Dynamic and static predictive modelling of psychotherapy outcome-Comparison of two statistical approaches.

OBJECTIVE: Improving prediction abilities in the therapy process can increase therapeutic success for a variety of reasons, such as more personalised treatment or resource optimisation. The increasingly applied methods of dynamic prediction seem to be very promising for this purpose. Prediction models are usually based on static approaches of frequentist statistics. However, the application of this statistical approach has been widely criticised in this research area. Bayesian statistics has been proposed in the literature as an alternative, especially for the task of dynamic modelling. In this study, we compare the performance of predicting therapy outcome over the course of therapy between both statistical approaches. METHOD: Based on a sample of 341 patients, a logistic regression analysis was performed using both statistical approaches. Therapy success was conceptualised as reliable pre-post improvement in brief symptom inventory (BSI) scores. As predictors, we used the subscales of the Outcome Questionnaire (OQ-30) and the Helping Alliance Questionnaire (HAQ) measured every fifth session, as well as baseline BSI scores. RESULTS: The influence of the predictors during therapy differs between the frequentist and the Bayesian approach. In contrast, predictive validity is comparable with a mean area under the curve (AUC) of 0.76 in both model types. CONCLUSION: Bayesian statistic provides an innovative and useful alternative to the frequentist approach in predicting therapy outcome. The theoretical foundation is particularly well suited for dynamic prediction. Nevertheless, no differences in predictive validity were found in this study. More complex methodology as well as further research seems necessary to exploit the potential of Bayesian statistics in this area.

[Convergence of Scales to Measure Social Anxiety: An IRT Linking Approach]. / Konvergenz von Skalen zur Erfassung sozialer Ängste: Ein IRT-Linking Ansatz.

OBJECTIVE: The Liebowitz Social Anxiety Scale (LSAS) and the Social Phobia Inventory (SPIN) are established measures in the investigation of social anxiety. Furthermore, the subscale Interpersonal Sensitivity of the Brief Symptom Inventory (BSI-53) is frequently used to screen social anxiety. All three scales claim to capture the same construct, which raises the question of the convergence of these scales. To make research findings comparable by a cross-questionnaire factor (common factor), an item response theory (IRT) linking approach is used in the present study. METHODS: 64 German-speaking psychiatric patients and 295 healthy subjects completed the three questionnaires. Different IRT models, including Graded Response Models (GRM), were constructed, and their model fit compared. Regression analyses were performed based on the best-fit model. The common factor was predicted from the questionnaire total values. RESULTS: The relationship between the different scales was best explained by a bifactor GRM with one common factor and three domain-specific factors (RMSEA=0.036, CFI=0.977, WRMR=1.061). Based on the results of the regression analyses, three equations were derived for the transformation of questionnaire's total values. CONCLUSION: The IRT linking approach allows the derivation of a general factor of social anxiety, taking into account commonalities and differences between the instruments used. This has advantages for both research and practice. A replication of this study as well as the implementation of further instruments are recommended to verify the validity of this approach and to generalize the results.