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Performance of a general location model with an ignorable missing‐data assumption in a multivariate mental health services study
Author(s) -
Belin Thomas R.,
Hu MingYi,
Young Alexander S.,
Grusky Oscar
Publication year - 1999
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19991130)18:22<3123::aid-sim277>3.0.co;2-2
Subject(s) - categorical variable , overfitting , missing data , multivariate statistics , computer science , mental health , outcome (game theory) , variety (cybernetics) , econometrics , data mining , statistics , data science , machine learning , psychology , artificial intelligence , psychiatry , mathematics , mathematical economics , artificial neural network
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. Copyright © 1999 John Wiley & Sons, Ltd.