Measuring autism in males and females with a differential item functioning approach: Results from a nation-wide population-based study.

Existing screening instruments for Autism Spectrum Disorder (ASD) might be prone to detect a male manifestation of ASD. Here, we examined the 17 items from the ASD domain in the Autism-Tics, ADHD and other Comorbidities inventory (A-TAC) for Differential Item Functioning (DIF). Data were obtained from the Child and Adolescent Twin Study in Sweden (CATSS) in which parents have responded to the A-TAC.

Harmonizing behavioral outcomes across studies, raters, and countries: application to the genetic analysis of aggression in the ACTION Consortium.

Background: Aggression in children has genetic and environmental causes. Studies of aggression can pool existing datasets to include more complex models of social effects. Such analyses require large datasets with harmonized outcome measures.

Data Integration Methods for Phenotype Harmonization in Multi-Cohort Genome-Wide Association Studies With Behavioral Outcomes.

Parallel meta-analysis is a popular approach for increasing the power to detect genetic effects in genome-wide association studies across multiple cohorts. Consortia studying the genetics of behavioral phenotypes are oftentimes faced with systematic differences in phenotype measurement across cohorts, introducing heterogeneity into the meta-analysis and reducing statistical power. This study investigated integrative data analysis (IDA) as an approach for jointly modeling the phenotype across multiple datasets.

Deductive data mining.

Data mining methods offer a powerful tool for psychologists to capture complex relations such as interaction and nonlinear effects without prior specification. However, interpreting and integrating information from data mining models can be challenging. The current research proposes a strategy to identify nonlinear and interaction effects by using a deductive data mining approach that in essence consists of comparing increasingly complex data mining models.

The development of a brief screener for autism using item response theory.

Background: Brief screening instruments focusing on autism spectrum disorder (ASD) that can be administered in primary care are scarce; there is a need for shorter and more precise instruments. The Autism-Tics, AD/HD and other Comorbidities inventory (A-TAC) has previously been validated for ASD reporting excellent validity. This study aims to determine the psychometric properties of each item in the ASD domain (17 items) in the A-TAC using item response theory (IRT), and thereby construct and validate a short form that could be used as a screening instrument in the general population.

The relation between the age at diagnosis of problem behaviors related to aggression and distal outcomes in Swedish children.

Severe childhood aggressive behaviors are known to predict negative outcomes later in life; however, little is known about the effect of when in childhood aggression problems are diagnosed. While an earlier first diagnosis of problematic aggressive behavior might be associated with increased severity and, thus, worse outcomes, it is also possible that an earlier diagnosis affords an earlier start of treatment programs or indicates that greater attention is being paid to behavioral problems, thus resulting in attenuation of the severity of childhood aggression's impact on distal outcomes. The current study analyzed data from the population-based Swedish Data Registries, which include data on all children formally diagnosed by the Swedish medical system with a wide range of aggression problems between ages 8 and 18 (N = 5816) during the years 1987-2013, along with a matched control.

Childhood aggression and the co-occurrence of behavioural and emotional problems: results across ages 3-16 years from multiple raters in six cohorts in the EU-ACTION project.

Childhood aggression and its resulting consequences inflict a huge burden on affected children, their relatives, teachers, peers and society as a whole. Aggression during childhood rarely occurs in isolation and is correlated with other symptoms of childhood psychopathology. In this paper, we aim to describe and improve the understanding of the co-occurrence of aggression with other forms of childhood psychopathology.

Genetic and environmental contributions to the development of childhood aggression.

Longitudinal data from a large sample of twins participating in the Netherlands Twin Register (n = 42,827, age range 3-16) were analyzed to investigate the genetic and environmental contributions to childhood aggression. Genetic auto-regressive (simplex) models were used to assess whether the same genes are involved or whether new genes come into play as children grow up. The authors compared 2 different simplex models to disentangle potentially changing behavioral expressions from changes in genetic and environmental effects.

Sum Scores in Twin Growth Curve Models: Practicality Versus Bias.

To study behavioral or psychiatric phenotypes, multiple indices of the behavior or disorder are often collected that are thought to best reflect the phenotype. Combining these items into a single score (e.g.

Disentangling Heterogeneity of Childhood Disruptive Behavior Problems Into Dimensions and Subgroups.

Objective: Irritable and oppositional behaviors are increasingly considered as distinct dimensions of oppositional defiant disorder. However, few studies have explored this multidimensionality across the broader spectrum of disruptive behavior problems (DBPs). This study examined the presence of dimensions and distinct subgroups of childhood DBPs, and the cross-sectional and longitudinal associations between these dimensions.

Assessing Model Selection Uncertainty Using a Bootstrap Approach: An update.

Model comparisons in the behavioral sciences often aim at selecting the model that best describes the structure in the population. Model selection is usually based on fit indices such as AIC or BIC, and inference is done based on the selected best-fitting model. This practice does not account for the possibility that due to sampling variability, a different model might be selected as the preferred model in a new sample from the same population.

Using a multivariate model to assess the interactive effects of demographics and lifestyle on the hematological profile.

Aim: To assess the extent to which a multivariate approach to modeling interrelated hematological indices provides more informative results than the traditional approach of modeling each index separately.

Materials & Methods: The effects of demographics and lifestyle on ten hematological indices collected from a Dutch population-based sample (n = 3278) were studied, jointly using multivariate distance matrix regression and separately using linear regression.

Results: The multivariate approach highlighted the main effects of all predictors and several interactions; the traditional approach highlighted only main effects.

Fitting latent variable mixture models.

Latent variable mixture models (LVMMs) are models for multivariate observed data from a potentially heterogeneous population. The responses on the observed variables are thought to be driven by one or more latent continuous factors (e.g.

Finding structure in data using multivariate tree boosting.

Technology and collaboration enable dramatic increases in the size of psychological and psychiatric data collections, but finding structure in these large data sets with many collected variables is challenging. Decision tree ensembles such as random forests (Strobl, Malley, & Tutz, 2009) are a useful tool for finding structure, but are difficult to interpret with multiple outcome variables which are often of interest in psychology. To find and interpret structure in data sets with multiple outcomes and many predictors (possibly exceeding the sample size), we introduce a multivariate extension to a decision tree ensemble method called gradient boosted regression trees (Friedman, 2001).

Extending multivariate distance matrix regression with an effect size measure and the asymptotic null distribution of the test statistic.

Person-centered methods are useful for studying individual differences in terms of (dis)similarities between response profiles on multivariate outcomes. Multivariate distance matrix regression (MDMR) tests the significance of associations of response profile (dis)similarities and a set of predictors using permutation tests. This paper extends MDMR by deriving and empirically validating the asymptotic null distribution of its test statistic, and by proposing an effect size for individual outcome variables, which is shown to recover true associations.

Detection of gene-environment interaction in pedigree data using genome-wide genotypes.

Heritability may be estimated using phenotypic data collected in relatives or in distantly related individuals using genome-wide single nucleotide polymorphism (SNP) data. We combined these approaches by re-parameterizing the model proposed by Zaitlen et al and extended this model to include moderation of (total and SNP-based) genetic and environmental variance components by a measured moderator. By means of data simulation, we demonstrated that the type 1 error rates of the proposed test are correct and parameter estimates are accurate.

Joint developmental trajectories of internalizing and externalizing disorders between childhood and adolescence.

This study sought to identify trajectories of DSM-IV based internalizing (INT) and externalizing (EXT) problem scores across childhood and adolescence and to provide insight into the comorbidity by modeling the co-occurrence of INT and EXT trajectories. INT and EXT were measured repeatedly between age 7 and age 15 years in over 7,000 children and analyzed using growth mixture models. Five trajectories were identified for both INT and EXT, including very low, low, decreasing, and increasing trajectories.

The use of vector bootstrapping to improve variable selection precision in Lasso models.

The Lasso is a shrinkage regression method that is widely used for variable selection in statistical genetics. Commonly, K-fold cross-validation is used to fit a Lasso model. This is sometimes followed by using bootstrap confidence intervals to improve precision in the resulting variable selections.

Inference Based on the Best-Fitting Model can Contribute to the Replication Crisis: Assessing Model Selection Uncertainty Using a Bootstrap Approach.

Inference and conclusions drawn from model fitting analyses are commonly based on a single "best-fitting" model. If model selection and inference are carried out using the same data model selection uncertainty is ignored. We illustrate the Type I error inflation that can result from using the same data for model selection and inference, and we then propose a simple bootstrap based approach to quantify model selection uncertainty in terms of model selection rates.

Population heterogeneity of trait anger and differential associations of trait anger facets with borderline personality features, neuroticism, depression, Attention Deficit Hyperactivity Disorder (ADHD), and alcohol problems.

Anger is an emotion consisting of feelings of variable intensity ranging from mild irritation to intense fury. High levels of trait anger are associated with a range of psychiatric, interpersonal, and health problems. The objectives of this study were to explore heterogeneity of anger as measured by the Spielberger Trait Anger Scale (STAS), and to assess the association of the different anger facets with a selection of psychiatric disorders covering externalizing and internalizing problems, personality disorders, and substance use.

A powerful phenotype for gene-finding studies derived from trajectory analyses of symptoms of anxiety and depression between age seven and 18.

To investigate the utility of longitudinal data in genetic analyses of symptoms of anxiety and depression, we assessed individual differences between age 7 and 18 using growth mixture models, and investigated the genetic and non-genetic factors contributing to the trajectories. Mothers of 7,706 girl and 7,418 boy twins from the Netherlands Twin Register rated the anxious depression scale (SxAnxDep) of the Child Behavior Check List (CBCL) at age 7, 10, and 12 years. Two thousand seven hundred and six girl and 1,856 boy twins completed the Youth Self Report (YSR) at age 14, 16, and 18.

Genetic analyses benefit from using less heterogeneous phenotypes: an illustration with the hospital anxiety and depression scale (HADS).

Phenotypic heterogeneity of depression has been cited as one of the causes of the limited success to detect genetic variants in genome-wide studies. The 7-item Hospital Anxiety and Depression Scale (HADS-D) was developed to detect depression in individuals with physical health problems. An initial psychometric analysis showed that a short version ("HADS-4") is less heterogeneous and hence more reliable than the full scale, and correlates equally strong with a DSM-oriented depression scale.