Objectives: If patients change their perspective due to treatment, this may alter the way they conceptualize, prioritize, or calibrate questionnaire items. These psychological changes, also called ... Show moreObjectives: If patients change their perspective due to treatment, this may alter the way they conceptualize, prioritize, or calibrate questionnaire items. These psychological changes, also called "response shifts," may pose a threat to the measurement of therapeutic change in patients. Therefore, it is important to test the occurrence of response shift in patients across their treatment.Methods: This study focused on self-reported psychological distress/psychopathology in a naturalistic sample of 206 psychiatric outpatients. Longitudinal measurement invariance tests were computed across treatment in order to detect response shifts.Results: Compared with before treatment, post-treatment psychopathology scores showed an increase in model fit and factor loading, suggesting that symptoms became more coherently interrelated within their psychopathology domains. Reconceptualization (depression/mood) and reprioritization (somatic and cognitive problems) response shift types were found in several items. We found no recalibration response shift.Conclusion: This study provides further evidence that response shift can occur in adult psychiatric patients across their mental health treatment. Future research is needed to determine whether response shift implies an unwanted potential bias in treatment evaluation or a desired cognitive change intended by treatment. Show less
Carlier, I.V.E.; Van Eeden, W.A.; Jong, K. de; Giltay, E.J.; Van Noorden, M.S.; Van der Feltz‐Cornelis, C.; ... ; Van Hemert, A.M. 2019
ObjectivesIf patients change their perspective due to treatment, this may alter the way they conceptualize, prioritize, or calibrate questionnaire items. These psychological changes, also called ... Show moreObjectivesIf patients change their perspective due to treatment, this may alter the way they conceptualize, prioritize, or calibrate questionnaire items. These psychological changes, also called “response shifts,” may pose a threat to the measurement of therapeutic change in patients. Therefore, it is important to test the occurrence of response shift in patients across their treatment.MethodsThis study focused on self‐reported psychological distress/psychopathology in a naturalistic sample of 206 psychiatric outpatients. Longitudinal measurement invariance tests were computed across treatment in order to detect response shifts.ResultsCompared with before treatment, post‐treatment psychopathology scores showed an increase in model fit and factor loading, suggesting that symptoms became more coherently interrelated within their psychopathology domains. Reconceptualization (depression/mood) and reprioritization (somatic and cognitive problems) response shift types were found in several items. We found no recalibration response shift.ConclusionThis study provides further evidence that response shift can occur in adult psychiatric patients across their mental health treatment. Future research is needed to determine whether response shift implies an unwanted potential bias in treatment evaluation or a desired cognitive change intended by treatment. Show less
Fokkema, M.; Smits, N.; Zeileis, A. Hothorn T.; Kelderman, H. 2018
Identification of subgroups of patients for whom treatment A is more effective than treatment B, and vice versa, is of key importance to the development of personalized medicine. Tree-based... Show moreIdentification of subgroups of patients for whom treatment A is more effective than treatment B, and vice versa, is of key importance to the development of personalized medicine. Tree-based algorithms are helpful tools for the detection of such interactions, but none of the available algorithms allow for taking into account clustered or nested dataset structures, which are particularly common in psychological research. Therefore, we propose the generalized linear mixed-effects model tree (GLMM tree) algorithm, which allows for the detection of treatment-subgroup interactions, while accounting for the clustered structure of a dataset. The algorithm uses model-based recursive partitioning to detect treatment-subgroup interactions, and a GLMM to estimate the random-effects parameters. In a simulation study, GLMM trees show higher accuracy in recovering treatment-subgroup interactions, higher predictive accuracy, and lower type II error rates than linear-model-based recursive partitioning and mixed-effects regression trees. Also, GLMM trees show somewhat higher predictive accuracy than linear mixed-effects models with pre-specified interaction effects, on average. We illustrate the application of GLMM trees on an individual patient-level data meta-analysis on treatments for depression. We conclude that GLMM trees are a promising exploratory tool for the detection of treatment-subgroup interactions in clustered datasets. Show less
Fokkema, M.; Smits, N.; Zeileis, A.; Hothorn, T.; Kelderman, H. 2017
Identification of subgroups of patients for whom treatment A is more effective than treatment B, and vice versa, is of key importance to the development of personalized medicine. Tree-based... Show moreIdentification of subgroups of patients for whom treatment A is more effective than treatment B, and vice versa, is of key importance to the development of personalized medicine. Tree-based algorithms are helpful tools for the detection of such interactions, but none of the available algorithms allow for taking into account clustered or nested dataset structures, which are particularly common in psychological research. Therefore, we propose the generalized linear mixed-effects model tree (GLMM tree) algorithm, which allows for the detection of treatment-subgroup interactions, while accounting for the clustered structure of a dataset. The algorithm uses model-based recursive partitioning to detect treatment-subgroup interactions, and a GLMM to estimate the random-effects parameters. In a simulation study, GLMM trees show higher accuracy in recovering treatment-subgroup interactions, higher predictive accuracy, and lower type II error rates than linear-model-based recursive partitioning and mixed-effects regression trees. Also, GLMM trees show somewhat higher predictive accuracy than linear mixed-effects models with pre-specified interaction effects, on average. We illustrate the application of GLMM trees on an individual patient-level data meta-analysis on treatments for depression. We conclude that GLMM trees are a promising exploratory tool for the detection of treatment-subgroup interactions in clustered datasets. Show less
Van de Graaf, E.S.; Borsboom, J.J.M.; Van der Sterre, G.W.; Felius, J.; Simonsz, H.J.; Kelderman, H. 2017
In clinical assessment, efficient screeners are needed to ensure low respondent burden. In thisarticle, Stochastic Curtailment (SC), a method for efficient computerized testing for... Show moreIn clinical assessment, efficient screeners are needed to ensure low respondent burden. In thisarticle, Stochastic Curtailment (SC), a method for efficient computerized testing for classificationinto two classes for observable outcomes, was extended to three classes. In a post hocsimulation study using the item scores on the Center for Epidemiologic Studies–DepressionScale (CES-D) of a large sample, three versions of SC, SC via Empirical Proportions (SC-EP),SC via Simple Ordinal Regression (SC-SOR), and SC via Multiple Ordinal Regression (SC-MOR)were compared at both respondent burden and classification accuracy. All methods wereapplied under the regular item order of the CES-D and under an ordering that was optimal interms of the predictive power of the items. Under the regular item ordering, the three methodswere equally accurate, but SC-SOR and SC-MOR needed less items. Under the optimalordering, additional gains in efficiency were found, but SC-MOR suffered from capitalization onchance substantially. It was concluded that SC-SOR is an efficient and accurate method for clinicalscreening. Strengths and weaknesses of the methods are discussed. Show less
Fokkema, M.; Smits, N.; Kelderman, H.; Penninx, B.W.J.H. 2015
