Effectiveness of Psychological Treatments for Borderline Personality Disorder and Predictors of Treatment Outcomes: A Multivariate Multilevel Meta-Analysis of Data from All Design Types

We examined the effectiveness of psychotherapies for adult Borderline Personality Disorder (BPD) in a multilevel meta-analysis, including all trial types (PROSPERO ID: CRD42020111351). We tested several predictors, including trial- and outcome type (continuous or dichotomous), setting, BPD symptom domain and mean age. We included 87 studies (N = 5881) from searches between 2013 and 2019 in four databases. We controlled for differing treatment lengths and a logarithmic relationship between treatment duration and effectiveness. Sensitivity analyses were conducted by excluding outliers and by prioritizing total scale scores when both subscale and total scores were reported. Schema Therapy, Mentalization-Based Treatment and reduced Dialectical Behavior Therapy were associated with higher effect sizes than average, and treatment-as-usual with lower effect sizes. General severity and affective instability showed the strongest improvement, dissociation, anger, impulsivity and suicidality/self-injury the least. Treatment effectiveness decreased as the age of participants increased. Dichotomous outcomes were associated to larger effects, and analyses based on last observation carried forward to smaller effects. Compared to the average, the highest reductions were found for certain specialized psychotherapies. All BPD domains improved, though not equally. These findings have a high generalizability. However, causal conclusions cannot be drawn, although the design type did not influence the results

Effectiveness of Psychological Treatments for Borderline Personality Disorder and Predictors of Treatment Outcomes: A Multivariate Multilevel Meta-Analysis of Data from All Design Types

We examined the effectiveness of psychotherapies for adult Borderline Personality Disorder (BPD) in a multilevel meta-analysis, including all trial types (PROSPERO ID: CRD42020111351). We tested several predictors, including trial- and outcome type (continuous or dichotomous), setting, BPD symptom domain and mean age. We included 87 studies (N = 5881) from searches between 2013 and 2019 in four databases. We controlled for differing treatment lengths and a logarithmic relationship between treatment duration and effectiveness. Sensitivity analyses were conducted by excluding outliers and by prioritizing total scale scores when both subscale and total scores were reported. Schema Therapy, Mentalization-Based Treatment and reduced Dialectical Behavior Therapy were associated with higher effect sizes than average, and treatment-as-usual with lower effect sizes. General severity and affective instability showed the strongest improvement, dissociation, anger, impulsivity and suicidality/self-injury the least. Treatment effectiveness decreased as the age of participants increased. Dichotomous outcomes were associated to larger effects, and analyses based on last observation carried forward to smaller effects. Compared to the average, the highest reductions were found for certain specialized psychotherapies. All BPD domains improved, though not equally. These findings have a high generalizability. However, causal conclusions cannot be drawn, although the design type did not influence the results

The Quality of Response Time Data Inference: A Blinded, Collaborative Assessment of the Validity of Cognitive Models

Most data analyses rely on models. To complement statistical models, psychologists have developed cognitive models, which translate observed variables into psychologically interesting constructs. Response time models, in particular, assume that response time and accuracy are the observed expression of latent variables including 1) ease of processing, 2) response caution, 3) response bias, and 4) non-decision time. Inferences about these psychological factors hinge upon the validity of the models' parameters. Here, we use a blinded, collaborative approach to assess the validity of such model-based inferences. Seventeen teams of researchers analyzed the same 14 data sets. In each of these two-condition data sets, we manipulated properties of participants' behavior in a two-alternative forced choice task. The contributing teams were blind to the manipulations, and had to infer what aspect of behavior was changed using their method of choice. The contributors chose to employ a variety of models, estimation methods, and inference procedures. Our results show that, although conclusions were similar across different methods, these "modeler's degrees of freedom" did affect their inferences. Interestingly, many of the simpler approaches yielded as robust and accurate inferences as the more complex methods. We recommend that, in general, cognitive models become a typical analysis tool for response time data. In particular, we argue that the simpler models and procedures are sufficient for standard experimental designs. We finish by outlining situations in which more complicated models and methods may be necessary, and discuss potential pitfalls when interpreting the output from response time models