A capture–recapture approach for screening using two diagnostic tests with availability of disease status for the test positives only
A capture–recapture approach for screening using two diagnostic tests with availability of disease status for the test positives only
The article considers screening human populations with two screening tests. If any of the two tests is positive, then full evaluation of the disease status is undertaken; however, if both diagnostic tests are negative, then disease status remains unknown. This procedure leads to a data constellation in which, for each disease status, the 2 × 2 table associated with the two diagnostic tests used in screening has exactly one empty, unknown cell. To estimate the unobserved cell counts, previous approaches assume independence of the two diagnostic tests and use specific models, including the special mixture model of Walter or unconstrained capture–recapture estimates. Often, as is also demonstrated in this article by means of a simple test, the independence of the two screening tests is not supported by the data. Two new estimators are suggested that allow associations of the screening test, although the form of association must be assumed to be homogeneous over disease status. These estimators are modifications of the simple capture–recapture estimator and easy to construct. The estimators are investigated for several screening studies with fully evaluated disease status in which the superior behavior of the new estimators compared to the previous conventional ones can be shown. Finally, the performance of the new estimators is compared with maximum likelihood estimators, which are more difficult to obtain in these models. The results indicate the loss of efficiency as minor
capture–recapture, capture–recapture estimator under screening test dependence, diagnostic test accuracy, testing independence
212-221
Böhning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
Patilea, Valentin
a96956f4-78aa-4d4e-b369-3bd5c5ce7140
March 2008
Böhning, Dankmar
1df635d4-e3dc-44d0-b61d-5fd11f6434e1
Patilea, Valentin
a96956f4-78aa-4d4e-b369-3bd5c5ce7140
Böhning, Dankmar and Patilea, Valentin
(2008)
A capture–recapture approach for screening using two diagnostic tests with availability of disease status for the test positives only.
Journal of the American Statistical Association, 103 (481), .
(doi:10.1198/016214507000000383).
Abstract
The article considers screening human populations with two screening tests. If any of the two tests is positive, then full evaluation of the disease status is undertaken; however, if both diagnostic tests are negative, then disease status remains unknown. This procedure leads to a data constellation in which, for each disease status, the 2 × 2 table associated with the two diagnostic tests used in screening has exactly one empty, unknown cell. To estimate the unobserved cell counts, previous approaches assume independence of the two diagnostic tests and use specific models, including the special mixture model of Walter or unconstrained capture–recapture estimates. Often, as is also demonstrated in this article by means of a simple test, the independence of the two screening tests is not supported by the data. Two new estimators are suggested that allow associations of the screening test, although the form of association must be assumed to be homogeneous over disease status. These estimators are modifications of the simple capture–recapture estimator and easy to construct. The estimators are investigated for several screening studies with fully evaluated disease status in which the superior behavior of the new estimators compared to the previous conventional ones can be shown. Finally, the performance of the new estimators is compared with maximum likelihood estimators, which are more difficult to obtain in these models. The results indicate the loss of efficiency as minor
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Published date: March 2008
Keywords:
capture–recapture, capture–recapture estimator under screening test dependence, diagnostic test accuracy, testing independence
Organisations:
Statistics, Statistical Sciences Research Institute, Primary Care & Population Sciences
Identifiers
Local EPrints ID: 210443
URI: http://eprints.soton.ac.uk/id/eprint/210443
ISSN: 0162-1459
PURE UUID: 0f7f4249-bf6a-4462-b19d-da74f00d0bd5
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Date deposited: 08 Feb 2012 15:29
Last modified: 15 Mar 2024 03:39
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Author:
Valentin Patilea
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