A Model for Evaluating Sensitivity and Specificity for Correlated Diagnostic Tests in Efficacy Studies with an Imperfect Reference Test

Abstract The purpose of a diagnostic efficacy study is to evaluate and compare the sensitivities and specificities of several diagnostic tests. Usually the diagnostic tests are correlated conditional on disease status, and the reference test is subject to error. In the Chlamydia trachomatis study, five screening tests for detecting chlamydia in endocervical specimens were compared. The five tests are correlated, and the reference test (the cell culture test) has less than 100% sensitivity. The conventional method ignores both the correlations between the tests and the misclassification of the reference test and thus cannot provide a valid analysis. We propose a model to evaluate and compare the sensitivities and specificities of correlated diagnostic tests when there is either an imperfect reference test or even no reference test. The model also can estimate the effects of covariates. It is a generalized linear mixed model with two unobserved variables, one continuous and one dichotomous. We use a hybrid ...

[1]  A Hadgu,et al.  Sensitivity and specificity for correlated observations. , 1992, Statistics in medicine.

[2]  S D Walter,et al.  Estimation of test error rates, disease prevalence and relative risk from misclassified data: a review. , 1988, Journal of clinical epidemiology.

[3]  R. Darrell Bock,et al.  Fitting a response model forn dichotomously scored items , 1970 .

[4]  W. Blattner,et al.  A method for predicting individual HIV infection status in the absence of clinical information. , 1988, AIDS research and human retroviruses.

[5]  T. Louis.  Finding the Observed Information Matrix When Using the EM Algorithm , 1982 .

[6]  P. Hoeprich,et al.  Infectious diseases : a treatise of infectious processes , 1994 .

[7]  M. Espeland,et al.  Using latent class models to characterize and assess relative error in discrete measurements. , 1989, Biometrics.

[8]  A. Washington,et al.  Chlamydia trachomatis infections in the United States. What are they costing us? , 1987, JAMA.

[9]  S. Zeger,et al.  Longitudinal data analysis using generalized linear models , 1986 .

[10]  G. Schwarz.  Estimating the Dimension of a Model , 1978 .

[11]  D. Rubin.  INFERENCE AND MISSING DATA , 1975 .

[12]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[13]  Qing Liu,et al.  A note on Gauss—Hermite quadrature , 1994 .

[14]  D. Rindskopf,et al.  The value of latent class analysis in medical diagnosis. , 1986, Statistics in medicine.

[15]  J S Uebersax,et al.  Latent class analysis of diagnostic agreement. , 1990, Statistics in medicine.

[16]  P. McCullagh,et al.  Generalized Linear Models , 1992 .

[17]  Irene A. Stegun,et al.  Handbook of Mathematical Functions. , 1966 .

[18]  M A Young,et al.  Evaluating diagnostic criteria: a latent class paradigm. , 1982, Journal of psychiatric research.

[19]  M. Tan,et al.  Random effects models in latent class analysis for evaluating accuracy of diagnostic tests. , 1996, Biometrics.

[20]  R. D. Bock,et al.  Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm , 1981 .

[21]  W. Grove,et al.  A latent trait finite mixture model for the analysis of rating agreement. , 1993, Biometrics.

[22]  R. D. Bock,et al.  Marginal maximum likelihood estimation of item parameters , 1982 .