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Estimating Disease Prevalence from Clinical Data Using Capture-Recapture
Carol Conell, Division of Research, Kaiser Permanente Northern California
Capture-recapture analysis provides a natural way to model disease prevalence using the longitudinal diagnostic and treatment data from Electronic Medical Records. This technique corrects for ascertainment bias due to not yet diagnosed incident cases as well as cases not being actively treated.
This paper shows how SAS® can be used to transform routinely generated data into a series of repeated measures of disease identification and to apply capture-recapture analysis to the resulting series. A simple example combining survey and clinical data on alcohol and substance use disorders is presented.
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