Short-time AOIs-based representative scanpath identification and scanpath aggregation

A new algorithm to identify a representative scanpath in a sample is presented and evaluated with eye-tracking data. According to Gestalt theory, each fixation of the scanpath should be on an area of interest (AOI) of the stimuli. As with existing methods, we first identify the AOIs and then extract the fixations of the representative scanpath from the AOIs. In contrast to existing methods, we propose a new concept of short-time AOI and extract the fixations of representative scanpath from the short-time AOIs. Our method outperforms the existing methods on two publicly available datasets. Our method can be applied to arbitrary visual stimuli, including static stimuli without natural segmentation, as well as dynamic stimuli. Our method also provides a solution for issues caused by the selection of scanpath similarity.

The Effects of Internet-Based Cognitive Behavioral Therapy for Suicidal Ideation or Behaviors on Depression, Anxiety, and Hopelessness in Individuals With Suicidal Ideation: Systematic Review and Meta-Analysis of Individual Participant Data

BACKGROUND

Suicide is a global public health problem. Digital interventions are considered a low-threshold treatment option for people with suicidal ideation or behaviors. Internet-based cognitive behavioral therapy (iCBT) targeting suicidal ideation has demonstrated effectiveness in reducing suicidal ideation. However, suicidal ideation often is related to additional mental health problems, which should be addressed for optimal care. Yet, the effects of iCBT on related symptoms, such as depression, anxiety, and hopelessness, remain unclear.

OBJECTIVE

We aimed to analyze whether digital interventions targeting suicidal ideation had an effect on related mental health symptoms (depression, anxiety, and hopelessness).

METHODS

We systematically searched CENTRAL, PsycInfo, Embase, and PubMed for randomized controlled trials that investigated guided or unguided iCBT for suicidal ideation or behaviors. Participants reporting baseline suicidal ideation were eligible. Individual participant data (IPD) were collected from eligible trials. We conducted a 1-stage IPD meta-analysis on the effects on depression, anxiety, and hopelessness-analyzed as 2 indices: symptom severity and treatment response.

RESULTS

We included IPD from 8 out of 9 eligible trials comprising 1980 participants with suicidal ideation. iCBT was associated with significant reductions in depression severity (b=-0.17; 95% CI -0.25 to -0.09; P<.001) and higher treatment response (ie, 50% reduction of depressive symptoms; b=0.36; 95% CI 0.12-0.60; P=.008) after treatment. We did not find significant effects on anxiety and hopelessness.

CONCLUSIONS

iCBT for people with suicidal ideation revealed significant effects on depression outcomes but only minor or no effects on anxiety and hopelessness. Therefore, individuals with comorbid symptoms of anxiety or hopelessness may require additional treatment components to optimize care. Studies that monitor symptoms with higher temporal resolution and consider a broader spectrum of factors influencing suicidal ideation are needed to understand the complex interaction of suicidality and related mental health symptoms.

Effects of digital cognitive behavioral therapy for depression on suicidal thoughts and behavior: Protocol for a systematic review and meta-analysis of individual participant data

INTRODUCTION

Digital cognitive behavioral therapy (i-CBT) interventions for the treatment of depression have been extensively studied and shown to be effective in the reduction of depressive symptoms. However, little is known about their effects on suicidal thoughts and behaviors (STB). Information on the impact of digital interventions on STB are essential for patients' safety because most digital interventions are self-help interventions without direct support options in case of a suicidal crisis. Therefore, we aim to conduct a meta-analysis of individual participant data (IPDMA) to investigate the effects of i-CBT interventions for depression on STB and to explore potential effect moderators.

METHODS

Data will be retrieved from an established and annually updated IPD database of randomized controlled trials investigating the effectiveness of i-CBT interventions for depression in adults and adolescents. We will conduct a one-stage and a two-stage IPDMA on the effects of these interventions on STB. All types of control conditions are eligible. STB can be measured using specific scales (e.g., Beck scale suicide, BSS) or single items from depression scales (e.g., item 9 of the PHQ-9) or standardized clinical interviews. Multilevel linear regression will be used for specific scales, and multilevel logistic regression will be used for treatment response or deterioration, operationalized as a change in score by at least one quartile from baseline. Exploratory moderator analyses will be conducted at participant, study, and intervention level. Two independent reviewers will assess the risk of bias using the Cochrane Risk of Bias Tool 2.

CONCLUSION

This IPDMA will harness the available data to assess the effects (response and deterioration) of i-CBT interventions for depression interventions on STB. Information about changes in STB is essential to estimate patients' safety when engaging in digital treatment formats.

TRIAL REGISTRATION

We will pre-register this study with the open science framework after article acceptance to ensure consistency between online registration and the published trial protocol.

Crohn's Disease as a Possible Risk Factor for Failed Healing in Ileocolic Anastomoses

Anastomotic leakage (AL) after colorectal resections is a serious complication in abdominal surgery. Especially in patients with Crohn's disease (CD), devastating courses are observed. Various risk factors for the failure of anastomotic healing have been identified; however, whether CD itself is independently associated with anastomotic complications still remains to be validated. A retrospective analysis of a single-institution inflammatory bowel disease (IBD) database was conducted. Only patients with elective surgery and ileocolic anastomoses were included. Patients with emergency surgery, more than one anastomosis, or protective ileostomies were excluded. For the investigation of the effect of CD on AL 141, patients with CD-type L1, B1-3 were compared to 141 patients with ileocolic anastomoses for other indications. Univariate statistics and multivariate analysis with logistic regression and backward stepwise elimination were performed. CD patients had a non-significant higher percentage of AL compared to non-IBD patients (12% vs. 5%, p = 0.053); although, the two samples differed in terms of age, body mass index (BMI), Charlson comorbidity index (CCI), and other clinical variables. However, Akaike information criterion (AIC)-based stepwise logistic regression identified CD as a factor for impaired anastomotic healing (final model: p = 0.027, OR: 17.043, CI: 1.703-257.992). Additionally, a CCI ≥ 2 (p = 0.010) and abscesses (p = 0.038) increased the disease risk. The alternative point estimate for CD as a risk factor for AL based on propensity score weighting also resulted in an increased risk, albeit lower (p = 0.005, OR 7.36, CI 1.82-29.71). CD might bear a disease-specific risk for the impaired healing of ileocolic anastomoses. CD patients are prone to postoperative complications, even in absence of other risk factors, and might benefit from treatment in dedicated centers.

Digital cognitive-behavioural therapy to reduce suicidal ideation and behaviours: a systematic review and meta-analysis of individual participant data

QUESTION

Digital interventions based on cognitive-behavioural therapy (iCBT) is associated with reductions in suicidal ideation. However, fine-grained analyses of effects and potential effect-moderating variables are missing. This study aimed to investigate the effectiveness of iCBT on suicidal ideation, effect moderators, effects on suicide attempts and predictors of adherence.

STUDY SELECTION AND ANALYSIS

We systematically searched CENTRAL, PsycINFO, Embase and PubMed for randomised controlled trials that investigated iCBT for suicidal ideation or behaviours. Participants reporting baseline suicidal ideation were eligible. We conducted a one-stage individual participant data (IPD) meta-analysis. Suicidal ideation was the primary outcome, analysed as three indices: severity of suicidal ideation, reliable changes and treatment response.

FINDINGS

We included IPD from nine out of ten eligible trials (2037 participants). iCBT showed significant reductions of suicidal ideation compared with control conditions across all indices (severity: b=-0.247, 95% CI -0.322 to -0.173; reliable changes: b=0.633, 95% CI 0.408 to 0.859; treatment response: b=0.606, 95% CI 0.410 to 0.801). In iCBT, the rate of reliable improvement was 40.5% (controls: 27.3%); the deterioration rate was 2.8% (controls: 5.1%). No participant-level moderator effects were identified. The effects on treatment response were higher for trials with waitlist-controls compared with active controls. There were insufficient data on suicide attempts. Human support and female gender predicted treatment adherence. The main source of potential bias was missing outcome data.

CONCLUSIONS

The current evidence indicates that iCBT is effective in reducing suicidal ideation irrespective of age, gender and previous suicide attempts. Future studies should rigorously assess suicidal behaviour and drop-out reasons.

Meta-analysis of diagnostic cell-free circulating microRNAs for breast cancer detection

Background

Breast cancer (BC) is the most frequently diagnosed cancer among women. Numerous studies explored cell-free circulating microRNAs as diagnostic biomarkers of BC. As inconsistent and rarely intersecting microRNA panels have been reported thus far, we aim to evaluate the overall diagnostic performance as well as the sources of heterogeneity between studies.

Methods

Based on the search of three online search engines performed up to March 21^st 2022, 56 eligible publications that investigated diagnostic circulating microRNAs by utilizing Real-Time Quantitative Reverse Transcription PCR (qRT-PCR) were obtained. Primary studies’ potential for bias was evaluated with the revised tool for the quality assessment of diagnostic accuracy studies (QUADAS-2). A bivariate generalized linear mixed-effects model was applied to obtain pooled sensitivity and specificity. A novel methodology was utilized in which the sample and study models’ characteristics were analysed to determine the potential preference of studies for sensitivity or specificity.

Results

Pooled sensitivity and specificity of 0.85 [0.81—0.88] and 0.83 [0.79—0.87] were obtained, respectively. Subgroup analysis showed a significantly better performance of multiple (sensitivity: 0.90 [0.86—0.93]; specificity: 0.86 [0.80—0.90]) vs single (sensitivity: 0.82 [0.77—0.86], specificity: 0.83 [0.78—0.87]) microRNA panels and a comparable pooled diagnostic performance between studies using serum (sensitivity: 0.87 [0.81—0.91]; specificity: 0.83 [0.78—0.87]) and plasma (sensitivity: 0.83 [0.77—0.87]; specificity: 0.85 [0.78—0.91]) as specimen type. In addition, based on bivariate and univariate analyses, miRNA(s) based on endogenous normalizers tend to have a higher diagnostic performance than miRNA(s) based on exogenous ones. Moreover, a slight tendency of studies to prefer specificity over sensitivity was observed.

Conclusions

In this study the diagnostic ability of circulating microRNAs to diagnose BC was reaffirmed. Nonetheless, some subgroup analyses showed between-study heterogeneity. Finally, lack of standardization and of result reproducibility remain the biggest issues regarding the diagnostic application of circulating cell-free microRNAs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09698-8.

Interactions of scores derived from two groups of variables: Alternating lasso regularization avoids overfitting and finds interpretable scores

Regression models with interaction terms are common models for moderating relationships. When effects of several predictors from one group-for example, genetic variables-are potentially moderated by several predictors from another-for example, environmental variables-many interaction terms result. This complicates model interpretation, especially when coefficient signs point in different directions. By first forming a score for each group of predictors, the interaction model's dimension is severely reduced. The hierarchical score model is an elegant one-step approach: Score weights and regression model coefficients are estimated simultaneously by an alternating optimization (AO) algorithm. Especially in high dimensional settings, scores remain an effective technique to reduce interaction model dimension, and we propose regularization to ensure sparsity and interpretability of the score weights. A nontrivial extension of the original AO algorithm is presented, which adds a lasso penalty, resulting in the alternating lasso optimization algorithm (ALOA). The hierarchical score model with ALOA is an interpretable statistical learning technique for moderation in potentially high dimensional applications, and encompasses generalized linear models for the main interaction model. In addition to the lasso regularization, a screening procedure called regularization and residualization (RR) is proposed to avoid spurious interactions. ALOA tuning parameter choice and the RR screening procedure are investigated by simulations, and two illustrative applications to depression risk are provided. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Fisher transformation based confidence intervals of correlations in fixed- and random-effects meta-analysis

Meta-analyses of correlation coefficients are an important technique to integrate results from many cross-sectional and longitudinal research designs. Uncertainty in pooled estimates is typically assessed with the help of confidence intervals, which can double as hypothesis tests for two-sided hypotheses about the underlying correlation. A standard approach to construct confidence intervals for the main effect is the Hedges-Olkin-Vevea Fisher-z (HOVz) approach, which is based on the Fisher-z transformation. Results from previous studies (Field, 2005, Psychol. Meth., 10, 444; Hafdahl and Williams, 2009, Psychol. Meth., 14, 24), however, indicate that in random-effects models the performance of the HOVz confidence interval can be unsatisfactory. To this end, we propose improvements of the HOVz approach, which are based on enhanced variance estimators for the main effect estimate. In order to study the coverage of the new confidence intervals in both fixed- and random-effects meta-analysis models, we perform an extensive simulation study, comparing them to established approaches. Data were generated via a truncated normal and beta distribution model. The results show that our newly proposed confidence intervals based on a Knapp-Hartung-type variance estimator or robust heteroscedasticity consistent sandwich estimators in combination with the integral z-to-r transformation (Hafdahl, 2009, Br. J. Math. Stat. Psychol., 62, 233) provide more accurate coverage than existing approaches in most scenarios, especially in the more appropriate beta distribution simulation model.

Rotate and Project: Measurement of the Intended Concept with Unidimensional Item Response Theory from Multidimensional Ordinal Items

What to do when item response data are multidimensional but a unidimensional model is preferred in terms of statistical simplicity and ease of interpretability? The projection method for the compensatory logistic multidimensional item response model for dichotomous data leads to a two parameter logistic model with local item dependence. Despite the local item dependence, the model is unidimensional for many practical purposes. Here, Ip's projection method is generalized to the case of the graded response model for polytomous variables, extending the applicability of the method to Likert-type response formats. A secondary aim of the paper is the study of rotation techniques intended for use prior to projection. In contrast to rotations aiming at a simple structure of factor loadings, the proposed techniques increase the variance explained before or after projection, facilitate the interpretation of the projected dimension by variants of target rotations or a mix of both. The method is illustrated with an application to the Highly Sensitive Person Scale and R code is provided.

Validity and Reliability of Automatically Generated Propositional Reasoning Items

Abstract. This study introduces a newly developed public-domain multilingual automatic item generator that creates propositional reasoning (PR) items belonging to 15 item families by using various inference rules. Psychometric properties of the resulting written PR test were investigated in three diverse samples in English, simplified Chinese, and German, respectively. Internal consistency was good to excellent across samples. The ICAR16 short form test of cognitive abilities ( Condon & Revelle, 2014 ) was used to evaluate construct validity. Correlations of ICAR16 scores and PR scores were high. Furthermore, items within families appeared to be equivalent, with only minor differential item functioning between the Chinese- and English-speaking samples. Performance on the PR test was shown to be reasonably stable over the course of 1 week. Differences of total scores between test forms (pen and paper vs. computerized administration) were not detected. Findings suggest that the automatically generated PR test is a valuable instrument for the assessment of propositional reasoning ability.

Interests, Motives, and Psychological Burdens in Times of Crisis and Lockdown: Google Trends Analysis to Inform Policy Makers

BACKGROUND

In the face of the COVID-19 pandemic, the German government and the 16 German federal states implemented a variety of nonpharmaceutical interventions (NPIs) to decelerate the spread of the SARS-CoV-2 virus and thus prevent a collapse of the health care system. These measures comprised, among others, social distancing, the temporary closure of shops and schools, and a ban of large public gatherings and meetings with people not living in the same household.

OBJECTIVE

It is fair to assume that the issued NPIs have heavily affected social life and psychological functioning. We therefore aimed to examine possible effects of this lockdown in conjunction with daily new infections and the state of the national economy on people's interests, motives, and other psychological states.

METHODS

We derived 249 keywords from the Google Trends database, tapping into 27 empirically and rationally selected psychological domains. To overcome issues with reliability and specificity of individual indicator variables, broad factors were derived by means of time series factor analysis. All domains were subjected to a change point analysis and time series regression analysis with infection rates, NPIs, and the state of the economy as predictors. All keywords and analyses were preregistered prior to analysis.

RESULTS

With the pandemic arriving in Germany, significant increases in people's search interests were observed in virtually all domains. Although most of the changes were short-lasting, each had a distinguishable onset during the lockdown period. Regression analysis of the Google Trends data confirmed pronounced autoregressive effects for the investigated variables, while forecasting by means of the tested predictors (ie, daily new infections, NPIs, and the state of economy) was moderate at best.

CONCLUSIONS

Our findings indicate that people's interests, motives, and psychological states are heavily affected in times of crisis and lockdown. Specifically, disease- and virus-related domains (eg, pandemic disease, symptoms) peaked early, whereas personal health strategies (eg, masks, homeschooling) peaked later during the lockdown. Domains addressing social life and psychosocial functioning showed long-term increases in public interest. Renovation was the only domain to show a decrease in search interest with the onset of the lockdown. As changes in search behavior are consistent over multiple domains, a Google Trends analysis may provide information for policy makers on how to adapt and develop intervention, information, and prevention strategies, especially when NPIs are in effect.

Reliability generalization of tasks and recommendations for assessing the ability to perceive facial expressions of emotion

Despite a strong research interest in emotion perception ability, this construct is usually assessed without sufficient consideration of measurement reliability, thus limiting its predictive utility. We present an overview of available emotion perception ability tests, followed by a brief review of test reliability concepts, including classical and modern approaches. Then, through a reliability generalization (k = 106, n total = 16,859), we evaluate which tests have high internal consistency and which design characteristics are associated with better internal consistency. We conclude with general recommendations for the psychometric evaluation of emotion perception measures and provide recommendations for best measurement practices. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Are Internet- and mobile-based interventions effective in adults with diagnosed panic disorder and/or agoraphobia? A systematic review and meta-analysis

BACKGROUND

There is no meta-analysis that specifically evaluates the effectiveness of Internet- and mobile-based interventions (IMIs) in adults with diagnosed panic disorder and/or agoraphobia (PD/A) so far. The current meta-analysis aims to fill this gap (PROSPERO CRD 42016034016).

METHODS

Systematic literature searches in six databases for randomised and controlled clinical trials investigating IMIs in adults, who met diagnostic criteria for PD/A. Study selection and data extraction were conducted independently by two reviewers. Random-effects meta-analyses, pre-planned subgroup and sensitivity analyses were conducted when appropriate. Primary outcomes were PD and A symptom severity. In addition, adherence, response, remission, quality of life, anxiety and depression symptom severity were examined.

RESULTS

A total of 16 trials (1015 patients), with 21 comparisons (9 IMI vs. waitlist; 7 IMI vs. IMI; 5 IMI vs. active treatment condition), were included. IMIs revealed beneficial effects on panic (Hedges' g range -2.61 to -0.25) and agoraphobia symptom severity when compared to waitlist (pooled g = -1.15, [95%-CI = -1.56; -0.74]). Studies comparing IMIs to active controls (i.e., face-to-face CBT and applied relaxation) did not find significant differences for reductions in panic (g = -0.02, [95%-CI = -0.25; 0.21]) and agoraphobia symptom severity (g = -0.10, [95%-CI = -0.39; 0.19]). Furthermore, IMIs were superior to waitlist controls regarding anxiety and depression symptom severity and quality of life.

LIMITATIONS

Tests for publication bias were not feasible due to the limited number of trials per comparison, and the risk of bias assessment indicated some methodological shortcomings.

CONCLUSIONS

Findings from this meta-analytic review provide support for the effectiveness of IMIs in patients with verified PD/A. However, before IMIs can be included in treatment guidelines for PD/A, future high quality research is needed that substantiates and extends the evidence base, especially in regard to intervention safety.

Revisiting dispersion in count data item response theory models: The Conway-Maxwell-Poisson counts model

Count data naturally arise in several areas of cognitive ability testing, such as processing speed, memory, verbal fluency, and divergent thinking. Contemporary count data item response theory models, however, are not flexible enough, especially to account for over- and underdispersion at the same time. For example, the Rasch Poisson counts model (RPCM) assumes equidispersion (conditional mean and variance coincide) which is often violated in empirical data. This work introduces the Conway-Maxwell-Poisson counts model (CMPCM) that can handle underdispersion (variance lower than the mean), equidispersion, and overdispersion (variance larger than the mean) in general and specifically at the item level. A simulation study revealed satisfactory parameter recovery at moderate sample sizes and mostly unbiased standard errors for the proposed estimation approach. In addition, plausible empirical reliability estimates resulted, while those based on the RPCM were biased downwards (underdispersion) and biased upwards (overdispersion) when the simulation model deviated from equidispersion. Finally, verbal fluency data were analysed and the CMPCM with item-specific dispersion parameters fitted the data best. Dispersion parameter estimates indicated underdispersion for three out of four items. Overall, these findings indicate the feasibility and importance of the suggested flexible count data modelling approach.

Effectiveness of Internet- and Mobile-Based Cognitive Behavioral Therapy to Reduce Suicidal Ideation and Behaviors: Protocol for a Systematic Review and Meta-Analysis of Individual Participant Data

Internet- and mobile-based cognitive behavioral therapy (iCBT) might reduce suicidal ideation. However, recent meta-analyses found small effect sizes, and it remains unclear whether specific subgroups of participants experience beneficial or harmful effects. This is the study protocol for an individual participant meta-analysis (IPD-MA) aiming to determine the effectiveness of iCBT on suicidal ideation and identify moderators. We will systematically search CENTRAL, PsycINFO, Embase, and Pubmed for randomized controlled trials examining guided or self-guided iCBT for suicidality. All types of control conditions are eligible. Participants experiencing suicidal ideation will be included irrespective of age, diagnoses, or co-interventions. We will conduct a one-stage IPD-MA with suicidal ideation as the primary outcome, using a continuous measure, reliable improvement and deterioration, and response rate. Moderator analyses will be performed on participant-, study-, and intervention-level. Two independent reviewers will assess risk of bias and the quality of evidence using Cochrane's Risk of Bias Tool 2 and GRADE. This review was registered with OSF and is currently in progress. The IPD-MA will provide effect estimates while considering covariates and will offer novel insights into differential effects on a participant level. This will help to develop more effective, safe, and tailored digital treatment options for suicidal individuals.

Comparison of random-effects meta-analysis models for the relative risk in the case of rare events: A simulation study

Pooling the relative risk (RR) across studies investigating rare events, for example, adverse events, via meta-analytical methods still presents a challenge to researchers. The main reason for this is the high probability of observing no events in treatment or control group or both, resulting in an undefined log RR (the basis of standard meta-analysis). Other technical challenges ensue, for example, the violation of normality assumptions, or bias due to exclusion of studies and application of continuity corrections, leading to poor performance of standard approaches. In the present simulation study, we compared three recently proposed alternative models (random-effects [RE] Poisson regression, RE zero-inflated Poisson [ZIP] regression, binomial regression) to the standard methods in conjunction with different continuity corrections and to different versions of beta-binomial regression. Based on our investigation of the models' performance in 162 different simulation settings informed by meta-analyses from the Cochrane database and distinguished by different underlying true effects, degrees of between-study heterogeneity, numbers of primary studies, group size ratios, and baseline risks, we recommend the use of the RE Poisson regression model. The beta-binomial model recommended by Kuss (2015) also performed well. Decent performance was also exhibited by the ZIP models, but they also had considerable convergence issues. We stress that these recommendations are only valid for meta-analyses with larger numbers of primary studies. All models are applied to data from two Cochrane reviews to illustrate differences between and issues of the models. Limitations as well as practical implications and recommendations are discussed; a flowchart summarizing recommendations is provided.

Comparing effects of detached mindfulness and cognitive restructuring in obsessive-compulsive disorder using ecological momentary assessment

Ecological momentary assessment (EMA) has been shown to be a valid and sensitive measure of treatment effects in obsessive-compulsive disorder (OCD). As part of a clinical trial, this EMA study deals with a comparison of two treatment conditions, that is, cognitive restructuring (CR) and detached mindfulness (DM). EMA data from n = 39 OCD patients were available from a randomized clinical trial on the effectiveness of CR and DM. Smartphone-based EMA sampling spread over 4 days each before and after treatment, with 10 random prompts per day and a 2-week intervention of either CR or DM. We tracked CR strategies (e.g., questioning an appraisal by re-evaluating risk), DM strategies (e.g., allowing one's thoughts to come and go), and application of newly learned strategies during Post-Treatment EMA. Although there was a trend towards DM strategies being applied more often during Pre-Treatment EMA than CR strategies, we did not find differences during Post-Treatment EMA between CR and DM regarding frequency or difficulty of application and experienced relief. As expected, we found a clear pre-post increase for all CR and DM behaviours except for one DM item. However, we did not find a treatment-specific increase of CR and DM behaviours; that is, both interventions equally well promoted a seemingly general treatment effect. Despite the ecological validity of EMA, however, social desirability effects cannot be ruled out, so that this conclusion must be handled cautiously. Further research is needed to replicate and generalize our results.

A Comparison of Different Item Response Theory Models for Scaling Speeded C-Tests

As measures of general language proficiency, C-tests are ubiquitous in language testing. Speeded C-tests are quite recent developments in the field and are deemed to be more discriminatory and provide more accurate diagnostic information than power C-tests especially with high-ability participants. Item response theory modeling of speeded C-tests has not been discussed in the literature, and current approaches for power C-tests based on ordinal models either violate the model assumptions or are relatively complex to be reliably fitted with small samples. Count data models are viable alternatives with less restrictive assumptions and lower complexity. In the current study, we compare count data models with commonly applied ordinal models for modeling a speeded C-test. It was found that a flexible count data model fits equally well in absolute and relative terms as compared with ordinal models. Implications and feasibility of count data models for the psychometric modeling of C-tests are discussed.

A study on treatment sensitivity of ecological momentary assessment in obsessive-compulsive disorder

As part of a larger clinical trial, this ecological momentary assessment (EMA) study pursued the main goal of demonstrating that the EMA method is sensitive to treatment effects of detached mindfulness and cognitive restructuring for obsessive-compulsive disorder (OCD). A second goal was to provide a descriptive analysis of OCD symptoms and influencing factors in participants' everyday lives. Thirty-nine participants were included in the final analyses. EMA sampling involved a smartphone and comprised 4 days with 10 random prompts per day both before (Pre-Treatment EMA) and after the completion of a 2-week clinical intervention of either detached mindfulness or cognitive restructuring (Post-Treatment EMA) that participants had been randomly allocated to. The EMA questionnaire included items on the frequency of obsessions, subjective burden due to obsessions, perceived current stress, emotions, and on the frequency of compulsions and other dysfunctional behaviors. Descriptive Pre-Treatment EMA results highlight the importance of compulsions and emotional states of tension/discomfort in OCD. Pre-Post comparisons showed a significant reduction of avoidance behavior, obsessions, and burden due to obsessions, with a nonsignificant trend also indicating a reduction of compulsions. There was no pre to post effect concerning emotions. This study adds to the existing research on OCD symptoms and offers further evidence in confirmation of established theoretical models of OCD. Also, our results can be taken as evidence for treatment sensitivity of the EMA method in OCD. Further research is needed to replicate, broaden, and generalize our results.

A randomized waitlist-controlled trial comparing detached mindfulness and cognitive restructuring in obsessive-compulsive disorder

Objective

Whereas research has demonstrated the efficacy of cognitive restructuring (CR) for obsessive-compulsive disorder (OCD), little is known about the efficacy of specific metacognitive interventions such as detached mindfulness (DM). Therefore, this study compared the efficacy of CR and DM as stand-alone interventions.

Design

We conducted a randomized waitlist-controlled trial. n = 43 participants were randomly assigned to either DM or CR. Out of those participants, n = 21 participants had been previously assigned to a two-week waitlist condition.

Materials and methods

In both conditions, treatment comprised four double sessions within two weeks. Assessment took place at baseline (Pre1), after treatment (Post) and four weeks after the end of treatment (FU). There was a second baseline assessment (Pre2) in the waitlist group. Independent evaluators were blinded concerning the active condition. Adherence and competence ratings for the two therapists were obtained from an independent rater.

Results

40 patients completed the treatment. Two patients dropped out because of exacerbated depression. There were no further adverse events. Both CR and DM were shown to be superior to waitlist and equally effective at reducing OCD symptoms from pre to post assessment as measured with the Y-BOCS (CR: d = 1.67, DM: d = 1.55). In each of the two treatment conditions, eight patients (40%) exhibited a clinical significant change at post assessment.

Conclusions

The results of this clinical trial suggest the potential efficacy of DM as a stand-alone intervention for OCD, however, our findings need to be interpreted with caution. Results indicate that both CR and DM should be considered as possible alternative treatments for OCD, whereas the working mechanisms of DM have yet to be elucidated.

Everyday emotional dynamics in major depression

The present study aimed to investigate the everyday emotional dynamics of depressed individuals, especially the role of emotional inertia, emotional context insensitivity, and emotional variability and instability. Using ecological momentary assessment, 40 currently depressed individuals and 40 healthy controls reported on their current emotional state and current activities 10 times a day for 4 consecutive days. There were no differences in the dynamics of positive affect (PA) between depressed and healthy subjects. Depressed participants' negative affect (NA), however, was found to be more inert than in healthy controls, while at the same time being more variable and more reactive to positive events. There was also an association between emotional instability and depression, but this was rendered nonsignificant when analyses were controlled for emotional variability. Altogether, emotional dynamics of NA appear to be more prominently disturbed in depression compared to PA. Results support earlier findings on NA emotional variability as well as inertia in depressed patients. In addition, there was some evidence for a mood brightening effect in depression. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

Four-Branch Model of Ability Emotional Intelligence With Fluid and Crystallized Intelligence: A Meta-Analysis of Relations

We meta-analytically investigated relations between the four-branch model of ability emotional intelligence (EI) with fluid (Gf) and crystallized intelligence (Gc; 352 effect sizes; ntotal = 15,333). We found that for each branch, the strength of relations with Gf and Gc were equivalent. Understanding emotions has the strongest relation with Gf/Gc combined (ρ = .43, k = 81, n = 11,524), relative to facilitating thought using emotion (ρ = .19, k = 51, n = 7,254), managing emotions (ρ = .20, k = 74, n = 11,359), and perceiving emotion (ρ = .20, k = 79, n = 9,636); for the latter, relations were also moderated by stimulus type. We conclude with implications and recommendations for the study of ability EI.

Introduction to the Rasch Poisson Counts Model: An R Tutorial

The Rasch Poisson Counts Model is the oldest Rasch model developed by the Danish mathematician Georg Rasch in 1952. Nevertheless, the model has had limited applications in psychoeducational assessment. With the rise of neurocognitive and psychomotor testing, there is more room for new applications of the model where other item response theory models cannot be applied. In this paper, we give a general introduction to the Rasch Poisson Counts Model and then using data of an attention test walk the reader through how to use the “lme4” package in R to estimate the model and interpret the outputs.

(Non-)symbolic magnitude processing in children with mathematical difficulties: A meta-analysis

Symbolic and non-symbolic magnitude representations, measured by digit or dot comparison tasks, are assumed to underlie the development of arithmetic skills. The comparison distance effect (CDE) has been suggested as a hallmark of the preciseness of mental magnitude representations. It implies that two magnitudes are harder to discriminate when the numerical distance between them is small, and may therefore differ in children with mathematical difficulties (MD), i.e. low mathematical achievement or dyscalculia. However, empirical findings on the CDE in children with MD are heterogeneous, and only few studies assess both symbolic and non-symbolic skills. This meta-analysis therefore integrates 44 symbolic and 48 non-symbolic response time (RT) outcomes reported in nineteen studies (N=1630 subjects, aged 6-14 years). Independent of age, children with MD show significantly longer mean RTs than typically achieving controls, particularly on symbolic (Hedges' g=0.75; 95% CI [0.51; 0.99]), but to a significantly lower extent also on non-symbolic (g=0.24; 95% CI [0.13; 0.36]) tasks. However, no group differences were found for the CDE. Extending recent work, these meta-analytical findings on children with MD corroborate the diagnostic importance of magnitude comparison speed in symbolic tasks. By contrast, the validity of CDE measures in assessing MD is questioned.

Childhood maltreatment and characteristics of adult depression: meta-analysis

BACKGROUND

Childhood maltreatment has been discussed as a risk factor for the development and maintenance of depression.

AIMS

To examine the relationship between childhood maltreatment and adult depression with regard to depression incidence, severity, age at onset, course of illness and treatment response.

METHOD

We conducted meta-analyses of original articles reporting an association between childhood maltreatment and depression outcomes in adult populations.

RESULTS

In total, 184 studies met inclusion criteria. Nearly half of patients with depression reported a history of childhood maltreatment. Maltreated individuals were 2.66 (95% CI 2.38-2.98) to 3.73 (95% CI 2.88-4.83) times more likely to develop depression in adulthood, had an earlier depression onset and were twice as likely to develop chronic or treatment-resistant depression. Depression severity was most prominently linked to childhood emotional maltreatment.

CONCLUSIONS

Childhood maltreatment, especially emotional abuse and neglect, represents a risk factor for severe, early-onset, treatment-resistant depression with a chronic course.

Emotional Processing Theory Put to Test: A Meta-Analysis on the Association Between Process and Outcome Measures in Exposure Therapy

In order to test the predictions derived from emotional processing theory (EPT), this meta-analysis examined correlations between outcome of exposure therapy and three process variables: initial fear activation (IFA), within-session habituation (WSH) and between-session habituation (BSH). Literature search comprised a keyword-based search in databases, a reverse search and the examination of reference lists. Of the 21 studies included in the analyses, 17 provided data concerning IFA (57 endpoints, total N = 490), five concerning WSH (7 endpoints, total N = 116) and eight concerning BSH (22 endpoints, total N = 304). Owing to this data structure, analyses were performed using robust variance estimation with random-effects models being assumed a priori. Results indicated that WSH and BSH are positively related to treatment outcome. By contrast, the statistical association between IFA and outcome of exposure was not confirmed, whereas our moderator analysis suggested that physiological process measures lead to higher correlations than non-physiological ones. The results for IFA and BSH were affected by selective reporting. In sum, our results do not specifically strengthen EPT while matching other theoretical perspectives such as inhibitory learning and reality testing. Further research is needed to provide recommendations concerning the best way of delivering exposure therapy. Copyright © 2016 John Wiley & Sons, Ltd.

KEY PRACTITIONER MESSAGE

This meta-analysis examined three variables of emotional processing theory (EPT). Initial fear activation was not linearly related to outcomes of exposure therapy. Habituation within and between sessions were shown to correlate with outcome. Outcome reporting bias was shown to play a crucial role in this meta-analysis. Results do not specifically support EPT.

Optimal design of the Wilcoxon-Mann-Whitney-test

In scientific research, many hypotheses relate to the comparison of two independent groups. Usually, it is of interest to use a design (i.e., the allocation of sample sizes m and n for fixed N=m+n) that maximizes the power of the applied statistical test. It is known that the two-sample t-tests for homogeneous and heterogeneous variances may lose substantial power when variances are unequal but equally large samples are used. We demonstrate that this is not the case for the nonparametric Wilcoxon-Mann-Whitney-test, whose application in biometrical research fields is motivated by two examples from cancer research. We prove the optimality of the design m=n in case of symmetric and identically shaped distributions using normal approximations and show that this design generally offers power only negligibly lower than the optimal design for a wide range of distributions.

Meta-analysis of Diagnostic Accuracy and ROC Curves with Covariate Adjusted Semiparametric Mixtures

Many screening tests dichotomize a measurement to classify subjects. Typically a cut-off value is chosen in a way that allows identification of an acceptable number of cases relative to a reference procedure, but does not produce too many false positives at the same time. Thus for the same sample many pairs of sensitivities and false positive rates result as the cut-off is varied. The curve of these points is called the receiver operating characteristic (ROC) curve. One goal of diagnostic meta-analysis is to integrate ROC curves and arrive at a summary ROC (SROC) curve. Holling, Böhning, and Böhning (Psychometrika 77:106-126, 2012a) demonstrated that finite semiparametric mixtures can describe the heterogeneity in a sample of Lehmann ROC curves well; this approach leads to clusters of SROC curves of a particular shape. We extend this work with the help of the [Formula: see text] transformation, a flexible family of transformations for proportions. A collection of SROC curves is constructed that approximately contains the Lehmann family but in addition allows the modeling of shapes beyond the Lehmann ROC curves. We introduce two rationales for determining the shape from the data. Using the fact that each curve corresponds to a natural univariate measure of diagnostic accuracy, we show how covariate adjusted mixtures lead to a meta-regression on SROC curves. Three worked examples illustrate the method.

Saddlepoint Approximations of the Distribution of the Person Parameter in the Two Parameter Logistic Model

Large sample theory states the asymptotic normality of the maximum likelihood estimator of the person parameter in the two parameter logistic (2PL) model. In short tests, however, the assumption of normality can be grossly wrong. As a consequence, intended coverage rates may be exceeded and confidence intervals are revealed to be overly conservative. Methods belonging to the higher-order-theory, more specifically saddlepoint approximations, are a convenient way to deal with small-sample problems. Confidence bounds obtained by these means hold the approximate confidence level for a broad range of the person parameter. Moreover, an approximation to the exact distribution permits to compute median unbiased estimates (MUE) that are as likely to overestimate as to underestimate the true person parameter. Additionally, in small samples, these MUE are less mean-biased than the often-used maximum likelihood estimator.

Is functional integration of resting state brain networks an unspecific biomarker for working memory performance?

Is there one optimal topology of functional brain networks at rest from which our cognitive performance would profit? Previous studies suggest that functional integration of resting state brain networks is an important biomarker for cognitive performance. However, it is still unknown whether higher network integration is an unspecific predictor for good cognitive performance or, alternatively, whether specific network organization during rest predicts only specific cognitive abilities. Here, we investigated the relationship between network integration at rest and cognitive performance using two tasks that measured different aspects of working memory; one task assessed visual-spatial and the other numerical working memory. Network clustering, modularity and efficiency were computed to capture network integration on different levels of network organization, and to statistically compare their correlations with the performance in each working memory test. The results revealed that each working memory aspect profits from a different resting state topology, and the tests showed significantly different correlations with each of the measures of network integration. While higher global network integration and modularity predicted significantly better performance in visual-spatial working memory, both measures showed no significant correlation with numerical working memory performance. In contrast, numerical working memory was superior in subjects with highly clustered brain networks, predominantly in the intraparietal sulcus, a core brain region of the working memory network. Our findings suggest that a specific balance between local and global functional integration of resting state brain networks facilitates special aspects of cognitive performance. In the context of working memory, while visual-spatial performance is facilitated by globally integrated functional resting state brain networks, numerical working memory profits from increased capacities for local processing, especially in brain regions involved in working memory performance.

Testing for publication bias in diagnostic meta-analysis: a simulation study

The present study investigates the performance of several statistical tests to detect publication bias in diagnostic meta-analysis by means of simulation. While bivariate models should be used to pool data from primary studies in diagnostic meta-analysis, univariate measures of diagnostic accuracy are preferable for the purpose of detecting publication bias. In contrast to earlier research, which focused solely on the diagnostic odds ratio or its logarithm ( ln ω), the tests are combined with four different univariate measures of diagnostic accuracy. For each combination of test and univariate measure, both type I error rate and statistical power are examined under diverse conditions. The results indicate that tests based on linear regression or rank correlation cannot be recommended in diagnostic meta-analysis, because type I error rates are either inflated or power is too low, irrespective of the applied univariate measure. In contrast, the combination of trim and fill and ln ω has non-inflated or only slightly inflated type I error rates and medium to high power, even under extreme circumstances (at least when the number of studies per meta-analysis is large enough). Therefore, we recommend the application of trim and fill combined with ln ω to detect funnel plot asymmetry in diagnostic meta-analysis.

Optimal and most exact confidence intervals for person parameters in item response theory models

The common way to calculate confidence intervals for item response theory models is to assume that the standardized maximum likelihood estimator for the person parameter θ is normally distributed. However, this approximation is often inadequate for short and medium test lengths. As a result, the coverage probabilities fall below the given level of significance in many cases; and, therefore, the corresponding intervals are no longer confidence intervals in terms of the actual definition. In the present work, confidence intervals are defined more precisely by utilizing the relationship between confidence intervals and hypothesis testing. Two approaches to confidence interval construction are explored that are optimal with respect to criteria of smallness and consistency with the standard approach.