Two new methods to fit models for network meta-analysis with random inconsistency effects.

BACKGROUND: Meta-analysis is a valuable tool for combining evidence from multiple studies. Network meta-analysis is becoming more widely used as a means to compare multiple treatments in the same analysis. However, a network meta-analysis may exhibit inconsistency, whereby the treatment effect estimates do not agree across all trial designs, even after taking between-study heterogeneity into account. We propose two new estimation methods for network meta-analysis models with random inconsistency effects. METHODS: The model we consider is an extension of the conventional random-effects model for meta-analysis to the network meta-analysis setting and allows for potential inconsistency using random inconsistency effects. Our first new estimation method uses a Bayesian framework with empirically-based prior distributions for both the heterogeneity and the inconsistency variances. We fit the model using importance sampling and thereby avoid some of the difficulties that might be associated with using Markov Chain Monte Carlo (MCMC). However, we confirm the accuracy of our importance sampling method by comparing the results to those obtained using MCMC as the gold standard. The second new estimation method we describe uses a likelihood-based approach, implemented in the metafor package, which can be used to obtain (restricted) maximum-likelihood estimates of the model parameters and profile likelihood confidence intervals of the variance components. RESULTS: We illustrate the application of the methods using two contrasting examples. The first uses all-cause mortality as an outcome, and shows little evidence of between-study heterogeneity or inconsistency. The second uses "ear discharge" as an outcome, and exhibits substantial between-study heterogeneity and inconsistency. Both new estimation methods give results similar to those obtained using MCMC. CONCLUSIONS: The extent of heterogeneity and inconsistency should be assessed and reported in any network meta-analysis. Our two new methods can be used to fit models for network meta-analysis with random inconsistency effects. They are easily implemented using the accompanying R code in the Additional file 1. Using these estimation methods, the extent of inconsistency can be assessed and reported

Long-term effectiveness and cost-effectiveness of cognitive behavioural therapy as an adjunct to pharmacotherapy for treatment-resistant depression in primary care: follow-up of the CoBalT randomised controlled trial

Background: Cognitive behavioural therapy (CBT) is an effective treatment for people whose depression has not responded to antidepressants. However, the long-term outcome is unknown. In a long-term follow-up of the CoBalT trial, we examined the clinical and cost-effectiveness of cognitive behavioural therapy as an adjunct to usual care that included medication over 3–5 years in primary care patients with treatment-resistant depression. Methods: CoBalT was a randomised controlled trial done across 73 general practices in three UK centres. CoBalT recruited patients aged 18–75 years who had adhered to antidepressants for at least 6 weeks and had substantial depressive symptoms (Beck Depression Inventory [BDI-II] score ≥14 and met ICD-10 depression criteria). Participants were randomly assigned using a computer generated code, to receive either usual care or CBT in addition to usual care. Patients eligible for the long-term follow-up were those who had not withdrawn by the 12 month follow-up and had given their consent to being re-contacted. Those willing to participate were asked to return the postal questionnaire to the research team. One postal reminder was sent and non-responders were contacted by telephone to complete a brief questionnaire. Data were also collected from general practitioner notes. Follow-up took place at a variable interval after randomisation (3–5 years). The primary outcome was self-report of depressive symptoms assessed by BDI-II score (range 0–63), analysed by intention to treat. Cost-utility analysis compared health and social care costs with quality-adjusted life-years (QALYs). This study is registered with isrctn.com, number ISRCTN38231611. Findings: Between Nov 4, 2008, and Sept 30, 2010, 469 eligible participants were randomised into the CoBalT study. Of these, 248 individuals completed a long-term follow-up questionnaire and provided data for the primary outcome (136 in the intervention group vs 112 in the usual care group). At follow-up (median 45·5 months [IQR 42·5–51·1]), the intervention group had a mean BDI-II score of 19·2 (SD 13·8) compared with a mean BDI-II score of 23·4 (SD 13·2) for the usual care group (repeated measures analysis over the 46 months: difference in means −4·7 [95% CI −6·4 to −3·0, p<0·001]). Follow-up was, on average, 40 months after therapy ended. The average annual cost of trial CBT per participant was £343 (SD 129). The incremental cost-effectiveness ratio was £5374 per QALY gain. This represented a 92% probability of being cost effective at the National Institute for Health and Care Excellence QALY threshold of £20 000. Interpretation: CBT as an adjunct to usual care that includes antidepressants is clinically effective and cost effective over the long-term for individuals whose depression has not responded to pharmacotherapy. In view of this robust evidence of long-term effectiveness and the fact that the intervention represented good value-for-money, clinicians should discuss referral for CBT with all those for whom antidepressants are not effective

What do our school reports really say?

School reports are an enduring feature of the education landscape. They form part of our personal history, fondly retained by parents well beyond a child’s school leaving age. The Department for Education requires schools in England to report to parents annually(Department for Education, 2015). There is widespread variation in reporting practice and many schools are doing more than is legally required of them(Power and Clark, 2000).While frequent, data focused reports are commonly used, many schools continue to write comment-based reports as part of their reporting regime. As students move into secondary school, reports of their day to day learning become less forthcoming from the students themselves and reports become one of very few channels of home-school communication

Between-trial heterogeneity in meta-analyses may be partially explained by reported design characteristics.

OBJECTIVE: We investigated the associations between risk of bias judgments from Cochrane reviews for sequence generation, allocation concealment and blinding, and between-trial heterogeneity. STUDY DESIGN AND SETTING: Bayesian hierarchical models were fitted to binary data from 117 meta-analyses, to estimate the ratio λ by which heterogeneity changes for trials at high/unclear risk of bias compared with trials at low risk of bias. We estimated the proportion of between-trial heterogeneity in each meta-analysis that could be explained by the bias associated with specific design characteristics. RESULTS: Univariable analyses showed that heterogeneity variances were, on average, increased among trials at high/unclear risk of bias for sequence generation (λˆ 1.14, 95% interval: 0.57-2.30) and blinding (λˆ 1.74, 95% interval: 0.85-3.47). Trials at high/unclear risk of bias for allocation concealment were on average less heterogeneous (λˆ 0.75, 95% interval: 0.35-1.61). Multivariable analyses showed that a median of 37% (95% interval: 0-71%) heterogeneity variance could be explained by trials at high/unclear risk of bias for sequence generation, allocation concealment, and/or blinding. All 95% intervals for changes in heterogeneity were wide and included the null of no difference. CONCLUSION: Our interpretation of the results is limited by imprecise estimates. There is some indication that between-trial heterogeneity could be partially explained by reported design characteristics, and hence adjustment for bias could potentially improve accuracy of meta-analysis results

Implementing informative priors for heterogeneity in meta-analysis using meta-regression and pseudo data.

Many meta-analyses combine results from only a small number of studies, a situation in which the between-study variance is imprecisely estimated when standard methods are applied. Bayesian meta-analysis allows incorporation of external evidence on heterogeneity, providing the potential for more robust inference on the effect size of interest. We present a method for performing Bayesian meta-analysis using data augmentation, in which we represent an informative conjugate prior for between-study variance by pseudo data and use meta-regression for estimation. To assist in this, we derive predictive inverse-gamma distributions for the between-study variance expected in future meta-analyses. These may serve as priors for heterogeneity in new meta-analyses. In a simulation study, we compare approximate Bayesian methods using meta-regression and pseudo data against fully Bayesian approaches based on importance sampling techniques and Markov chain Monte Carlo (MCMC). We compare the frequentist properties of these Bayesian methods with those of the commonly used frequentist DerSimonian and Laird procedure. The method is implemented in standard statistical software and provides a less complex alternative to standard MCMC approaches. An importance sampling approach produces almost identical results to standard MCMC approaches, and results obtained through meta-regression and pseudo data are very similar. On average, data augmentation provides closer results to MCMC, if implemented using restricted maximum likelihood estimation rather than DerSimonian and Laird or maximum likelihood estimation. The methods are applied to real datasets, and an extension to network meta-analysis is described. The proposed method facilitates Bayesian meta-analysis in a way that is accessible to applied researchers. © 2016 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd

Falling admissions to hospital with febrile seizures in the UK

Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.Peer reviewedPostprin

Aspects of alcohol use disorder affecting social cognition as assessed using the Mini Social and Emotional Assessment (mini-SEA)

Background Alcohol Use Disorder (AUD) is associated with problems with processing complex social scenarios. Little is known about the relationship between distinct AUD-related factors (e.g., years of problematic drinking), aspects of cognitive function and dysfunction in individuals diagnosed with AUD, and the relative impact these may have on social cognition. Aims To explore differences in social cognition between a group of participants diagnosed with AUD and controls, using a clinical measure, the Mini Social and Emotional Assessment (mini-SEA). The mini-SEA was used to evaluate social and emotional understanding through a facial emotional recognition task and by utilising a series of social scenes some of which contain a faux pas (social error). Methods Eighty-five participants (individuals with AUD and controls) completed demographic questions and a general cognitive and social cognitive test battery over three consecutive days. Results Between group analyses revealed that the participants with AUD performed less well on the faux pas test, and differences were also revealed in the emotional facial recognition task. Years of problematic alcohol consumption was the strongest predictor of poor ToM reasoning. Conclusion These results suggest a strong link between AUD chronicity and social cognition, though the direction of this relationship needs further elucidation. This may be of clinical relevance to abstinence and relapse management, as basic social cognition skills and ability to maintain interpersonal relationships are likely to be crucial to recovery

Essential requirement for polypyrimidine tract binding proteins 1 and 3 in the maturation and maintenance of mature B cells in mice

Abstract: The maturation of immature B cells and the survival of mature B cells is stringently controlled to maintain a diverse repertoire of antibody specificities while avoiding self‐reactivity. At the molecular level this is regulated by signaling from membrane Ig and the BAFF‐receptor that sustain a pro‐survival program of gene expression. Whether and how posttranscriptional mechanisms contribute to B cell maturation and survival remains poorly understood. Here, we show that the polypyrimidine tract binding proteins (PTBP) PTBP1 and PTBP3 bind to a large and overlapping set of transcripts in B cells. Both PTBP1 and PTBP3 bind to introns and exons where they are predicted to regulate alternative splicing. Moreover, they also show high‐density of binding to 3’ untranslated regions suggesting they influence the transcriptome in diverse ways. We show that PTBP1 and PTBP3 are required in B cells beyond the immature cell stage to sustain transitional B cells and the B1, marginal zone and follicular B cell lineages. Therefore, PTBP1 and PTBP3 promote the maturation of quiescent B cells by regulating gene expression at the posttranscriptional level