2), 599–604.
Jung, S. H., and C. Ahn. “Estimation of response probability in correlated binary data: A new approach.” Drug Information Journal 34, no. 2 (2000): 599–604.
Jung SH, Ahn C. Estimation of response probability in correlated binary data: A new approach. Drug Information Journal. 2000;34(2):599–604.
Jung, S. H., and C. Ahn. “Estimation of response probability in correlated binary data: A new approach.” Drug Information Journal, vol. 34, no. 2, 2000, pp. 599–604.
Jung SH, Ahn C. Estimation of response probability in correlated binary data: A new approach. Drug Information Journal. 2000;34(2):599–604.

Published In

Drug Information Journal

Publication Date

2000

Volume

34

Issue

2

Start / End Page

599 / 604

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3214 Pharmacology and pharmaceutical sciences
  • 1117 Public Health and Health Services
  • 0104 Statistics