Philipp Doebler

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.

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.

A mixed model approach to meta-analysis of diagnostic studies with binary test outcome.

We propose 2 related models for the meta-analysis of diagnostic tests. Both models are based on the bivariate normal distribution for transformed sensitivities and false-positive rates. Instead of using the logit as a transformation for these proportions, we employ the tα family of transformations that contains the log, logit, and (approximately) the complementary log. A likelihood ratio test for the cutoff value problem is developed, and summary receiver operating characteristic (SROC) curves are discussed. Worked examples showcase the methodology. We compare the models to the hierarchical SROC model, which in contrast employs a logit transformation. Data from various meta-analyses are reanalyzed, and the reanalysis indicates a better performance of the models based on the tα transformation.

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.

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.

Adaptive experiments with a multivariate Elo-type algorithm

The present article introduces the multivariate Elo-type algorithm (META), which is inspired by the Elo rating system, a tool for the measurement of the performance of chess players. The META is intended for adaptive experiments with correlated traits. The relationship of the META to other existing procedures is explained, and useful variants and modifications are discussed. The META was investigated within three simulation studies. The gain in efficiency of the univariate Elo-type algorithm was compared to standard univariate procedures; the impact of using correlational information in the META was quantified; and the adaptability to learning and fatigue was investigated. Our results show that the META is a powerful tool to efficiently control task performance in a short time period and to assess correlated traits. The R code of the simulations, the implementation of the META in MATLAB, and an example of how to use the META in the context of neuroscience are provided in supplemental materials.

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-analytic evidence of the effectiveness of stress management at work

ABSTRACT To increase employees’ psychological health and to achieve a competitive advantage, organizations are increasingly introducing flexible work arrangements (FWAs) and stress management training (SMT). This paper provides meta-analytic evidence of the effects of two forms of FWA (flexitime and telecommuting) and three forms of SMT (cognitive-behavioural skills training, relaxation techniques and multiple SMT) on employees’ psychological health, job satisfaction, job performance and absenteeism. Applying the conservation of resource theory, we conjecture that both FWAs and SMT improve all four employee-related outcomes. Quantitative meta-analyses based on 43 primary studies and 22,882 employees show that both FWAs and SMT are positively associated with psychological health and job satisfaction. However, due to a lack of primary studies we were mostly unable to analyse the effects on performance and absenteeism. Although we found a large heterogeneity in the hypothesized relationships, additional moderator analyses of study quality, age, gender, duration and intention of intervention yielded no significant effects. We discuss limitations and implications for practice and for future research.

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

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