A difficulty in the interpretation of the reliability/stability of a lifetime diagnosis of mental disorders is the lack of a theoretical perspective. A model expressed in terms of the three unknowns-sensitivity, specificity and true base rate - is problematic due to the lack of a "gold standard", so that only two of these unknowns can be estimated. We extend this model to allow for clinical covariates that increase the likelihood that a positive case at Time 1 will be positive at Time 2. Under the assumption that all observed cases are true cases at the highest covariate values, we obtain a direct estimate of the sensitivity, so that all unknowns can be estimated. Moreover, we then calculate the likelihood that an observed case with given covariate levels is in fact a true case. The implications of diagnostic error for the fitting of genetic models are given. These methods are applied to stability data collected as part of the NIMH Psychobiology of Depression Program. A total of 1,629 relatives have been assessed with interviews separated by a 6-year interval. A logistic function was used to model the stability in relatives with an initial lifetime diagnosis of affective disorders. We discuss the use of these techniques in genetic models to increase information by defining an ordinal phenotype, use multiple assessments to minimize the impact of diagnostic error and increase the heritability, and utilize clinical covariates to model the certainty of diagnosis.
|Number of pages||6|
|Journal||European Archives of Psychiatry and Clinical Neuroscience|
|State||Published - Nov 1993|
- Diagnostic stability
- Genetics of affective disorders
- Psychiatric genetics