title: Dirichlet Component Regression and its Applications to Psychiatric Data.
abstract: We describe a Dirichlet multivariable regression method useful for modeling data representing components as a percentage of a total. This model is motivated by the unmet need in psychiatry and other areas to simultaneously assess the effects of covariates on the relative contributions of different components of a measure. The model is illustrated using the Positive and Negative Syndrome Scale (PANSS) for assessment of schizophrenia symptoms which, like many other metrics in psychiatry, is composed of a sum of scores on several components, each in turn, made up of sums of evaluations on several questions. We simultaneously examine the effects of baseline socio-demographic and co-morbid correlates on all of the components of the total PANSS score of patients from a schizophrenia clinical trial and identify variables associated with increasing or decreasing relative contributions of each component. Several definitions of residuals are provided. Diagnostics include measures of overdispersion, Cook's distance, and a local jackknife influence metric.
authors: Division of Biostatistics, Department of Epidemiology and Public Health Yale University, New Haven, CT 06520.
authors: Ralitza
authors: R
authors: Gueorguieva
authors: Robert
authors: R
authors: Rosenheck
authors: Daniel
authors: D
authors: Zelterman