The performance of diagnostic tests is often evaluated by estimating their sensitivity and specificity with respect to a traditionally accepted standard test regarded as a "gold standard" in making the diagnosis. Correlated samples of binary data arise in many fields of application. The fundamental unit for analysis is occasionally the site rather than the subject in site-specific studies. Statistical methods that take into account the within-subject corelation should be employed to estimate the sensitivity and the specificity of diagnostic tests since site-specific results within a subject can be highly correlated. I introduce several statistical methods for the estimation of the sensitivity and the specificity of site-specific diagnostic tests. I apply these techniques to the data from a study involving an enzymatic diagnostic test to motivate and illustrate the estimation of the sensitivity and the specificity of period-ontal diagnostic tests. I present results from a simulation study for the estimation of diagnostic sensitivity when the data are correlated within subjects. Through a simulation study, I compare the performance of the binomial estimator p̂BE, the ratio estimator p̂RE, the weighted estimator p̂WE, the intracluster correlation estimator p̂IC, and the generalized estimating equation (GEE) estimator p̂GEE in terms of biases, observed variances, mean squared errors (MSE), relative efficiencies of their variances and 95 per cent coverage proportions. I recommend using p̂BE when ρ = 0. I recommend use of the weighted estimator p̂WE when ρ = 0.6. When ρ = 0.2 or ρ = 0.4, and the number of subjects is at least 30, p̂GEE performs well.
- Correlated binary data
- Site-specific sensitivity
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty