title: Performance of a general location model with an ignorable missing-data assumption in a multivariate mental health services study.
abstract: In a study of the impact of case management teams in a publicly funded mental health programme, mental health patients were interviewed about a variety of outcomes suggestive of successful community adaptation, such as support from family and friends and avoidance of legal problems. Because outcome data were missing for a number of patients, a follow-up study was carried out to obtain this information form previous non-responders whenever possible. Because the data of interest were multivariate and included both continuous and categorical variables, a candidate approach for handling incomplete data in the absence of follow-up data would have been to fit a general location model, presumably with log-linear constraints on cell probabilities to avoid overfitting of the data. Here, we use available follow-up data to investigate the performance of a series of general location models with ignorable non-response. We note some problems with this approach and embed the discussion of this example in a broader consideration of the role of ignorable and non-ignorable models in applied research.
authors.affiliation: UCLA Department of Psychiatry and Biobehavioral Sciences, Los Angeles, CA, USA. tbelin@mednet.ucla.edu
authors.forename: T R
authors.initials: TR
authors.lastname: Belin
authors.forename: M Y
authors.initials: MY
authors.lastname: Hu
authors.forename: A S
authors.initials: AS
authors.lastname: Young
authors.forename: O
authors.initials: O
authors.lastname: Grusky
mesh_terms: D005260:Female; D005500:Follow-Up Studies; D006297:Health Services Accessibility / Q000706:statistics & numerical data; D006306:Health Surveys; D006801:Humans; D007407:Interviews as Topic; D008297:Male; D008390:Markov Chains; D008605:Mental Health Services / Q000706:statistics & numerical data*; D015233:Models, Statistical; D009010:Monte Carlo Method; D015999:Multivariate Analysis