School Social Capital Mediates Associations Between ASD Traits and Depression Among Adolescents in General Population.

Though autism spectrum disorder (ASD) traits are associated with depression, it is unclear if school social capital mediates their association. We examined whether school social capital mediates the association between ASD traits and depression, and moderation effect of sex on the mediation effect among adolescents in a general population sample (1750 males, 1779 females; equivalent 12-15 years old). The results of this study indicate that ASD traits are associated with depression among adolescents, and that this association is partly mediated by school social capital. Furthermore, the results of the moderated mediation analysis suggest that lower level of school social capital can lead to more increase level of depression for females than for males.

Does Functional Somatic Symptoms Measurement Differ Across Sex and Age? Differential Item Functioning in Somatic Symptoms Measured With the CIDI.

Functional Somatic Symptoms (FSS) are physical symptoms that cannot be attributed to underlying pathology. Their severity is often measured with sum scores on questionnaires; however, this may not adequately reflect FSS severity in subgroups of patients. We aimed to identify the items of the somatization section of the Composite International Diagnostic Interview that best discriminate FSS severity levels, and to assess their functioning in sex and age subgroups. We applied the two-parameter logistic model to 19 items in a population-representative cohort of 962 participants. Subsequently, we examined differential item functioning (DIF). "Localized (muscle) weakness" was the most discriminative item of FSS severity. "Abdominal pain" consistently showed DIF by sex, with males reporting it at higher FSS severity. There was no consistent DIF by age, however, "Joint pain" showed poor discrimination of FSS severity in older adults. These findings could be helpful for the development of better assessment instruments for FSS, which can improve both future research and clinical care.

Predictors of new onsets of irritable bowel syndrome, chronic fatigue syndrome and fibromyalgia: the lifelines study.

BACKGROUND: It has been claimed that functional somatic syndromes share a common etiology. This prospective population-based study assessed whether the same variables predict new onsets of irritable bowel syndrome (IBS), chronic fatigue syndrome (CFS) and fibromyalgia (FM). METHODS: The study included 152 180 adults in the Dutch Lifelines study who reported the presence/absence of relevant syndromes at baseline and follow-up. They were screened at baseline for physical and psychological disorders, socio-demographic, psycho-social and behavioral variables. At follow-up (mean 2.4 years) new onsets of each syndrome were identified by self-report. We performed separate analyses for the three syndromes including participants free of the relevant syndrome or its key symptom at baseline. LASSO logistic regressions were applied to identify which of the 102 baseline variables predicted new onsets of each syndrome. RESULTS: There were 1595 (1.2%), 296 (0.2%) and 692 (0.5%) new onsets of IBS, CFS, and FM, respectively. LASSO logistic regression selected 26, 7 and 19 predictors for IBS, CFS and FM, respectively. Four predictors were shared by all three syndromes, four predicted IBS and FM and two predicted IBS and CFS but 28 predictors were specific to a single syndrome. CFS was more distinct from IBS and FM, which predicted each other. CONCLUSIONS: Syndrome-specific predictors were more common than shared ones and these predictors might form a better starting point to unravel the heterogeneous etiologies of these syndromes than the current approach based on symptom patterns. The close relationship between IBS and FM is striking and requires further research.

Comparing the evidential strength for psychotropic drugs: a Bayesian meta-analysis.

Approval and prescription of psychotropic drugs should be informed by the strength of evidence for efficacy. Using a Bayesian framework, we examined (1) whether psychotropic drugs are supported by substantial evidence (at the time of approval by the Food and Drug Administration), and (2) whether there are systematic differences across drug groups. Data from short-term, placebo-controlled phase II/III clinical trials for 15 antipsychotics, 16 antidepressants for depression, nine antidepressants for anxiety, and 20 drugs for attention deficit hyperactivity disorder (ADHD) were extracted from FDA reviews. Bayesian model-averaged meta-analysis was performed and strength of evidence was quantified (i.e. BFBMA). Strength of evidence and trialling varied between drugs. Median evidential strength was extreme for ADHD medication (BFBMA = 1820.4), moderate for antipsychotics (BFBMA = 365.4), and considerably lower and more frequently classified as weak or moderate for antidepressants for depression (BFBMA = 94.2) and anxiety (BFBMA = 49.8). Varying median effect sizes (ESschizophrenia = 0.45, ESdepression = 0.30, ESanxiety = 0.37, ESADHD = 0.72), sample sizes (Nschizophrenia = 324, Ndepression = 218, Nanxiety = 254, NADHD = 189.5), and numbers of trials (kschizophrenia = 3, kdepression = 5.5, kanxiety = 3, kADHD = 2) might account for differences. Although most drugs were supported by strong evidence at the time of approval, some only had moderate or ambiguous evidence. These results show the need for more systematic quantification and classification of statistical evidence for psychotropic drugs. Evidential strength should be communicated transparently and clearly towards clinical decision makers.

Towards personalized assessment of fatigue perpetuating factors in patients with chronic fatigue syndrome using ecological momentary assessment: A pilot study.

OBJECTIVE: This study aimed to explore the associations between cognitions, behaviours and affects and fatigue in chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME), and their relation to reduction of fatigue after cognitive behaviour therapy (CBT). METHODS: In CFS/ME patients, 22 behaviours, cognitions and affects, potentially perpetuating fatigue were registered 5 times a day using ecological momentary assessment (EMA) and an actigraphy. Simultaneous Components Analysis (SCA) was used to identify components of perpetuation, that were tested for their associations with fatigue in multilevel vector autoregressive (VAR) modelling. Fatigue severity was measured pre- and posttreatment with the Checklist Individual Strength. The relationship between perpetuation (the strength and direction of the possible associations between fatigue and the components) and therapy outcome was investigated. RESULTS: 58 patients met inclusion criteria (m age = 36.5; 65.5% female) and data of 50 patients were analysed in the multilevel analysis. Two perpetuating components were found: "psychological discomfort" and "activity". For the total group, both perpetuating components did not predict fatigue on a following time-point. For individual patients the strength and direction of the associations varied. None of the associations between perpetuating components and fatigue significantly predicted treatment outcome. CONCLUSION: Results suggest that there is heterogeneity in perpetuation of fatigue in CFS/ME. Investigating fatigue and perpetuators on an individual rather than group level could lead to new insights.

Improving the Measurement of Functional Somatic Symptoms With Item Response Theory.

More than 40 questionnaires have been developed to assess functional somatic symptoms (FSS), but there are several methodological issues regarding the measurement of FSS. We aimed to identify which items of the somatization subscale of the Symptom Checklist-90 (SCL-90) are more informative and discriminative between persons at different levels of severity of FSS. To this end, item response theory was applied to the somatization scale of the SCL-90, collected from a sample of 82,740 adult participants without somatic conditions in the Lifelines Cohort Study. Sensitivity analyses were performed with all the participants who completed the somatization scale. Both analyses showed that Items 11 "feeling weak physically" and 12 "heavy feelings in arms or legs" were the most discriminative and informative to measure severity levels of FSS, regardless of somatic conditions. Clinicians and researchers may pay extra attention to these symptoms to augment the assessment of FSS.

Gender and sex independently associate with common somatic symptoms and lifetime prevalence of chronic disease.

Sex and gender influence health differently. Associations between sex and health have been extensively studied, but gender (i.e. psychosocial sex) has been largely neglected, partly due to the absence of gender measures in cohort studies. Therefore, our objective was to test the unique associations of gender and sex with common somatic symptoms and chronic diseases, using a gender index created from existing cohort data. We applied LASSO logistic regression to identify, out of 153 unique variables, psychosocial variables that were predictive of sex (i.e. gender-related) in the Dutch LifeLines Cohort Study. These psychosocial variables covered gender roles and institutionalized gender. Using the estimated coefficients, gender indexes were calculated for each adult participant in the study (n = 152,728; 58.5% female; mean age 44.6 (13.1) years). We applied multiple ordinal and logistic regression to test the unique associations of the gender index and sex, and their interactions, with common somatic symptoms assessed by the SCL-90 SOM and self-reported lifetime prevalence of chronic diseases, respectively. We found that in 10.1% of the participants the gender index was not in line with participants' sex: 12.5% of men and 8.4% of women showed a discrepancy between gender index and sex. Feminine gender characteristics are associated with increased common somatic symptoms and chronic diseases, especially in men. Female sex is associated with a higher common somatic symptom burden, but not with a higher prevalence of chronic diseases. The study shows that gender and sex uniquely impact health, and should be considered in epidemiological studies. Our methodology shows that consideration of gender measures in studies is necessary and feasible, based on data generally present in cohort studies.

Development of Grip self-help: An online patient-tailored self-help intervention for functional somatic symptoms in primary care.

Functional Somatic Symptoms (FSS) are somatic symptoms for which no somatic cause can be identified despite adequate diagnostic testing. FSS are common, costly, and disabling, and treatment options are limited. Psychotherapy is one of few evidence-based treatments for FSS. Yet, this form of therapy is not widely used, since it is usually reserved for severe symptoms, requires a highly trained therapist, and is not well accepted by patients. The current paper describes the development of the online intervention 'Grip self-help' and provides a description of the intervention itself. Grip self-help is an early intervention for mild to moderate FSS in primary care, which aims to reduce somatic symptoms and improve quality of life. In the Grip self-help intervention, patients fill out a set of online questionnaires exploring unhelpful cognitions, emotions, behaviors, and social factors associated with the symptoms. Using this information, a personal profile is generated, identifying factors that might maintain FSS in that individual. As a next step, patients are offered online self-help exercises that are tailored to these factors. Guidance is offered by a primary care professional. The intervention will ultimately result in a personalized self-help guide, composed of texts that are extracted from the exercises patients found useful during the intervention. Grip self-help is the first intervention for FSS combining the concepts of e-health, self-help, and personalized medicine. Guided by a primary care professional, patients are offered an easily accessible, yet highly personalized treatment. Grip self-help thus has the potential to meet the needs of the large group of patients with mild to moderate FSS.

Bayes factors for superiority, non-inferiority, and equivalence designs.

BACKGROUND: In clinical trials, study designs may focus on assessment of superiority, equivalence, or non-inferiority, of a new medicine or treatment as compared to a control. Typically, evidence in each of these paradigms is quantified with a variant of the null hypothesis significance test. A null hypothesis is assumed (null effect, inferior by a specific amount, inferior by a specific amount and superior by a specific amount, for superiority, non-inferiority, and equivalence respectively), after which the probabilities of obtaining data more extreme than those observed under these null hypotheses are quantified by p-values. Although ubiquitous in clinical testing, the null hypothesis significance test can lead to a number of difficulties in interpretation of the results of the statistical evidence. METHODS: We advocate quantifying evidence instead by means of Bayes factors and highlight how these can be calculated for different types of research design. RESULTS: We illustrate Bayes factors in practice with reanalyses of data from existing published studies. CONCLUSIONS: Bayes factors for superiority, non-inferiority, and equivalence designs allow for explicit quantification of evidence in favor of the null hypothesis. They also allow for interim testing without the need to employ explicit corrections for multiple testing.

The comparative evidence basis for the efficacy of second-generation antidepressants in the treatment of depression in the US: A Bayesian meta-analysis of Food and Drug Administration reviews.

BACKGROUND: Studies have shown similar efficacy of different antidepressants in the treatment of depression. METHOD: Data of phase-2 and -3 clinical-trials for 16 antidepressants (levomilnacipran, desvenlafaxine, duloxetine, venlafaxine, paroxetine, escitalopram, vortioxetine, mirtazapine, venlafaxine XR, sertraline, fluoxetine, citalopram, paroxetine CR, nefazodone, bupropion, vilazodone), approved by the FDA for the treatment of depression between 1987 and 2016, were extracted from the FDA reviews that were used to evaluate efficacy prior to marketing approval, which are less liable to reporting biases. Meta-analytic Bayes factors, which quantify the strength of evidence for efficacy, were calculated. In addition, posterior pooled effect-sizes were calculated and compared with classical estimations. RESULTS: The resulted Bayes factors showed that the evidence load for efficacy varied strongly across antidepressants. However, all tested drugs except for bupropion and vilazodone showed strong evidence for their efficacy. The posterior effect-size distributions showed variation across antidepressants, with the highest pooled estimated effect size for venlafaxine followed by paroxetine, and the lowest for bupropion and vilazodone. LIMITATIONS: Not all published trials were included in the study. CONCLUSIONS: The results illustrate the importance of considering both the effect size and the evidence-load when judging the efficacy of a treatment. In doing so, the currently employed Bayesian approach provided clear insights on top of those gained with traditional approaches.

Bayesian reanalysis of null results reported in medicine: Strong yet variable evidence for the absence of treatment effects.

Efficient medical progress requires that we know when a treatment effect is absent. We considered all 207 Original Articles published in the 2015 volume of the New England Journal of Medicine and found that 45 (21.7%) reported a null result for at least one of the primary outcome measures. Unfortunately, standard statistical analyses are unable to quantify the degree to which these null results actually support the null hypothesis. Such quantification is possible, however, by conducting a Bayesian hypothesis test. Here we reanalyzed a subset of 43 null results from 36 articles using a default Bayesian test for contingency tables. This Bayesian reanalysis revealed that, on average, the reported null results provided strong evidence for the absence of an effect. However, the degree of this evidence is variable and cannot be reliably predicted from the p-value. For null results, sample size is a better (albeit imperfect) predictor for the strength of evidence in favor of the null hypothesis. Together, our findings suggest that (a) the reported null results generally correspond to strong evidence in favor of the null hypothesis; (b) a Bayesian hypothesis test can provide additional information to assist the interpretation of null results.

Toward evidence-based medical statistics: a Bayesian analysis of double-blind placebo-controlled antidepressant trials in the treatment of anxiety disorders.

The Food and Drug Administration (FDA) uses a p < 0.05 null-hypothesis significance testing framework to evaluate "substantial evidence" for drug efficacy. This framework only allows dichotomous conclusions and does not quantify the strength of evidence supporting efficacy. The efficacy of FDA-approved antidepressants for the treatment of anxiety disorders was re-evaluated in a Bayesian framework that quantifies the strength of the evidence. Data from 58 double-blind placebo-controlled trials were retrieved from the FDA for the second-generation antidepressants for the treatment of anxiety disorders. Bayes factors (BFs) were calculated for all treatment arms compared to placebo and were compared with the corresponding p-values and the FDA conclusion categories. BFs ranged from 0.07 to 131,400, indicating a range of no support of evidence to strong evidence for the efficacy. Results also indicate a varying strength of evidence between the trials with p < 0.05. In sum, there were large differences in BFs across trials. Among trials providing "substantial evidence" according to the FDA, only 27 out of 59 dose groups obtained strong support for efficacy according to the typically used cutoff of BF ≥ 20. The Bayesian framework can provide valuable information on the strength of the evidence for drug efficacy. Copyright © 2016 John Wiley & Sons, Ltd.

Predicting long-term depression outcome using a three-mode principal component model for depression heterogeneity.

BACKGROUND: Depression heterogeneity has hampered development of adequate prognostic models. Therefore, more homogeneous clinical entities (e.g. dimensions, subtypes) have been developed, but their differentiating potential is limited because neither captures all relevant variation across persons, symptoms and time. To address this, three-mode Principal Component Analysis (3MPCA) was previously applied to capture person-, symptom- and time-level variation in a single model (Monden et al., 2015). This study evaluated the added prognostic value of such an integrated model for longer-term depression outcomes. METHODS: The Beck Depression Inventory (BDI) was administered quarterly for two years to major depressive disorder outpatients participating in a randomized controlled trial. A previously developed 3MPCA model decomposed the data into 2 symptom-components ('somatic-affective', 'cognitive'), 2 time-components ('recovering', 'persisting') and 3 person-components ('severe non-persisting depression', 'somatic depression' and 'cognitive depression'). The predictive value of the 3MPCA model for BDI scores at 3-year (n=136) and 11-year follow-up (n=145) was compared with traditional latent variable models and traditional prognostic factors (e.g. baseline BDI component scores, personality). RESULTS: 3MPCA components predicted 41% and 36% of the BDI variance at 3- and 11-year follow-up, respectively. A latent class model, growth mixture model and other known prognostic variables predicted 4-32% and 3-24% of the BDI variance at 3- and 11-year follow-up, respectively. LIMITATIONS: Only primary care patients were included. There was no independent validation sample. CONCLUSION: Accounting for depression heterogeneity at the person-, symptom- and time-level improves longer-term predictions of depression severity, underlining the potential of this approach for developing better prognostic models.

Simultaneous Decomposition of Depression Heterogeneity on the Person-, Symptom- and Time-Level: The Use of Three-Mode Principal Component Analysis.

Although heterogeneity of depression hinders research and clinical practice, attempts to reduce it with latent variable models have yielded inconsistent results, probably because these techniques cannot account for all interacting sources of heterogeneity at the same time. Therefore, to simultaneously decompose depression heterogeneity on the person-, symptom and time-level, three-mode Principal Component Analysis (3MPCA) was applied to data of 219 Major Depression patients, who provided Beck Depression Inventory assessments every three months for two years. The resulting person-level components were correlated with external baseline clinical and demographic variables. The 3MPCA extracted two symptom-level components ('cognitive', 'somatic-affective'), two time-level components ('improving', 'persisting') and three person-level components, characterized by different interaction-patterns between the symptom- and time-components ('severe non-persisting', 'somatic depression' and 'cognitive depression'). This model explained 28% of the total variance and 65% when also incorporating the general trend in the data). Correlations with external variables illustrated the content differentiation between the person-components. Severe non-persisting depression was positively correlated with psychopathology (r=0.60) and negatively with quality of life (r=-0.50). Somatic depression was negatively correlated with physical functioning (r=-0.45). Cognitive depression was positively correlated with neuroticism (r=0.38) and negatively with self-esteem (r=-0.47). In conclusion, 3MPCA decomposes depression into homogeneous entities, while accounting for the interactions between different sources of heterogeneity, which shows the utility of the technique to investigate the underlying structure of complex psychopathology data and could help future development of better empirical depression subtypes.