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A configural analysis of psychiatric diagnostic stereotypes
In: Systems research and behavioral science: the official journal of the International Federation for Systems Research, Band 8, Heft 3, S. 211-219
ISSN: 1099-1743
Multiple covariance analysis by the general least squares regression method
In: Behavioral science, Band 17, Heft 3, S. 313-320
Analysis of Data from a Controlled Repeated Measurements Design with Baseline-Dependent Dropouts
In: Methodology: European journal of research methods for the behavioral and social sciences, Band 3, Heft 2, S. 58-66
ISSN: 1614-2241
Abstract. Differences in mean rates of change are of primary interest in many controlled treatment evaluation studies. Generalized linear mixed model (GLMM) procedures are widely conceived to be the preferred method of analysis for repeated measurement designs when there are missing data due to dropouts, but systematic dependence of the dropout probabilities on antecedent or concurrent factors poses a problem for testing the significance of differences in mean rates of change across time in such designs. Controlling for the dependence of dropout probabilities on baseline values poses a special problem because a theoretically correct GLMM random-effects model does not permit including the same baseline score as both covariate and dependent variable. Monte Carlo methods are used herein to evaluate the actual Type 1 error rates and power resulting from two commonly-illustrated GLMM random-effects model formulations for testing the GROUPS × TIMES linear interaction effect in group-randomized repeated measurements designs. The two GLMM model formulations differ by either including or not including baseline scores as a covariate in the attempt to control for imbalance caused by the baseline-dependent dropouts. Results from those analyses are compared with results from a simpler two-stage analysis in which dropout-weighted slope coefficients fitted separately to the available repeated measurements for each subject serve as the dependent variable for an ordinary ANCOVA test for difference in mean rates of change. The Monte Carlo results confirm modestly superior Type 1 error protection but quite superior power for the simpler two-stage analysis of dropout-weighted slope coefficients as compared with those for either of the more mathematically complex GLMM analyses.
A pattern probability model for the classification of psychiatric patients
In: Systems research and behavioral science: the official journal of the International Federation for Systems Research, Band 8, Heft 2, S. 108-116
ISSN: 1099-1743
Models for medical diagnosis
In: Systems research and behavioral science: the official journal of the International Federation for Systems Research, Band 6, Heft 2, S. 134-141
ISSN: 1099-1743
Decisions about drug therapy II: Expert opinion in a hypothetical situation
In: Behavioral science, Band 17, Heft 4, S. 349-360
Testing the Significance of Difference in Average Rates of Change in Controlled Longitudinal Studies With High Dropout Rates
In: Methodology: European journal of research methods for the behavioral and social sciences, Band 5, Heft 2, S. 46-54
ISSN: 1614-2241
This article concerns methodology for testing the significance of differences in mean rates of change in controlled repeated measurements designs with limited sample sizes, autoregressive error structures, nonlinear patterns of underlying true mean change, dropout rates exceeding 50%, plus other missing data. Each of these is problematic for ordinary repeated measures analysis of variance, and a complex generalized linear mixed model formulation popularly advocated for the ability to deal with autoregressive error structures and missing data is shown to perform poorly in such circumstances. Monte Carlo simulation methods confirm that simple two-stage analyses of dropout-weighted linear slope coefficients provide conservative Type 1 error protection, although adequate power requires the presence of large treatment effects in studies with the limited sample sizes and high proportions of missing data. No other analysis has been documented to provide both conservative Type 1 error protection and competitive power under similarly taxing conditions.
Predicting the Posttreatment Depressive State of an Alcoholic Patient
In: International journal of the addictions, Band 25, Heft 10, S. 1263-1273
Alcohol-Induced Euphoria: Alcoholics Compared to Nonalcoholics
In: International journal of the addictions, Band 17, Heft 5, S. 823-845