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Background: The aggregation of a series of N-of-1 trials presents an innovative and efficient study design, as an alternative to traditional randomized clinical trials. Challenges for the statistical analysis arise when there is carry-over or complex dependencies of the treatment effect of interest. Methods: In this study, we evaluate and compare methods for the analysis of aggregated N-of-1 trials in different scenarios with carry-over and complex dependencies of treatment effects on covariates. For this, we simulate data of a series of N-of-1 trials for Chronic Nonspecific Low Back Pain based on assumed causal relationships parameterized by directed acyclic graphs. In addition to existing statistical methods such as regression models, Bayesian Networks, and G-estimation, we introduce a carry-over adjusted parametric model (COAPM). Results: The results show that all evaluated existing models have a good performance when there is no carry-over and no treatment dependence. When there is carry-over, COAPM yields unbiased and more efficient estimates while all other methods show some bias in the estimation. When there is known treatment dependence, all approaches that are capable to model it yield unbiased estimates. Finally, the efficiency of all methods decreases slightly when there are missing values, and the bias in the estimates can also increase. Conclusions: This study presents a systematic evaluation of existing and novel approaches for the statistical analysis of a series of N-of-1 trials. We derive practical recommendations which methods may be best in which scenarios. [ABSTRACT FROM AUTHOR]

Objective: The aims of this study were to determine the effectiveness of a routine clinical care treatment and to identify predictors of treatment outcome in PTSD inpatients. Methods: A routinely collected data set of 612 PTSD inpatients (M = 42.3 years [SD = 11.6], 75.7% female) having received trauma-focused psychotherapy was analyzed. Primary outcome was the clinical symptom severity change score, secondary outcomes were assessed using functional, anxiety, and depression change scores. Hedges g-corrected pre–post effect sizes (ES) were computed for all outcomes. Elastic net regulation as a data-driven, stability-based machine-learning approach was used to build stable clinical prediction models. Results: Hedges g ES indicated medium to large effects on all outcomes. The results of the predictor analyses suggested that a combined predictor model with sociodemographic, clinical, and psychometric variables contribute to predicting different treatment outcomes. Across the clinical and functional outcome, psychoticism, total number of diagnoses, and bronchial asthma consistently showed a stable negative predictive relationship to treatment outcome. Conclusion: Trauma-focused psychotherapy could effectively be implemented in a routine inpatient setting. Some important prognostic variables could be identified. If the proposed models of predictors are replicated, they may help personalize treatment for patients receiving routine clinical care. [ABSTRACT FROM AUTHOR]

Background: A routinely collected dataset was analyzed (1) to determine the naturalistic effectiveness of inpatient psychotherapy for depression in routine psychotherapeutic care, and (2) to identify potential predictors of change.Methods: In a sample of 22,681 inpatients with depression, pre-post and pre-follow-up effect sizes were computed for various outcome variables. To build a probabilistic model of predictors of change, an independent component analysis generated components from demographic and clinical data, and Bayesian EFA extracted factors from the available pre-test, post-test and follow-up questionnaires in a subsample (N = 6377). To select the best-fitted model, the BIC of different path models were compared. A Bayesian path analysis was performed to identify the most important factors to predict changes.Results: Effect sizes were large for the primary outcome and moderate for various secondary outcomes. Almost all pretreatment factors exerted significant influences on different baseline factors. Several factors were found to be resistant to change during treatment: suicidality, agoraphobia, life dissatisfaction, physical disability and pain. The strongest cross-loadings were observed from suicidality on negative cognitions, from agoraphobia on anxiety, and from physical disability on perceived disability.Limitations: No causal conclusions can be drawn directly from our results as we only used cross-lagged panel data without control group.Conclusions: The results indicate large effects of inpatient psychotherapy for depression in routine clinical care. The direct influence of pretreatment factors decreased over the course of treatment. However, some factors appeared stable and difficult to treat, which might hinder treatment outcome. Findings of different predictors of change are discussed. [ABSTRACT FROM AUTHOR]

A routinely collected big data set was analyzed to determine the effectiveness of naturalistic inpatient treatment and to identify predictors of treatment outcome and discontinuation. The sample included 878 patients with borderline personality disorder who received non-manualized dialectic behavioral therapy in a psychosomatic clinic. Effect sizes (Hedge's g) were calculated to determine effectiveness. A bootstrap-enhanced regularized regression with 91 potential predictors was used to identify stable predictors of residualized symptom- and functional change and treatment discontinuation. Results were validated in a holdout sample and repeated cross validation. Effect sizes were small to medium (g = 0.28-0.51). Positive symptom-related outcome was predicted by low affect regulation skills and no previous outpatient psychotherapy. Lower age, absence of work disability, high emotional and physical role limitations and low bodily pain were associated with greater improvement in functional outcome. Higher education and comorbid recurrent depressive disorder were the main predictors of treatment completion. The predictive quality of the models varied, with the best being found for symptom-related outcome (R2 = 18%). While the exploratory process of variable selection replicates previous findings, the validation results suggest that tailoring treatment to the individual patient might not be based solely on sociodemographic, clinical and psychological baseline data. • The naturalistic effectiveness of a DBT program for severely impaired BPD inpatients is small to moderate. • Higher education and comorbid recurrent depressive disorder appear to be the main predictors of treatment completion. • Lower age, absence of work disability, high perceived emotional and physical role limitations and low reported bodily pain may positively predict functioning-related change in addition to baseline functioning. • The applied variable selection procedure replicates previous research findings on predictors, but its validation does not appear to be stable. • The identification of stable predictors of treatment outcomes in BPD remains difficult. Tailoring DBT to individual BPD patients should therefore not only be based on demographic and clinical baseline data. [ABSTRACT FROM AUTHOR]