Dropout from psychological treatment for borderline personality disorder: a multilevel survival meta-analysis.

BACKGROUND: Dropout from psychotherapy for borderline personality disorder (BPD) is a notorious problem. We investigated whether treatment, treatment format, treatment setting, substance use exclusion criteria, proportion males, mean age, country, and other variables influenced dropout. METHODS: From Pubmed, Embase, Cochrane, Psycinfo and other sources, 111 studies (159 treatment arms, N = 9100) of psychotherapy for non-forensic adult patients with BPD were included. Dropout per quarter during one year of treatment was analyzed on participant level with multilevel survival analysis, to deal with multiple predictors, nonconstant dropout chance over time, and censored data. Multiple imputation was used to estimate quarter of drop-out if unreported. Sensitivity analyses were done by excluding DBT-arms with deviating push-out rules. RESULTS: Dropout was highest in the first quarter of treatment. Schema therapy had the lowest dropout overall, and mentalization-based treatment in the first two quarters. Community treatment by experts had the highest dropout. Moreover, individual therapy had lowest dropout, group therapy highest, with combined formats in-between. Other variables such as age or substance-use exclusion criteria were not associated with dropout. CONCLUSION: The findings do not support claims that all treatments are equal, and indicate that efforts to reduce dropout should focus on early stages of treatment and on group treatment.

Group schema therapy for cluster-C personality disorders: A multicentre open pilot study.

BACKGROUND: Group schema therapy (GST) is increasingly popular as a treatment for personality disorders (PDs), including Cluster-C PDs. Individual ST has proven to be effective for Cluster-C PD patients, while the evidence for GST is limited. This study aimed to investigate the effectiveness of GST for Cluster-C PD. Moreover, differences between the specific Cluster-C PDs (avoidant PD, dependent PD and obsessive-compulsive PD) were explored. METHODS: A multicentre open trial was conducted, including 137 patients with a Cluster-C PD (avoidant PD: n = 107, dependent PD: n = 11 and obsessive-compulsive PD: n = 19). Patients received 30 weekly GST sessions with a maximum of 180 min of individual ST and five optional monthly booster sessions. Outcome measures including Cluster-C PD severity, general psychopathological symptoms, quality of life, functional impairment, happiness, PD-related beliefs, self-esteem, self-ideal discrepancy, schemas and schema modes were assessed at baseline until 2-year follow-up with semi-structured interviews and self-report measures. Change over time and differences between the specific Cluster-C PDs were analysed with mixed regression analyses. RESULTS: The outcome measures showed significant improvements for all Cluster-C PDs, with medium to large effect sizes after 2 years. A treatment dropout rate of 11.7% was found. There were some indications for differences between the Cluster-C PDs in severity at baseline, change trajectories and effectiveness of GST. CONCLUSIONS: This study demonstrated that GST is a promising treatment for Cluster-C PDs. The following step is a randomized controlled trial to further document the (cost-)effectiveness of GST.

Towards optimal treatment selection for borderline personality disorder patients (BOOTS): a study protocol for a multicenter randomized clinical trial comparing schema therapy and dialectical behavior therapy.

BACKGROUND: Specialized evidence-based treatments have been developed and evaluated for borderline personality disorder (BPD), including Dialectical Behavior Therapy (DBT) and Schema Therapy (ST). Individual differences in treatment response to both ST and DBT have been observed across studies, but the factors driving these differences are largely unknown. Understanding which treatment works best for whom and why remain central issues in psychotherapy research. The aim of the present study is to improve treatment response of DBT and ST for BPD patients by a) identifying patient characteristics that predict (differential) treatment response (i.e., treatment selection) and b) understanding how both treatments lead to change (i.e., mechanisms of change). Moreover, the clinical effectiveness and cost-effectiveness of DBT and ST will be evaluated. METHODS: The BOOTS trial is a multicenter randomized clinical trial conducted in a routine clinical setting in several outpatient clinics in the Netherlands. We aim to recruit 200 participants, to be randomized to DBT or ST. Patients receive a combined program of individual and group sessions for a maximum duration of 25 months. Data are collected at baseline until three-year follow-up. Candidate predictors of (differential) treatment response have been selected based on the literature, a patient representative of the Borderline Foundation of the Netherlands, and semi-structured interviews among 18 expert clinicians. In addition, BPD-treatment-specific (ST: beliefs and schema modes; DBT: emotion regulation and skills use), BPD-treatment-generic (therapeutic environment characterized by genuineness, safety, and equality), and non-specific (attachment and therapeutic alliance) mechanisms of change are assessed. The primary outcome measure is change in BPD manifestations. Secondary outcome measures include functioning, additional self-reported symptoms, and well-being. DISCUSSION: The current study contributes to the optimization of treatments for BPD patients by extending our knowledge on "Which treatment - DBT or ST - works the best for which BPD patient, and why?", which is likely to yield important benefits for both BPD patients (e.g., prevention of overtreatment and potential harm of treatments) and society (e.g., increased economic productivity of patients and efficient use of treatments). TRIAL REGISTRATION: Netherlands Trial Register, NL7699 , registered 25/04/2019 - retrospectively registered.

Design of an RCT on cost-effectiveness of group schema therapy versus individual schema therapy for patients with Cluster-C personality disorder: the QUEST-CLC study protocol.

BACKGROUND: Given the high prevalence of Cluster-C Personality Disorders (PDs) in clinical populations, disease burden, high societal costs and poor prognosis of comorbid disorders, a major gain in health care can be achieved if Cluster-C PDs are adequately treated. The only controlled cost-effectiveness study published so far found Individual Schema Therapy (IST) to be superior to Treatment as Usual (TAU). Group ST (GST) might improve cost-effectiveness as larger numbers can be treated in (>50%) less time compared to IST. However, to date there is no RCT supporting its (cost-) effectiveness. The overall aim of this study is to assess the evidence for GST for Cluster-C PDs and to improve treatment allocation for individual patients. Three main questions are addressed: 1) Is GST for Cluster-C PDs (cost-)effective compared to TAU? 2) Is GST for Cluster-C PDs (cost-) effective compared to IST? 3) Which patient-characteristics predict better response to GST, IST, or TAU? METHODS: In a multicenter RCT, the treatment conditions GST, IST, and TAU are compared in 378 Cluster-C PD patients within 10 sites. GST and IST follow treatment protocols and are completed within 1 year. TAU is the optimal alternative treatment available at the site according to regular procedures. Severity of the Cluster-C PD is the primary outcome, assessed with clinical interviews by independent raters blind for treatment. Functioning and wellbeing are important secondary outcomes. Assessments take place at week 0 (baseline), 17 (mid-GST), 34 (post-GST), 51 (post-booster sessions of GST), and 2 years (FU). Patient characteristics predicting better response to a specific treatment are studied, e.g., childhood trauma, autistic features, and introversion. A tool supporting patients and clinicians in matching treatment to patient will be developed. An economic evaluation investigates the cost-effectiveness and cost-utility from a societal perspective. A process evaluation by qualitative methods explores experiences of participants, loved ones and therapists regarding recovery, quality of life, and improving treatment. DISCUSSION: This study will determine the (cost-)effectiveness of treatments for Cluster-C PDs regarding treatment type as well as optimal matching of patient to treatment and deliver insight into which aspects help Cluster-C-PD patients recover and create a fulfilling life. TRIAL REGISTRATION: Dutch Trial Register: NL9209 . Registered on 28-01-2021.

Moderate acute alcohol use impairs intentional inhibition rather than stimulus-driven inhibition.

Moderate alcohol intake may impair stimulus-driven inhibition of motor actions in go/no-go and stop-signal tasks. Exposure to alcohol-related cues has been found to exacerbate this impairment. By contrast, the effect of alcohol use on intentional inhibition, or the capacity to voluntarily suspend an action, has rarely been investigated. We examined whether and how moderate alcohol intake affects stimulus-driven inhibition (stop-signal task) and intentional inhibition (chasing bottles task), during exposure to alcohol-related stimuli. One hundred and eleven participants were randomly assigned to an alcohol (male: 0.55 g/kg, female: 0.45 g/kg), placebo, or control group. For the stop-signal task, ANOVAs were performed on stop-signal reaction time (SSRT) and go RT with Pharmacological and Expectancy Effects of Alcohol, Stimulus Category (alcohol-related or neutral), and Sex as factors. For the chasing bottles task, multilevel survival analysis was performed to predict whether and when intentional inhibition was initiated, with the same factors. For the stop-signal task, Sex moderated the Pharmacological Effect of Alcohol on SSRT: only for females, alcohol consumption shortened SSRT. In the non-alcohol groups, males had shorter SSRT than females. Concerning intentional inhibition, the alcohol group initiated intentional inhibition less often, especially when stimuli were non-alcohol related. These findings indicate that (1) stimulus-driven inhibition and intentional inhibition reflect different aspects of response inhibition; (2) moderate alcohol intake negatively affects intentional inhibition (but not stimulus-driven inhibition). Speculatively, the observed impairment in intentional inhibition might underlie the lack of control over alcohol drinking behavior after a priming dose. This study highlights the potential role of intentional inhibition in the development of addiction.

Non-fear emotions in changes in posttraumatic stress disorder symptoms during treatment.

BACKGROUND AND OBJECTIVES: Posttraumatic stress disorder (PTSD) is not only associated with fear but also with other emotions. The present study aimed to examine if changes in shame, guilt, anger, and disgust predicted changes in PTSD symptoms during treatment, while also testing if PTSD symptoms, in turn, predicted changes in these emotions. METHODS: Participants (N = 155) with childhood-related PTSD received a maximum of 12 sessions of eye movement desensitization and reprocessing or imagery rescripting. The data was analyzed using Granger causality models across 12 treatment sessions and 6 assessment sessions (up until one year after the start of treatment). Differences between the two treatments were explored. RESULTS: Across treatment sessions, shame, and disgust showed a reciprocal relationship with PTSD symptoms, while changes in guilt preceded PTSD symptoms. Across assessments, anger was reciprocally related to PTSD, suggesting that anger might play a more important role in the longer term. LIMITATIONS: The individual emotion items were not yet validated, and the CAPS was not administered at all assessments. CONCLUSIONS: These findings partly differ from earlier studies that suggested a unidirectional relationship in which changes in emotions preceded changes in PTSD symptoms during treatment. This is in line with the idea that non-fear emotions do play an important role in the treatment of PTSD and constitute an important focus of treatment and further research.

Postural freezing relates to startle potentiation in a human fear-conditioning paradigm.

Freezing to impending threat is a core defensive response. It has been studied primarily using fear conditioning in non-human animals, thwarting advances in translational human anxiety research that has used other indices, such as skin conductance responses. Here we examine postural freezing as a human conditioning index for translational anxiety research. We employed a mixed cued/contextual fear-conditioning paradigm where one context signals the occurrence of the US upon the presentation of the CS, and another context signals that the CS is not followed by the US. Critically, during the following generalization phase, the CS is presented in a third and novel context. We show that human freezing is highly sensitive to fear conditioning, generalizes to ambiguous contexts, and amplifies with threat imminence. Intriguingly, stronger parasympathetically driven freezing under threat, but not sympathetically mediated skin conductance, predicts subsequent startle magnitude. These results demonstrate that humans show fear-conditioned animal-like freezing responses, known to aid in active preparation for unexpected attack, and that freezing captures real-life anxiety expression. Conditioned freezing offers a promising new, non-invasive, and continuous, readout for human fear conditioning, paving the way for future translational studies into human fear and anxiety.

Threat learning impairs subsequent associative inference.

Despite it being widely acknowledged that the most important function of memory is to facilitate the prediction of significant events in a complex world, no studies to date have investigated how our ability to infer associations across distinct but overlapping experiences is affected by the inclusion of threat memories. To address this question, participants (n = 35) encoded neutral predictive associations (A → B). The following day these memories were reactivated by pairing B with a new aversive or neutral outcome (B → CTHREAT/NEUTRAL) while pupil dilation was measured as an index of emotional arousal. Then, again 1 day later, the accuracy of indirect associations (A → C?) was tested. Associative inferences involving a threat learning memory were impaired whereas the initial memories were retroactively strengthened, but these effects were not moderated by pupil dilation at encoding. These results imply that a healthy memory system may compartmentalize episodic information of threat, and so hinders its recall when cued only indirectly. Malfunctioning of this process may cause maladaptive linkage of negative events to distant and benign memories, and thereby contribute to the development of clinical intrusions and anxiety.

Functional connectivity analysis of fMRI data using parameterized regions-of-interest.

Connectivity analysis of fMRI data requires correct specification of regions-of-interest (ROIs). Selection of ROIs based on outcomes of a GLM analysis may be hindered by conservativeness of the multiple comparison correction, while selection based on brain anatomy may be biased due to inconsistent structure-to-function mapping. To alleviate these problems we propose a method to define functional ROIs without the need for a stringent multiple comparison correction. We extend a flexible framework for fMRI analysis (Activated Region Fitting, Weeda et al. 2009) to connectivity analysis of fMRI data. This method describes an entire fMRI data volume by regions of activation defined by a limited number of parameters. Therefore a less stringent multiple comparison procedure is required. The regions of activation from this analysis can be directly used to estimate functional connectivity. Simulations show that Activated Region Fitting can recover the connectivity of brain regions. An application to real data of a Go/No-Go experiment highlights the advantages of the method.

On the Relation Between the Linear Factor Model and the Latent Profile Model.

The relationship between linear factor models and latent profile models is addressed within the context of maximum likelihood estimation based on the joint distribution of the manifest variables. Although the two models are well known to imply equivalent covariance decompositions, in general they do not yield equivalent estimates of the unconditional covariances. In particular, a 2-class latent profile model with Gaussian components underestimates the observed covariances but not the variances, when the data are consistent with a unidimensional Gaussian factor model. In explanation of this phenomenon we provide some results relating the unconditional covariances to the goodness of fit of the latent profile model, and to its excess multivariate kurtosis. The analysis also leads to some useful parameter restrictions related to symmetry.

Subtypes of smokers in a randomized controlled trial of a web-based smoking cessation program and their role in predicting intervention non-usage attrition: Implications for the development of tailored interventions.

INTRODUCTION: Web-based smoking interventions hold potential for smoking cessation; however, many of them report low intervention usage (i.e., high levels of non-usage attrition). One strategy to counter this issue is to tailor such interventions to user subtypes if these can be identified and related to non-usage attrition outcomes. The aim of this study was two-fold: (1) to identify and describe a smoker typology in participants of a web-based smoking cessation program and (2) to explore subtypes of smokers who are at a higher risk for non-usage attrition (i.e., early dropout times). METHODS: We conducted secondary analyses of data from a large randomized controlled trial (RCT) that investigated effects of a web-based Cognitive Bias Modification intervention in adult smokers. First, we conducted a two-step cluster analysis to identify subtypes of smokers based on participants' baseline characteristics (including demographics, psychological and smoking-related variables, N = 749). Next, we conducted a discrete-time survival analysis to investigate the predictive value of the subtypes on time until dropout. RESULTS: We found three distinct clusters of smokers: Cluster 1 (25.2%, n = 189) was characterized by participants being relatively young, highly educated, unmarried, light-to-moderate smokers, poly-substance users, and relatively high scores on sensation seeking and impulsivity; Cluster 2 (41.0%, n = 307) was characterized by participants being older, with a relatively high socio-economic status (SES), moderate-to-heavy smokers and regular drinkers; Cluster 3 (33.8%, n = 253) contained mostly females of older age, and participants were further characterized by a relatively low SES, heavy smoking, and relatively high scores on hopelessness, anxiety sensitivity, impulsivity, depression, and alcohol use. Additionally, Cluster 1 was more likely to drop out at the early stage of the intervention compared to Cluster 2 (adjusted Hazard Ratio (HR adjusted) = 1.51, 95% CI = [1.25, 1.83]) and Cluster 3 (HR adjusted = 1.52, 95% CI = [1.25, 1.86]). CONCLUSIONS: We identified three clusters of smokers that differed on a broad range of characteristics and on intervention non-usage attrition patterns. This highlights the heterogeneity of participants in a web-based smoking cessation program. Also, it supports the idea that such interventions could be tailored to these subtypes to prevent non-usage attrition. The subtypes of smokers identified in this study need to be replicated in the field of e-health outside the context of RCT; based on the smoker subtypes identified in this study, we provided suggestions for developing tailored web-based smoking cessation intervention programs in future research.

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.

Combining Web-Based Attentional Bias Modification and Approach Bias Modification as a Self-Help Smoking Intervention for Adult Smokers Seeking Online Help: Double-Blind Randomized Controlled Trial.

BACKGROUND: Automatically activated cognitive motivational processes such as the tendency to attend to or approach smoking-related stimuli (ie, attentional and approach bias) have been related to smoking behaviors. Therefore, these cognitive biases are thought to play a role in maintaining smoking behaviors. Cognitive biases can be modified with cognitive bias modification (CBM), which holds promise as an easy-access and low-cost online intervention. However, little is known about the effectiveness of online interventions combining two varieties of CBM. Targeting multiple cognitive biases may improve treatment outcomes because these biases have been shown to be relatively independent. OBJECTIVE: This study aimed to test the individual and combined effects of two web-based CBM varieties-attentional bias modification (AtBM) and approach bias modification (ApBM)-in a double-blind randomized controlled trial (RCT) with a 2 (AtBM: active versus sham) × 2 (ApBM: active versus sham) factorial design. METHODS: A total of 504 adult smokers seeking online help to quit smoking were randomly assigned to 1 of 4 experimental conditions to receive 11 fully automated CBM training sessions. To increase participants' intrinsic motivation to change their smoking behaviors, all participants first received brief, automated, tailored feedback. The primary outcome was point prevalence abstinence during the study period. Secondary outcomes included daily cigarette use and attentional and approach bias. All outcomes were repeatedly self-assessed online from baseline to the 3-month follow-up. For the examination of training effects on outcome changes, an intention-to-treat analysis with a multilevel modeling (MLM) approach was adopted. RESULTS: Only 10.7% (54/504) of the participants completed all 11 training sessions, and 8.3% (42/504) of the participants reached the 3-month follow-up assessment. MLM showed that over time, neither AtBM or ApBM nor a combination of both differed from their respective sham training in point prevalence abstinence rates (P=.17, P=.56, and P=.14, respectively), and in changes in daily cigarette use (P=.26, P=.08, and P=.13, respectively), attentional bias (P=.07, P=.81, and P=.15, respectively), and approach bias (P=.57, P=.22, and P=.40, respectively), while daily cigarette use decreased over time across conditions for all participants (P<.001). CONCLUSIONS: This RCT provides no support for the effectiveness of combining AtBM and ApBM in a self-help web-based smoking cessation intervention. However, this study had a very high dropout rate and a very low frequency of training usage, indicating an overall low acceptability of the intervention, which precludes any definite conclusion on its efficacy. We discuss how this study can inform future designs and settings of online CBM interventions. TRIAL REGISTRATION: Netherlands Trial Register NTR4678; https://www.trialregister.nl/trial/4678.

Dynamics of sleep: Exploring critical transitions and early warning signals.

BACKGROUND AND OBJECTIVES: In standard practice, sleep is classified into distinct stages by human observers according to specific rules as for instance specified in the AASM manual. We here show proof of principle for a conceptualization of sleep stages as attractor states in a nonlinear dynamical system in order to develop new empirical criteria for sleep stages. METHODS: EEG (single channel) of two healthy sleeping participants was used to demonstrate this conceptualization. Firstly, distinct EEG epochs were selected, both detected by a MLR classifier and through manual scoring. Secondly, change point analysis was used to identify abrupt changes in the EEG signal. Thirdly, these detected change points were evaluated on whether they were preceded by early warning signals. RESULTS: Multiple change points were identified in the EEG signal, mostly in interplay with N2. The dynamics before these changes revealed, for a part of the change points, indicators of generic early warning signals, characteristic of complex systems (e.g., ecosystems, climate, epileptic seizures, global finance systems). CONCLUSIONS: The sketched new framework for studying critical transitions in sleep EEG might benefit the understanding of individual and pathological differences in the dynamics of sleep stage transitions. Formalising sleep as a nonlinear dynamical system can be useful for definitions of sleep quality, i.e. stability and accessibility of an equilibrium state, and disrupted sleep, i.e. constant shifting between instable sleep states.

A comprehensive meta-analysis of money priming.

Research on money priming typically investigates whether exposure to money-related stimuli can affect people's thoughts, feelings, motivations, and behaviors (for a review, see Vohs, 2015). Our study answers the call for a comprehensive meta-analysis examining the available evidence on money priming (Vadillo, Hardwicke, & Shanks, 2016). By conducting a systematic search of published and unpublished literature on money priming, we sought to achieve three key goals. First, we aimed to assess the presence of biases in the available published literature (e.g., publication bias). Second, in the case of such biases, we sought to derive a more accurate estimate of the effect size after correcting for these biases. Third, we aimed to investigate whether design factors such as prime type and study setting moderated the money priming effects. Our overall meta-analysis included 246 suitable experiments and showed a significant overall effect size estimate (Hedges' g = .31, 95% CI [0.26, 0.36]). However, publication bias and related biases are likely given the asymmetric funnel plots, Egger's test and two other tests for publication bias. Moderator analyses offered insight into the variation of the money priming effect, suggesting for various types of study designs whether the effect was present, absent, or biased. We found the largest money priming effect in lab studies investigating a behavioral dependent measure using a priming technique in which participants actively handled money. Future research should use sufficiently powerful preregistered studies to replicate these findings. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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.

Intra-individual variability in ADHD, autism spectrum disorders and Tourette's syndrome.

The potential for response variability to serve as an endophenotype for attention deficit hyperactivity disorders (ADHD) rests, in part, upon the development of reliable and valid methods to decompose variability. This study investigated the specificity of intra-individual variability (IIV) in 53 children with ADHD by comparing them with 25 children with high functioning autism (HFA), 32 children with autism spectrum disorders (ASD), who also were comorbid for ADHD (ASD+ADHD), 21 children with Tourette's syndrome (TS), and 85 typically developing controls (TD). In order to decompose the variability of the reaction times, we applied three distinct techniques: ex-Gaussian modeling, intra-individual variability analysis, and spectral analysis. Our data revealed that children with HFA and children with ASD+ADHD exhibited substantial IIV compared with ADHD and TD children. We argue that: (1) all three methods lead to a single consistent conclusion; (2) careful documentation of the analytic steps used in spectral analysis is mandatory for comparison between studies; (3) the presence of comorbidities may constitute an important factor in the observed response variability in previous studies of ADHD.

EZ does it! Extensions of the EZ-diffusion model.

In this rejoinder, we address two of Ratcliff's main concerns with respect to the EZ-diffusion model (Ratcliff, 2008). First, we introduce "robust-EZ," a mixture model approach to achieve robustness against the presence of response contaminants that might otherwise distort parameter estimates. Second, we discuss an extension of the EZ model that allows the estimation of starting point as an additional parameter. Together with recently developed, user-friendly software programs for fitting the full diffusion model (Vandekerckhove & Tuerlinckx, 2007; Voss & Voss, 2007), the development of the EZ model and its extensions is part of a larger effort to make diffusion model analyses accessible to a broader audience, an effort that is long overdue.

Unidimensional factor models imply weaker partial correlations than zero-order correlations.

In this paper we present a new implication of the unidimensional factor model. We prove that the partial correlation between two observed variables that load on one factor given any subset of other observed variables that load on this factor lies between zero and the zero-order correlation between these two observed variables. We implement this result in an empirical bootstrap test that rejects the unidimensional factor model when partial correlations are identified that are either stronger than the zero-order correlation or have a different sign than the zero-order correlation. We demonstrate the use of the test in an empirical data example with data consisting of fourteen items that measure extraversion.

Multivariate normative comparisons.

In neuropsychological evaluations and single case research generally a number of tests are administered, since the interest is not in a single, but in multiple characteristics of a patient. The typical problem is to decide whether or not a patient is different from normal controls with respect to one or more of these characteristics. Consideration of each characteristic separately entails an increased risk of a false positive decision (a wrongful decision that the patient is abnormal, or a type 1 error). From a statistical point of view this calls for a multivariate analysis. In this paper, we propose two approaches to perform normative comparisons for such multivariate data: Bonferroni corrected univariate comparisons and a multivariate comparison. Both approaches allow for the testing of unidirectional (two-sided) as well as directional (one-sided) hypothesis, i.e. the hypothesis that a patient deviates in a negative sense from the norm. Monte Carlo simulations were performed to check if the type I error of both approaches is adequately controlled, and to investigate the power of both approaches to detect deviation from the norm. The results indicate that the type I error rate of both approaches is correct, even in small samples. The results also indicate that the power is higher for the univariate approach if the normative sample size is very small (i.e. just exceeds the number of tests administered). In larger samples, the multivariate comparison has in general increased power. We illustrate both approaches with a clinical example of patients with Parkinson disease, who received deep brain stimulation to alleviate motor symptoms, and who were neuropsychologically evaluated to detect possible cognitive side effects.