### Abstract

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 p̂_{BE}, the ratio estimator p̂_{RE}, the weighted estimator p̂_{WE}, the intracluster correlation estimator p̂_{IC}, and the generalized estimating equation (GEE) estimator p̂_{GEE} in terms of biases, observed variances, mean squared errors (MSE), relative efficiencies of their variances and 95 per cent coverage proportions. I recommend using p̂_{BE} when ρ = 0. I recommend use of the weighted estimator p̂_{WE} when ρ = 0.6. When ρ = 0.2 or ρ = 0.4, and the number of subjects is at least 30, p̂_{GEE} performs well.

Original language | English (US) |
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Pages (from-to) | 793-807 |

Number of pages | 15 |

Journal | Biometrical Journal |

Volume | 39 |

Issue number | 7 |

State | Published - Dec 1 1997 |

### Keywords

- Correlated binary data
- Simulation
- Site-specific sensitivity

### ASJC Scopus subject areas

- Statistics and Probability
- Statistics, Probability and Uncertainty