Arsenic in drinking water and lung cancer: A systematic review of 35 years of evidence.

The association between higher arsenic concentrations in drinking water and lung cancer is well-established. However, the risk associated with lower levels of arsenic exposure remains uncertain. This systematic review and meta-analysis summarizes the evidence on the relationship between exposure to arsenic in drinking water and lung cancer outcomes as measured over a broad range of exposures, including lower levels. A total of 51 studies were included in the review and 15 met criteria for inclusion in meta-analysis. Risk estimates for lung cancer incidence and mortality were pooled and analyzed separately using Bayesian hierarchical random-effects models with a Gaussian observation submodel for log(Risk), computed using the "brms" R package. For lung cancer incidence, the predicted posterior mean relative risks (RRs) at arsenic concentrations of 10, 50 and 150 µg/L were 1.11 (0.86-1.43), 1.67 (1.27-2.17) and 2.21 (1.61-3.02), respectively, with posterior probabilities of 79%, 100% and 100%, respectively, for the RRs to be >1. The posterior mean mortality ratios at 20, 50 and 150 µg/L were 1.22 (0.83-1.78), 2.10 (1.62-2.71) and 2.41 (1.88-3.08), respectively, with posterior probabilities being above 80%. In addition to observing the dose-response relationship, these findings demonstrate that individuals exposed to low to moderate levels of arsenic (<150 µg/L) were at an elevated risk of developing or dying from lung cancer. Given the widespread exposure to lower levels of arsenic, there is an urgent need for vigilance and potential revisions to regulatory guidelines to protect people from the cancer risks associated with arsenic exposure.

Large-scale data decipher children's scale errors: A meta-analytic approach using the zero-inflated Poisson models.

Scale errors are intriguing phenomena in which a child tries to perform an object-specific action on a tiny object. Several viewpoints explaining the developmental mechanisms underlying scale errors exist; however, there is no unified account of how different factors interact and affect scale errors, and the statistical approaches used in the previous research do not adequately capture the structure of the data. By conducting a secondary analysis of aggregated datasets across nine different studies (n = 528) and using more appropriate statistical methods, this study provides a more accurate description of the development of scale errors. We implemented the zero-inflated Poisson (ZIP) regression that could directly handle the count data with a stack of zero observations and regarded developmental indices as continuous variables. The results suggested that the developmental trend of scale errors was well documented by an inverted U-shaped curve rather than a simple linear function, although nonlinearity captured different aspects of the scale errors between the laboratory and classroom data. We also found that repeated experiences with scale error tasks reduced the number of scale errors, whereas girls made more scale errors than boys. Furthermore, a model comparison approach revealed that predicate vocabulary size (e.g., adjectives or verbs), predicted developmental changes in scale errors better than noun vocabulary size, particularly in terms of the presence or absence of scale errors. The application of the ZIP model enables researchers to discern how different factors affect scale error production, thereby providing new insights into demystifying the mechanisms underlying these phenomena. A video abstract of this article can be viewed at https://youtu.be/1v1U6CjDZ1Q RESEARCH HIGHLIGHTS: We fit a large dataset by aggregating the existing scale error data to the zero-inflated Poisson (ZIP) model. Scale errors peaked along the different developmental indices, but the underlying statistical structure differed between the in-lab and classroom datasets. Repeated experiences with scale error tasks and the children's gender affected the number of scale errors produced per session. Predicate vocabulary size (e.g., adjectives or verbs) better predicts developmental changes in scale errors than noun vocabulary size.

Clinicopathological characteristics and diagnostic accuracy of BRAF mutations in ameloblastoma: A Bayesian network analysis.

OBJECTIVE: This Bayesian network meta-analysis was performed to analyze the associations between clinicopathological characteristics and BRAF mutations in ameloblastoma (AM) patients and to evaluate the diagnostic accuracy. MATERIALS AND METHODS: Four electronic databases were searched from 2010 to 2024. The search terms used were specific to BRAF and AM. Observational studies or randomized controlled trials were considered eligible. The incidence of BRAF mutation and corresponding clinicopathological features in AM patients were subjected to Bayesian network analyses and diagnostic accuracy evaluation. RESULTS: A total of 937 AM patients from 20 studies were included. The pooled prevalence of BRAF mutations in AM patients was 72%. According to the Bayesian network analysis, BRAF mutations are more likely to occur in younger (odds ratio [OR], 2.3; credible interval [CrI]: 1.2-4.5), mandible site (OR, 3.6; 95% CrI: 2.7-5.2), and unicystic (OR, 1.6; 95% CrI: 1.1-2.4) AM patients. Similarly, higher diagnostic accuracy was found in the younger, mandible, and unicystic AM groups. CONCLUSIONS: The incidence, risk, and diagnostic accuracy of BRAF mutation in AM were greater in younger patients, those with mandible involvement, and those with unicystic AM than in patients with other clinicopathological features. In addition, there was a strong concordance in the diagnostic accuracy between molecular tests and immunohistochemical analysis.

DE-PASS best evidence statement (BESt): determinants of adolescents' device-based physical activity and sedentary behaviour in settings: a systematic review and meta-analysis.

BACKGROUND: Although physical activity (PA) is associated with significant health benefits, only a small percentage of adolescents meet recommended PA levels. This systematic review with meta-analysis explored the modifiable determinants of adolescents' device-based PA and/or sedentary behaviour (SB), evaluated in previous interventions and examined the associations between PA/SB and these determinants in settings. METHODS: A search was conducted on five electronic databases, including papers published from January 2010 to July 2023. Randomized Controlled Trials (RCTs) or Controlled Trials (CTs) measuring adolescents' device-based PA/SB and their modifiable determinants at least at two time points: pre- and post-intervention were considered eligible. PA/SB and determinants were the main outcomes. Modifiable determinants were classified after data extraction adopting the social-ecological perspective. Robust Bayesian meta-analyses (RoBMA) were performed per each study setting. Outcomes identified in only one study were presented narratively. The risk of bias for each study and the certainty of the evidence for each meta-analysis were evaluated. The publication bias was also checked. PROSPERO ID: CRD42021282874. RESULTS: Fourteen RCTs (eight in school, three in school and family, and one in the family setting) and one CT (in the school setting) were included. Fifty-four modifiable determinants were identified and were combined into 33 broader determinants (21 individual-psychological, four individual-behavioural, seven interpersonal, and one institutional). RoBMAs revealed none or negligible pooled intervention effects on PA/SB or determinants in all settings. The certainty of the evidence of the impact of interventions on outcomes ranged from very low to low. Narratively, intervention effects in favour of the experimental group were detected in school setting for the determinants: knowledge of the environment for practicing PA, d = 1.84, 95%CI (1.48, 2.20), behaviour change techniques, d = 0.90, 95%CI (0.09, 1.70), choice provided, d = 0.70, 95%CI (0.36, 1.03), but no corresponding effects on PA or SB were found. CONCLUSIONS: Weak to minimal evidence regarding the associations between the identified modifiable determinants and adolescents' device-based PA/SB in settings were found, probably due to intervention ineffectiveness. Well-designed and well-implemented multicomponent interventions should further explore the variety of modifiable determinants of adolescents' PA/SB, including policy and environmental variables.

Systematic update to the mammalian relative potency estimate database and development of best estimate toxic equivalency factors for dioxin-like compounds.

The World Health Organization (WHO) assesses potential health risks of dioxin-like compounds using Toxic Equivalency Factors (TEFs). This study systematically updated the relative potency (REP) database underlying the 2005 WHO TEFs and applied advanced methods for quantitative integration of study quality and dose-response. Data obtained from fifty-one publications more than doubled the size of the previous REP database (∼1300 datasets). REP quality and relevance for these data was assessed via application of a consensus-based weighting framework. Using Bayesian dose-response modeling, available data were modeled to produce standardized dose/concentration-response Hill curves. Study quality and REP data were synthesized via Bayesian meta-analysis to integrate dose/concentration-response data, author-calculated REPs and benchmark ratios. The output is a prediction of the most likely relationship between each congener and its reference as model-predicted TEF uncertainty distributions, or the 'best estimate TEF' (BE-TEF). The resulting weighted BE-TEFs were similar to the 2005 TEFs, though provide more information to inform selection of TEF values as well as to provide risk assessors and managers with information needed to quantitatively characterize uncertainty around TEF values. Collectively, these efforts produce an updated REP database and an objective, reproducible approach to support development of TEF values based on all available data.

Comparing various Bayesian random-effects models for pooling randomized controlled trials with rare events.

The meta-analysis of rare events presents unique methodological challenges owing to the small number of events. Bayesian methods are often used to combine rare events data to inform decision-making, as they can incorporate prior information and handle studies with zero events without the need for continuity corrections. However, the comparative performances of different Bayesian models in pooling rare events data are not well understood. We conducted a simulation to compare the statistical properties of four parameterizations based on the binomial-normal hierarchical model, using two different priors for the treatment effect: weakly informative prior (WIP) and non-informative prior (NIP), pooling randomized controlled trials with rare events using the odds ratio metric. We also considered the beta-binomial model proposed by Kuss and the random intercept and slope generalized linear mixed models. The simulation scenarios varied based on the treatment effect, sample size ratio between the treatment and control arms, and level of heterogeneity. Performance was evaluated using median bias, root mean square error, median width of 95% credible or confidence intervals, coverage, Type I error, and empirical power. Two reviews are used to illustrate these methods. The results demonstrate that the WIP outperforms the NIP within the same model structure. Among the compared models, the model that included the treatment effect parameter in the risk model for the control arm did not perform well. Our findings confirm that rare events meta-analysis faces the challenge of being underpowered, highlighting the importance of reporting the power of results in empirical studies.

Associations of water, sanitation, and hygiene with typhoid fever in case-control studies: a systematic review and meta-analysis.

BACKGROUND: Water, sanitation, and hygiene (WASH) play a pivotal role in controlling typhoid fever, as it is primarily transmitted through oral-fecal pathways. Given our constrained resources, staying current with the most recent research is crucial. This ensures we remain informed about practical insights regarding effective typhoid fever control strategies across various WASH components. We conducted a systematic review and meta-analysis of case-control studies to estimate the associations of water, sanitation, and hygiene exposures with typhoid fever. METHODS: We updated the previous review conducted by Brockett et al. We included new findings published between June 2018 and October 2022 in Web of Science, Embase, and PubMed. We used the Risk of Bias in Non-Randomized Studies of Interventions (ROBINS-I) tool for risk of bias (ROB) assessment. We classified WASH exposures according to the classification provided by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation, and Hygiene (JMP) update in 2015. We conducted the meta-analyses by only including studies that did not have a critical ROB in both Bayesian and frequentist random-effects models. RESULTS: We identified 8 new studies and analyzed 27 studies in total. Our analyses showed that while the general insights on the protective (or harmful) impact of improved (or unimproved) WASH remain the same, the pooled estimates of OR differed. Pooled estimates of limited hygiene (OR = 2.26, 95% CrI: 1.38 to 3.64), untreated water (OR = 1.96, 95% CrI: 1.28 to 3.27) and surface water (OR = 2.14, 95% CrI: 1.03 to 4.06) showed 3% increase, 18% decrease, and 16% increase, respectively, from the existing estimates. On the other hand, improved WASH reduced the odds of typhoid fever with pooled estimates for improved water source (OR = 0.54, 95% CrI: 0.31 to 1.08), basic hygiene (OR = 0.6, 95% CrI: 0.38 to 0.97) and treated water (OR = 0.54, 95% CrI: 0.36 to 0.8) showing 26% decrease, 15% increase, and 8% decrease, respectively, from the existing estimates. CONCLUSIONS: The updated pooled estimates of ORs for the association of WASH with typhoid fever showed clear changes from the existing estimates. Our study affirms that relatively low-cost WASH strategies such as basic hygiene or water treatment can be an effective tool to provide protection against typhoid fever in addition to other resource-intensive ways to improve WASH. TRIAL REGISTRATION: PROSPERO 2021 CRD42021271881.

Does response inhibition occur unconsciously? A systematic review and Bayesian meta-analysis.

Consciousness is traditionally considered necessary for response inhibition. Recently, researchers have attempted to explore unconscious response inhibition using the masked go/no-go task. However, their findings were controversial and might have been confounded by the methodology employed. Therefore, we used a three-level Bayesian meta-analysis to provide the first systematic overview of the field of unconscious response inhibition. Finally, 34 studies in 16 articles with a total sample size of 521 were included. In summary, we found only inconclusive evidence of a reaction time slowing effect after excluding studies with conscious no-go experience (mean difference = 8.47 ms, BF10 = 2.71). In addition, the overall effect size of the difference in sensitivity to masked stimuli between the masked go/no-go task and the objective awareness task was small and uncertain (mean difference = 0.09, BF10 = 2.39). Taken together, these findings indicate a lack of solid evidence for the occurrence of unconscious response inhibition. Our findings do not oppose the possibility of unconscious response inhibition, but rather emphasize the need for more rigorous research methodologies in this field.

Behaviour change techniques in cardiovascular disease smartphone apps to improve physical activity and sedentary behaviour: Systematic review and meta-regression.

BACKGROUND: Smartphone apps are increasingly used to deliver physical activity and sedentary behaviour interventions for people with cardiovascular disease. However, the active components of these interventions which aim to change behaviours are unclear. AIMS: To identify behaviour change techniques used in smartphone app interventions for improving physical activity and sedentary behaviour in people with cardiovascular disease. Secondly, to investigate the association of the identified techniques on improving these behaviours. METHODS: Six databases (Medline, CINAHL Plus, Cochrane Library, SCOPUS, Sports Discus, EMBASE) were searched from 2007 to October 2020. Eligible studies used a smartphone app intervention for people with cardiovascular disease and reported a physical activity and/or sedentary behaviour outcome. The behaviour change techniques used within the apps for physical activity and/or sedentary behaviour were coded using the Behaviour Change Technique Taxonomy (v1). The association of behaviour change techniques on physical activity outcomes were explored through meta-regression. RESULTS: Forty behaviour change techniques were identified across the 19 included app-based interventions. Only two studies reported the behaviour change techniques used to target sedentary behaviour change. The most frequently used techniques for sedentary behaviour and physical activity were habit reversal and self-monitoring of behaviour respectively. In univariable analyses, action planning (ß =0.42, 90%CrI 0.07-0.78) and graded tasks (ß =0.33, 90%CrI -0.04-0.67) each had medium positive associations with increasing physical activity. Participants in interventions that used either self-monitoring outcome(s) of behaviour (i.e. outcomes other than physical activity) (ß = - 0.47, 90%CrI -0.79--0.16), biofeedback (ß = - 0.47, 90%CrI -0.81--0.15) and information about health consequences (ß = - 0.42, 90%CrI -0.74--0.07) as behaviour change techniques, appeared to do less physical activity. In the multivariable model, these predictors were not clearly removed from zero. CONCLUSION: The behaviour change techniques action planning and graded tasks are good candidates for causal testing in future experimental smartphone app designs.

Selective serotonin reuptake inhibitors in the treatment of depression, anxiety, and post-traumatic stress disorder in substance use disorders: a Bayesian meta-analysis.

PURPOSE: Examine SSRIs' efficacy in treating depression, anxiety, PTSD, and substance use in individuals with addiction. METHODS: From their inception until August 6, 2021, we searched Google Scholar, PubMed, Scopus, OVID MEDLINE, and Academic Search Complete. We included randomized controlled trials (RCTs) and omitted open-label studies. Bayesian analysis was performed. Bayes factor (BF) established efficacy and tau (τ) statistical heterogeneity. The RoB2 method assessed potential biases. Subgroup analysis was carried out to determine SSRI performance. Treatment duration, SSRI dosage, and attrition rate were all examined in meta-regression. RESULTS: We investigated 64 RCTs with 6128 participants. SSRIs reduced depressive symptoms in opioid, alcohol, cocaine, cannabis, and nicotine use disorders (d = 0.353, BF > 99); social anxiety symptoms in alcohol use disorder (d = 0.875, BF > 99); and generalized anxiety symptoms in opioid, alcohol, cocaine, marijuana, and nicotine use disorders (d = 0.346, BF = 4.236). Evidence for PTSD was inconclusive. SSRIs facilitated abstinence for opioid, alcohol, cocaine, cannabis, and nicotine use (d = 0.325, BF > 99); reduced craving for alcohol, cocaine, and nicotine use (d = 0.533, BF = 24.129); and reduced alcohol use (d = 0.452, BF > 99) and cocaine use (d = 0.255, BF = 3.87). Fluoxetine showed the highest antidepressant effect. There was no effect of attrition rate, SSRI dosage, or treatment length on SSRI's efficacy. CONCLUSIONS: Results support the use of SSRIs to treat substance use, depression, and anxiety in individuals with addiction. PROTOCOL REGISTRATION: PROSPERO registration number: CRD42020164944.

Bayesian analysis of a systematic review of early versus late tracheostomy in ICU patients.

BACKGROUND: A recent systematic review and meta-analysis of RCTs of early vs late tracheostomy in mechanically ventilated patients suggest that early tracheostomy reduces the duration of ICU stay and mechanical ventilation, but does not reduce short-term mortality or ventilator-associated pneumonia (VAP). Meta-analysis of randomised trials is typically performed using a frequentist approach, and although reporting confidence intervals, interpretation is usually based on statistical significance. To provide a robust basis for clinical decision-making, we completed the search used from the previous review and analysed the data using Bayesian methods to estimate posterior probabilities of the effect of early tracheostomy on clinical outcomes. METHODS: The search was completed for RCTS comparing early vs late tracheostomy in the databases PubMed, EMBASE, and Cochrane library in June 2022. Effect estimates and 95% confidence intervals were calculated for the outcomes short-term mortality, VAP, duration of ICU stay, and mechanical ventilation. A Bayesian meta-analysis was performed with uninformative priors. Risk ratios (RRs) and standardised mean differences (SMDs) with 95% credible intervals were reported alongside posterior probabilities for any benefit (RR<1; SMD<0), a small benefit (number needed to treat, 200; SMD<-0.5), or modest benefit (number needed to treat, 100; SMD<-1). RESULTS: Nineteen RCTs with 3508 patients were included. Comparing patients with early vs late tracheostomy, the posterior probabilities for any benefit, small benefit, and modest benefit, respectively, were: 99%, 99%, and 99% for short-term mortality; 94%, 78%, and 51% for VAP; 97%, 43%, and 1% for duration of mechanical ventilation; and 97%, 75%, and 27% and for length of ICU stay. CONCLUSIONS: Bayesian meta-analysis suggests a high probability that early tracheostomy compared with delayed tracheostomy has at least some benefit across all clinical outcomes considered.

A Cortical Surface-Based Meta-Analysis of Human Reasoning.

Recent advances in neuroimaging have augmented numerous findings in the human reasoning process but have yielded varying results. One possibility for this inconsistency is that reasoning is such an intricate cognitive process, involving attention, memory, executive functions, symbolic processing, and fluid intelligence, whereby various brain regions are inevitably implicated in orchestrating the process. Therefore, researchers have used meta-analyses for a better understanding of neural mechanisms of reasoning. However, previous meta-analysis techniques include weaknesses such as an inadequate representation of the cortical surface's highly folded geometry. Accordingly, we developed a new meta-analysis method called Bayesian meta-analysis of the cortical surface (BMACS). BMACS offers a fast, accurate, and accessible inference of the spatial patterns of cognitive processes from peak brain activations across studies by applying spatial point processes to the cortical surface. Using BMACS, we found that the common pattern of activations from inductive and deductive reasoning was colocalized with the multiple-demand system, indicating that reasoning is a high-level convergence of complex cognitive processes. We hope surface-based meta-analysis will be facilitated by BMACS, bringing more profound knowledge of various cognitive processes.

Risk of hematoma after hemithyroidectomy in an outpatient setting: a systematic review and meta-analysis.

PURPOSE: After thyroid surgery, the overriding concern is the risk of post-thyroid bleeding (PTB). This systematic review and meta-analysis aimed to assess the safety of hemithyroidectomy in an outpatient setting compared to an inpatient setting. The objectives were to (1) find the proportion of PTB in patients scheduled for outpatient hemithyroidectomy, (2) examine if outpatient hemithyroidectomy is clinically safe compared to an inpatient setting and (3) evaluate which selection criteria are most relevant for hemithyroidectomy in an outpatient setting. METHODS: A systematic review was conducted using the following databases: MEDLINE (Ovid), EMBASE (Ovid) and the Cochrane Library from inception until September 2021. We included studies reporting on PTB of patients after hemithyroidectomy in an outpatient setting. The risk of bias was assessed using the Newcastle-Ottawa tool. The results were synthesised using Bayesian meta-analysis. Certainty in evidence was assessed using the GRADE approach. RESULTS: This review included 11 cohort studies and 9 descriptive studies reporting solely on outpatients resulting in a total of 46,866 patients. PTB was experienced by 58 of the 9025 outpatients (0.6%) and 415 of the 37,841 inpatients (1.1%). There was no difference between the PTB rate of outpatients and inpatients (RR 0.715 CrI [0.396-1.243]). The certainty of the evidence was very low due to the high risk of bias. CONCLUSION: The risk of PTB in an outpatient setting is very low, and outpatient hemithyroidectomy should be considered clinically safe. The most relevant selection criteria to consider in outpatient hemithyroidectomy are (1) relevant comorbidities and (2) psycho/-social factors.

A hierarchical, multivariate meta-analysis approach to synthesising global change experiments.

Meta-analyses enable synthesis of results from globally distributed experiments to draw general conclusions about the impacts of global change factors on ecosystem function. Traditional meta-analyses, however, are challenged by the complexity and diversity of experimental results. We illustrate how several key issues can be addressed by a multivariate, hierarchical Bayesian meta-analysis (MHBM) approach applied to information extracted from published studies. We applied an MHBM to log-response ratios for aboveground biomass (AB, n = 300), belowground biomass (BB, n = 205) and soil CO2 exchange (SCE, n = 544), representing 100 studies. The MHBM accounted for study duration, climate effects and covariation among the AB, BB and SCE responses to elevated CO2 (eCO2 ) and/or warming. The MHBM revealed significant among-study covariation in the AB and BB responses to experimental treatments. The MHBM imputed missing duration (4.2%) and climate (6%) data, and revealed that climate context governs how eCO2 and warming impact ecosystem function. Predictions identified biomes that may be particularly sensitive to eCO2 or warming, but that are under-represented in global change experiments. The MHBM approach offers a flexible and powerful tool for synthesising disparate experimental results reported across multiple studies, sites and response variables.

C-reactive protein and fracture risk: an updated systematic review and meta-analysis of cohort studies through the use of both frequentist and Bayesian approaches.

This systematic review and meta-analysis were conducted on all eligible cohort studies to evaluate the association between high-sensitivity C-reactive protein (hs-CRP) and osteoporotic fracture risk. Both frequentist and Bayesian approaches were employed for the meta-analysis. We found that high tertiles of hs-CRP were significantly associated with increased fracture risk. INTRODUCTION: The association between the inflammatory marker CRP and osteoporotic fracture has remained uncertain. In this study, we conducted a systematic review and meta-analysis to examine the association of serum hs-CRP and fracture risk. METHODS: We performed a systematic literature search of relevant databases, including PubMed, Embase, and MEDLINE publications from January 1950 through April 2020. Three reviewers independently performed the study selection, quality assessment, and data abstraction. Frequentist and Bayesian hierarchical random-effects models were used separately for the analysis. Statistical heterogeneity was assessed using Higgin's I2 and Cochran's Q statistic, and publication bias was examined using funnel plots and rank correlation tests. RESULTS: Fourteen cohort studies that reported t fracture outcomes were eligible for the systematic review. Only ten studies (n = 29,741) qualified for meta-analysis. In the frequentist approach, the RR for fracture in a comparison of the top tertile group to the bottom tertile group of hs-CRP was 1.54 (1.18, 2.01). The estimated risk of fracture remained significant in all sensitivity and subgroup analyses. Higgin's I2 (30.52%) and Cochran's Q statistic (p < 0.01) suggested there was moderate heterogeneity for the meta-analysis. In the Bayesian approach, the pooled RR was 1.60 (95% CI (1.07-2.49)), and the probabilities that the high level of hs-CRP increased fracture risk by more than 0%, 10%, and 20% were 99%, 98%, and 93%, respectively. CONCLUSION: A high level of hs-CRP is associated with a significantly increased risk of osteoporotic fracture.

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.

How vague is vague? How informative is informative? Reference analysis for Bayesian meta-analysis.

Meta-analysis provides important insights for evidence-based medicine by synthesizing evidence from multiple studies which address the same research question. Within the Bayesian framework, meta-analysis is frequently expressed by a Bayesian normal-normal hierarchical model (NNHM). Recently, several publications have discussed the choice of the prior distribution for the between-study heterogeneity in the Bayesian NNHM and used several "vague" priors. However, no approach exists to quantify the informativeness of such priors, and thus, we develop a principled reference analysis framework for the Bayesian NNHM acting at the posterior level. The posterior reference analysis (post-RA) is based on two posterior benchmarks: one induced by the improper reference prior, which is minimally informative for the data, and the other induced by a highly anticonservative proper prior. This approach applies the Hellinger distance to quantify the informativeness of a heterogeneity prior of interest by comparing the corresponding marginal posteriors with both posterior benchmarks. The post-RA is implemented in the freely accessible R package ra4bayesmeta and is applied to two medical case studies. Our findings show that anticonservative heterogeneity priors produce platykurtic posteriors compared with the reference posterior, and they produce shorter 95% credible intervals (CrI) and optimistic inference compared with the reference prior. Conservative heterogeneity priors produce leptokurtic posteriors, longer 95% CrI and cautious inference. The novel post-RA framework could support numerous Bayesian meta-analyses in many research fields, as it determines how informative a heterogeneity prior is for the actual data as compared with the minimally informative reference prior.

Exploring Risks of Human Challenge Trials For COVID-19.

Human challenge trials (HCTs) are a potential method to accelerate development of vaccines and therapeutics. However, HCTs for COVID-19 pose ethical and practical challenges, in part due to the unclear and developing risks. In this article , we introduce an interactive model for exploring some risks of a severe acute respiratory syndrome coronavirus-2 (SARS-COV-2) dosing study, a prerequisite for any COVID-19 challenge trials. The risk estimates we use are based on a Bayesian evidence synthesis model which can incorporate new data on infection fatality risks (IFRs) to patients, and infer rates of hospitalization. The model estimates individual risk, which we then extrapolate to overall mortality and hospitalization risk in a dosing study. We provide a web tool to explore risk under different study designs. Based on the Bayesian model, IFR for someone between 20 and 30 years of age is 15.1 in 100,000, with a 95% uncertainty interval from 11.8 to 19.2, while risk of hospitalization is 130 per 100,000 (100-160). However, risk will be reduced in an HCT via screening for comorbidities, selecting lower-risk population, and providing treatment. Accounting for this with stronger assumptions, we project the fatality risk to be as low as 2.5 per 100,000 (1.6-3.9) and the hospitalization risk to be 22.0 per 100,000 (14.0-33.7). We therefore find a 50-person dosing trial has a 99.74% (99.8-99.9%) chance of no fatalities, and a 98.9% (98.3-99.3%) probability of no cases requiring hospitalization.

Sensitivity and identification quantification by a relative latent model complexity perturbation in Bayesian meta-analysis.

In recent years, Bayesian meta-analysis expressed by a normal-normal hierarchical model (NNHM) has been widely used for combining evidence from multiple studies. Data provided for the NNHM are frequently based on a small number of studies and on uncertain within-study standard deviation values. Despite the widespread use of Bayesian NNHM, it has always been unclear to what extent the posterior inference is impacted by the heterogeneity prior (sensitivity S ) and by the uncertainty in the within-study standard deviation values (identification I ). Thus, to answer this question, we developed a unified method to simultaneously quantify both sensitivity and identification ( S - I ) for all model parameters in a Bayesian NNHM, based on derivatives of the Bhattacharyya coefficient with respect to relative latent model complexity (RLMC) perturbations. Three case studies exemplify the applicability of the method proposed: historical data for a conventional therapy, data from which one large study is first included and then excluded, and two subgroup meta-analyses specified by their randomization status. We analyzed six scenarios, crossing three RLMC targets with two heterogeneity priors (half-normal, half-Cauchy). The results show that S - I explicitly reveals which parameters are affected by the heterogeneity prior and by the uncertainty in the within-study standard deviation values. In addition, we compare the impact of both heterogeneity priors and quantify how S - I values are affected by omitting one large study and by the randomization status. Finally, the range of applicability of S - I is extended to Bayesian NtHM. A dedicated R package facilitates automatic S - I quantification in applied Bayesian meta-analyses.

Comparative diagnostic accuracy of respective nuclear imaging for suspected fracture-related infection: a systematic review and Bayesian network meta-analysis.

BACKGROUND: The aim of this study was to compare the accuracy of available nuclear imaging modalities in the diagnosis of suspected fracture-related infection (FRI). METHODS: We conducted a comprehensive literature search of PubMed, EMBASE and the Cochrane Library to retrieve diagnostic accuracy studies in which FRI was investigated using different nuclear imaging modalities. The pooled sensitivity, specificity, likelihood ratios and diagnostic odds ratios were constructed using the bivariate meta-analysis framework, while the superior index was pooled using Bayesian network meta-analysis. RESULTS: 22 eligible studies (1,565 patients) were included in the quantitative analysis. A broad overlapping confidence interval (CI) of pooled sensitivity was observed among bone scintigraphy (0.94; 95% CI 0.85-0.98), 18F-FDG PET and PET/CT (0.91; 95% CI 0.85-0.94) and leukocyte scintigraphy (0.86; 95% CI 0.53-0.97). Bone scintigraphy (0.34; 95% CI 0.08-0.75) seemed to be less specific than all the other modalities, while leukocyte scintigraphy (0.96, 95% CI 0.92-0.98) was notably more specific than 18F-FDG PET and PET/CT (0.78; 95% CI 0.69-0.85). Based on the superiority index, 18F-FDG PET/CT (3.78; 95% CI 0.14-11.00), 18F-FDG PET (2.98; 95% CI 0.14-9.00) and leukocyte scintigraphy (1.51; 95% CI 0.11-7.00) all achieved high accuracy in detecting FRI. CONCLUSION: Bone scintigraphy is a highly sensitive nuclear imaging technique but lacks the specificity needed to unequivocally differentiate among various conditions suspected to be FRI. Leukocyte scintigraphy, 18F-FDG PET/CT and PET all present good satisfactory accuracy for the diagnosis of FRI, but their costs should be further reduced to promote their wide application.