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Coordinate-based meta-analyses (CBMA) allow researchers to combine the results from multiple functional magnetic resonance imaging experiments with the goal of obtaining results that are more likely to generalize. However, the interpretation of CBMA findings can be impaired by the file drawer problem, a type of publication bias that refers to experiments that are carried out but are not published. Using foci per contrast count data from the BrainMap database, we propose a zero-truncated modeling approach that allows us to estimate the prevalence of nonsignificant experiments. We validate our method with simulations and real coordinate data generated from the Human Connectome Project. Application of our method to the data from BrainMap provides evidence for the existence of a file drawer effect, with the rate of missing experiments estimated as at least 6 per 100 reported. The R code that we used is available at https://osf.io/ayhfv/.

Assessing the impact of an intervention by using time-series observational data on multiple units and outcomes is a frequent problem in many fields of scientific research. Here, we propose a novel Bayesian multivariate factor analysis model for estimating intervention effects in such settings and develop an efficient Markov chain Monte Carlo algorithm to sample from the high-dimensional and nontractable posterior of interest. The proposed method is one of the few that can simultaneously deal with outcomes of mixed type (continuous, binomial, count), increase efficiency in the estimates of the causal effects by jointly modeling multiple outcomes affected by the intervention, and easily provide uncertainty quantification for all causal estimands of interest. Using the proposed approach, we evaluate the impact that Local Tracing Partnerships had on the effectiveness of England's Test and Trace programme for COVID-19. [ABSTRACT FROM AUTHOR]

Many people with chronic hepatitis C infection don't engage in treatment. To eliminate hepatitis C and avoid health inequalities therapy must be provided to everyone. In other diseases peers with lived experience of the condition have improved care but, for hepatitis C, studies have not shown unequivocal benefit. We completed a retrospective analysis of the English National Health Service treatment registry comparing treatment networks with and without peers using Bayesian Poisson (for count outcomes) or Bayesian Binomial (for proportion outcomes) mixed effects models with time fixed effects. For each outcome, we estimated relative ratio (RR‐Poisson model) or odds ratio (Odds Ratio (OR)‐Binomial model) between peer and non‐peer networks. We analysed 30,729 patients within 20 operational delivery networks. In networks with peers there was an increase in the number of people initiating therapy (RR 1.12 95%, credible interval 1.02–1.21) and an increase in the proportion completing therapy (OR 2.45 95%, credible interval 1.49–3.84). However, we saw no change in proportions of people using drugs who initiated therapy nor any significant change in virological response (OR 1.14 95% credible interval 0.979–1.36). We repeated the analysis looking at the impact of peers two months after they had been introduced, when they had established networks of contacts, and saw an increase in the proportion of people treated in addiction services. In treating patients with chronic hepatitis C infection the inclusion of peer supporters may increase the number of people who initiate and complete antiviral therapy. [ABSTRACT FROM AUTHOR]

Background: There is limited empirical work assessing the effectiveness of treatment as prevention (TasP) in reducing HCV prevalence among people who inject drugs (PWID). Here, we used survey data from the UK during 2010-2020, to evaluate the impact of direct-acting antiviral agent (DAA) treatment scale-up, which started in 2015, on HCV prevalence among PWID., Methods: We fitted a logistic regression to time/location specific data on prevalence from the Needle Exchange Surveillance Initiative in Scotland and Unlinked Anonymous Monitoring programme in England. For each post-intervention year and location, we quantified the effect of TasP as the difference between estimated prevalence and its counterfactual (prevalence in the absence of scale-up). Progress to elimination was assessed by comparing most recent prevalence against one in 2015., Results: In 2015, prevalence ranged from 0.44 to 0.71 across the 23 locations (3 Scottish, 20 English). Compared to counterfactuals, there was an absolute reduction of 46% (95% credible interval [32%,59%]) in Tayside in 2020, 35% ([24%,44%]) in Glasgow in 2019, and 25% ([10%,39%]) in the Rest of Scotland in 2020. The English sites with highest estimated absolute reductions in 2021 were South Yorkshire (45%, [29%,58%]), Thames Valley (49%, [34%,59%]) and West London (41%, [14%,59%]). Compared to 2015, there was 80% probability that prevalence had fallen by 65% in Tayside, 53% in Glasgow and 36% in the Rest of Scotland. The English sites with highest % prevalence decrease compared to 2015, achieved with probability 80%, were Chesire & Merseyside (70%), South Yorkshire (65%) and Thames Valley (71%). Higher treatment intensity was associated with higher reductions in prevalence., Conclusion: Conclusion. Real-world evidence showing substantial reductions in chronic HCV associated with increase of HCV treatment scale-up in the community thus supporting the effectiveness of HCV treatmen as prevention in people who inject drugs., Competing Interests: Declaration of competing interest None., (Copyright © 2024. Published by Elsevier B.V.)

A problem that is frequently encountered in many areas of scientific research is that of estimating the effect of a non-randomized binary intervention on an outcome of interest by using time series data on units that received the intervention ('treated') and units that did not ('controls'). One popular estimation method in this setting is based on the factor analysis (FA) model. The FA model is fitted to the preintervention outcome data on treated units and all the outcome data on control units, and the counterfactual treatment-free post-intervention outcomes of the former are predicted from the fitted model. Intervention effects are estimated as the observed outcomes minus these predicted counterfactual outcomes. We propose a model that extends the FA model for estimating intervention effects by jointly modelling the multiple outcomes to exploit shared variability, and assuming an auto-regressive structure on factors to account for temporal correlations in the outcome. Using simulation studies, we show that the method proposed can improve the precision of the intervention effect estimates and achieve better control of the type I error rate (compared with the FA model), especially when either the number of preintervention measurements or the number of control units is small. We apply our method to estimate the effect of stricter alcohol licensing policies on alcohol-related harms.

Working memory (WM) was one of the first cognitive processes studied with functional magnetic resonance imaging (fMRI). With now over 20 years of studies on WM, each study with tiny sample sizes, there is a need for meta-analysis to identify the brain regions consistently activated by WM tasks, and to understand the inter-study variation in those activations. However, current methods in the field cannot fully account for the spatial nature of neuroimaging meta-analysis data or the heterogeneity observed among WM studies. In this work, we propose a fully Bayesian random-effects meta-regression model based on log-Gaussian Cox processes, which can be used for meta-analysis of neuroimaging studies. An efficient MCMC scheme for posterior simulations is presented which makes use of some recent advances in parallel computing using graphics processing units (GPUs). Application of the proposed model to a real dataset provides valuable insights regarding the function of the WM.