A Complex Meta-Regression Model to Identify Effective Features of Interventions From Multi-Arm, Multi-Follow-Up Trials.

Network meta-analysis (NMA) combines evidence from multiple trials to compare the effectiveness of a set of interventions. In many areas of research, interventions are often complex, made up of multiple components or features. This makes it difficult to define a common set of interventions on which to perform the analysis. One approach to this problem is component network meta-analysis (CNMA) which uses a meta-regression framework to define each intervention as a subset of components whose individual effects combine additively. In this article, we are motivated by a systematic review of complex interventions to prevent obesity in children. Due to considerable heterogeneity across the trials, these interventions cannot be expressed as a subset of components but instead are coded against a framework of characteristic features. To analyse these data, we develop a bespoke CNMA-inspired model that allows us to identify the most important features of interventions. We define a meta-regression model with covariates on three levels: intervention, study, and follow-up time, as well as flexible interaction terms. By specifying different regression structures for trials with and without a control arm, we relax the assumption from previous CNMA models that a control arm is the absence of intervention components. Furthermore, we derive a correlation structure that accounts for trials with multiple intervention arms and multiple follow-up times. Although, our model was developed for the specifics of the obesity data set, it has wider applicability to any set of complex interventions that can be coded according to a set of shared features.

Meta-regression of genome-wide association studies to estimate age-varying genetic effects.

Fixed-effect meta-analysis has been used to summarize genetic effects on a phenotype across multiple Genome-Wide Association Studies (GWAS) assuming a common underlying genetic effect. Genetic effects may vary with age (or other characteristics), and not allowing for this in a GWAS might lead to bias. Meta-regression models between study heterogeneity and allows effect modification of the genetic effects to be explored. The aim of this study was to explore the use of meta-analysis and meta-regression for estimating age-varying genetic effects on phenotypes. With simulations we compared the performance of meta-regression to fixed-effect and random -effects meta-analyses in estimating (i) main genetic effects and (ii) age-varying genetic effects (SNP by age interactions) from multiple GWAS studies under a range of scenarios. We applied meta-regression on publicly available summary data to estimate the main and age-varying genetic effects of the FTO SNP rs9939609 on Body Mass Index (BMI). Fixed-effect and random-effects meta-analyses accurately estimated genetic effects when these did not change with age. Meta-regression accurately estimated both main genetic effects and age-varying genetic effects. When the number of studies or the age-diversity between studies was low, meta-regression had limited power. In the applied example, each additional minor allele (A) of rs9939609 was inversely associated with BMI at ages 0 to 3, and positively associated at ages 5.5 to 13. Our findings challenge the assumption that genetic effects are consistent across all ages and provide a method for exploring this. GWAS consortia should be encouraged to use meta-regression to explore age-varying genetic effects.

Statistical power in clinical trials of interventions for mood, anxiety, and psychotic disorders.

BACKGROUND: Previous research has suggested that statistical power is suboptimal in many biomedical disciplines, but it is unclear whether power is better in trials for particular interventions, disorders, or outcome types. We therefore performed a detailed examination of power in trials of psychotherapy, pharmacotherapy, and complementary and alternative medicine (CAM) for mood, anxiety, and psychotic disorders. METHODS: We extracted data from the Cochrane Database of Systematic Reviews (Mental Health). We focused on continuous efficacy outcomes and estimated power to detect predetermined effect sizes (standardized mean difference [SMD] = 0.20-0.80, primary SMD = 0.40) and meta-analytic effect sizes (ESMA). We performed meta-regression to estimate the influence of including underpowered studies in meta-analyses. RESULTS: We included 256 reviews with 10 686 meta-analyses and 47 384 studies. Statistical power for continuous efficacy outcomes was very low across intervention and disorder types (overall median [IQR] power for SMD = 0.40: 0.32 [0.19-0.54]; for ESMA: 0.23 [0.09-0.58]), only reaching conventionally acceptable levels (80%) for SMD = 0.80. Median power to detect the ESMA was higher in treatment-as-usual (TAU)/waitlist-controlled (0.49-0.63) or placebo-controlled (0.12-0.38) trials than in trials comparing active treatments (0.07-0.13). Adequately-powered studies produced smaller effect sizes than underpowered studies (B = -0.06, p ⩽ 0.001). CONCLUSIONS: Power to detect both predetermined and meta-analytic effect sizes in psychiatric trials was low across all interventions and disorders examined. Consistent with the presence of reporting bias, underpowered studies produced larger effect sizes than adequately-powered studies. These results emphasize the need to increase sample sizes and to reduce reporting bias against studies reporting null results to improve the reliability of the published literature.

A new approach to evaluating loop inconsistency in network meta-analysis.

In network meta-analysis, studies evaluating multiple treatment comparisons are modeled simultaneously, and estimation is informed by a combination of direct and indirect evidence. Network meta-analysis relies on an assumption of consistency, meaning that direct and indirect evidence should agree for each treatment comparison. Here we propose new local and global tests for inconsistency and demonstrate their application to three example networks. Because inconsistency is a property of a loop of treatments in the network meta-analysis, we locate the local test in a loop. We define a model with one inconsistency parameter that can be interpreted as loop inconsistency. The model builds on the existing ideas of node-splitting and side-splitting in network meta-analysis. To provide a global test for inconsistency, we extend the model across multiple independent loops with one degree of freedom per loop. We develop a new algorithm for identifying independent loops within a network meta-analysis. Our proposed models handle treatments symmetrically, locate inconsistency in loops rather than in nodes or treatment comparisons, and are invariant to choice of reference treatment, making the results less dependent on model parameterization. For testing global inconsistency in network meta-analysis, our global model uses fewer degrees of freedom than the existing design-by-treatment interaction approach and has the potential to increase power. To illustrate our methods, we fit the models to three network meta-analyses varying in size and complexity. Local and global tests for inconsistency are performed and we demonstrate that the global model is invariant to choice of independent loops.

Diagnostic accuracy of 1p/19q codeletion tests in oligodendroglioma: A comprehensive meta-analysis based on a Cochrane systematic review.

Codeletion of chromosomal arms 1p and 19q, in conjunction with a mutation in the isocitrate dehydrogenase 1 or 2 gene, is the molecular diagnostic criterion for oligodendroglioma, IDH mutant and 1p/19q codeleted. 1p/19q codeletion is a diagnostic marker and allows prognostication and prediction of the best drug response within IDH-mutant tumours. We performed a Cochrane review and simple economic analysis to establish the most sensitive, specific and cost-effective techniques for determining 1p/19q codeletion status. Fluorescent in situ hybridisation (FISH) and polymerase chain reaction (PCR)-based loss of heterozygosity (LOH) test methods were considered as reference standard. Most techniques (FISH, chromogenic in situ hybridisation [CISH], PCR, real-time PCR, multiplex ligation-dependent probe amplification [MLPA], single nucleotide polymorphism [SNP] array, comparative genomic hybridisation [CGH], array CGH, next-generation sequencing [NGS], mass spectrometry and NanoString) showed good sensitivity (few false negatives) for detection of 1p/19q codeletions in glioma, irrespective of whether FISH or PCR-based LOH was used as the reference standard. Both NGS and SNP array had a high specificity (fewer false positives) for 1p/19q codeletion when considered against FISH as the reference standard. Our findings suggest that G banding is not a suitable test for 1p/19q analysis. Within these limits, considering cost per diagnosis and using FISH as a reference, MLPA was marginally more cost-effective than other tests, although these economic analyses were limited by the range of available parameters, time horizon and data from multiple healthcare organisations.

The impact of the COVID-19 pandemic on presentations to health services following self-harm: systematic review.

BACKGROUND: Evidence on the impact of the pandemic on healthcare presentations for self-harm has accumulated rapidly. However, existing reviews do not include studies published beyond 2020. AIMS: To systematically review evidence on presentations to health services following self-harm during the COVID-19 pandemic. METHOD: A comprehensive search of databases (WHO COVID-19 database; Medline; medRxiv; Scopus; PsyRxiv; SocArXiv; bioRxiv; COVID-19 Open Research Dataset, PubMed) was conducted. Studies published from 1 January 2020 to 7 September 2021 were included. Study quality was assessed with a critical appraisal tool. RESULTS: Fifty-one studies were included: 57% (29/51) were rated as 'low' quality, 31% (16/51) as 'moderate' and 12% (6/51) as 'high-moderate'. Most evidence (84%, 43/51) was from high-income countries. A total of 47% (24/51) of studies reported reductions in presentation frequency, including all six rated as high-moderate quality, which reported reductions of 17-56%. Settings treating higher lethality self-harm were overrepresented among studies reporting increased demand. Two of the three higher-quality studies including study observation months from 2021 reported reductions in self-harm presentations. Evidence from 2021 suggests increased numbers of presentations among adolescents, particularly girls. CONCLUSIONS: Sustained reductions in numbers of self-harm presentations were seen into the first half of 2021, although this evidence is based on a relatively small number of higher-quality studies. Evidence from low- and middle-income countries is lacking. Increased numbers of presentations among adolescents, particularly girls, into 2021 is concerning. Findings may reflect changes in thresholds for help-seeking, use of alternative sources of support and variable effects of the pandemic across groups.

SARS-CoV-2 Infection and the Risk of Suicidal and Self-Harm Thoughts and Behaviour: A Systematic Review.

OBJECTIVE: The COVID-19 pandemic has had a complex impact on risks of suicide and non-fatal self-harm worldwide with some evidence of increased risk in specific populations including women, young people, and people from ethnic minority backgrounds. This review aims to systematically address whether SARS-CoV-2 infection and/or COVID-19 disease confer elevated risk directly. METHOD: As part of a larger Living Systematic Review examining self-harm and suicide during the pandemic, automated daily searches using a broad list of keywords were performed on a comprehensive set of databases with data from relevant articles published between January 1, 2020 and July 18, 2021. Eligibility criteria for our present review included studies investigating suicide and/or self-harm in people infected with SARS-CoV-2 with or without manifestations of COVID-19 disease with a comparator group who did not have infection or disease. Suicidal and self-harm thoughts and behaviour (STBs) were outcomes of interest. Studies were excluded if they reported data for people who only had potential infection/disease without a confirmed exposure, clinical/molecular diagnosis or self-report of a positive SARS-CoV-2 test result. Studies of news reports, treatment studies, and ecological studies examining rates of both SARS-CoV-2 infections and suicide/self-harm rates across a region were also excluded. RESULTS: We identified 12 studies examining STBs in nine distinct samples of people with SARS-CoV-2. These studies, which investigated STBs in the general population and in subpopulations, including healthcare workers, generally found positive associations between SARS-CoV-2 infection and/or COVID-19 disease and subsequent suicidal/self-harm thoughts and suicidal/self-harm behaviour. CONCLUSIONS: This review identified some evidence that infection with SARS-CoV-2 and/or COVID-19 disease may be associated with increased risks for suicidal and self-harm thoughts and behaviours but a causal link cannot be inferred. Further research with longer follow-up periods is required to confirm these findings and to establish whether these associations are causal.

ROB-MEN: a tool to assess risk of bias due to missing evidence in network meta-analysis.

BACKGROUND: Selective outcome reporting and publication bias threaten the validity of systematic reviews and meta-analyses and can affect clinical decision-making. A rigorous method to evaluate the impact of this bias on the results of network meta-analyses of interventions is lacking. We present a tool to assess the Risk Of Bias due to Missing Evidence in Network meta-analysis (ROB-MEN). METHODS: ROB-MEN first evaluates the risk of bias due to missing evidence for each of the possible pairwise comparison that can be made between the interventions in the network. This step considers possible bias due to the presence of studies with unavailable results (within-study assessment of bias) and the potential for unpublished studies (across-study assessment of bias). The second step combines the judgements about the risk of bias due to missing evidence in pairwise comparisons with (i) the contribution of direct comparisons to the network meta-analysis estimates, (ii) possible small-study effects evaluated by network meta-regression, and (iii) any bias from unobserved comparisons. Then, a level of "low risk", "some concerns", or "high risk" for the bias due to missing evidence is assigned to each estimate, which is our tool's final output. RESULTS: We describe the methodology of ROB-MEN step-by-step using an illustrative example from a published NMA of non-diagnostic modalities for the detection of coronary artery disease in patients with low risk acute coronary syndrome. We also report a full application of the tool on a larger and more complex published network of 18 drugs from head-to-head studies for the acute treatment of adults with major depressive disorder. CONCLUSIONS: ROB-MEN is the first tool for evaluating the risk of bias due to missing evidence in network meta-analysis and applies to networks of all sizes and geometry. The use of ROB-MEN is facilitated by an R Shiny web application that produces the Pairwise Comparisons and ROB-MEN Table and is incorporated in the reporting bias domain of the CINeMA framework and software.

Detecting Heterogeneity of Intervention Effects Using Analysis and Meta-analysis of Differences in Variance Between Trial Arms.

BACKGROUND: Randomized controlled trials (RCTs) with continuous outcomes usually only examine mean differences in response between trial arms. If the intervention has heterogeneous effects, then outcome variances will also differ between arms. Power of an individual trial to assess heterogeneity is lower than the power to detect the same size of main effect. METHODS: We describe several methods for assessing differences in variance in trial arms and apply them to a single trial with individual patient data and to meta-analyses using summary data. Where individual data are available, we use regression-based methods to examine the effects of covariates on variation. We present an additional method to meta-analyze differences in variances with summary data. RESULTS: In the single trial, there was agreement between methods, and the difference in variance was largely due to differences in prevalence of depression at baseline. In two meta-analyses, most individual trials did not show strong evidence of a difference in variance between arms, with wide confidence intervals. However, both meta-analyses showed evidence of greater variance in the control arm, and in one example, this was perhaps because mean outcome in the control arm was higher. CONCLUSIONS: Using meta-analysis, we overcame low power of individual trials to examine differences in variance using meta-analysis. Evidence of differences in variance should be followed up to identify potential effect modifiers and explore other possible causes such as varying compliance.

Prevalence of evidence of inconsistency and its association with network structural characteristics in 201 published networks of interventions.

BACKGROUND: Network meta-analysis (NMA) has attracted growing interest in evidence-based medicine. Consistency between different sources of evidence is fundamental to the reliability of the NMA results. The purpose of the present study was to estimate the prevalence of evidence of inconsistency and describe its association with different NMA characteristics. METHODS: We updated our collection of NMAs with articles published up to July 2018. We included networks with randomised clinical trials, at least four treatment nodes, at least one closed loop, a dichotomous primary outcome, and available arm-level data. We assessed consistency using the design-by-treatment interaction (DBT) model and testing all the inconsistency parameters globally through the Wald-type chi-squared test statistic. We estimated the prevalence of evidence of inconsistency and its association with different network characteristics (e.g., number of studies, interventions, intervention comparisons, loops). We evaluated the influence of the network characteristics on the DBT p-value via a multivariable regression analysis and the estimated Pearson correlation coefficients. We also evaluated heterogeneity in NMA (consistency) and DBT (inconsistency) random-effects models. RESULTS: We included 201 published NMAs. The p-value of the design-by-treatment interaction (DBT) model was lower than 0.05 in 14% of the networks and lower than 0.10 in 20% of the networks. Networks including many studies and comparing few interventions were more likely to have small DBT p-values (less than 0.10), which is probably because they yielded more precise estimates and power to detect differences between designs was higher. In the presence of inconsistency (DBT p-value lower than 0.10), the consistency model displayed higher heterogeneity than the DBT model. CONCLUSIONS: Our findings show that inconsistency was more frequent than what would be expected by chance, suggesting that researchers should devote more resources to exploring how to mitigate inconsistency. The results of this study highlight the need to develop strategies to detect inconsistency (because of the relatively high prevalence of evidence of inconsistency in published networks), and particularly in cases where the existing tests have low power.

Prognostic value of test(s) for O6-methylguanine-DNA methyltransferase (MGMT) promoter methylation for predicting overall survival in people with glioblastoma treated with temozolomide.

BACKGROUND: Glioblastoma is an aggressive form of brain cancer. Approximately five in 100 people with glioblastoma survive for five years past diagnosis. Glioblastomas that have a particular modification to their DNA (called methylation) in a particular region (the O6-methylguanine-DNA methyltransferase (MGMT) promoter) respond better to treatment with chemotherapy using a drug called temozolomide. OBJECTIVES: To determine which method for assessing MGMT methylation status best predicts overall survival in people diagnosed with glioblastoma who are treated with temozolomide. SEARCH METHODS: We searched MEDLINE, Embase, BIOSIS, Web of Science Conference Proceedings Citation Index to December 2018, and examined reference lists. For economic evaluation studies, we additionally searched NHS Economic Evaluation Database (EED) up to December 2014. SELECTION CRITERIA: Eligible studies were longitudinal (cohort) studies of adults with diagnosed glioblastoma treated with temozolomide with/without radiotherapy/surgery. Studies had to have related MGMT status in tumour tissue (assessed by one or more method) with overall survival and presented results as hazard ratios or with sufficient information (e.g. Kaplan-Meier curves) for us to estimate hazard ratios. We focused mainly on studies comparing two or more methods, and listed brief details of articles that examined a single method of measuring MGMT promoter methylation. We also sought economic evaluations conducted alongside trials, modelling studies and cost analysis. DATA COLLECTION AND ANALYSIS: Two review authors independently undertook all steps of the identification and data extraction process for multiple-method studies. We assessed risk of bias and applicability using our own modified and extended version of the QUality In Prognosis Studies (QUIPS) tool. We compared different techniques, exact promoter regions (5'-cytosine-phosphate-guanine-3' (CpG) sites) and thresholds for interpretation within studies by examining hazard ratios. We performed meta-analyses for comparisons of the three most commonly examined methods (immunohistochemistry (IHC), methylation-specific polymerase chain reaction (MSP) and pyrosequencing (PSQ)), with ratios of hazard ratios (RHR), using an imputed value of the correlation between results based on the same individuals. MAIN RESULTS: We included 32 independent cohorts involving 3474 people that compared two or more methods. We found evidence that MSP (CpG sites 76 to 80 and 84 to 87) is more prognostic than IHC for MGMT protein at varying thresholds (RHR 1.31, 95% confidence interval (CI) 1.01 to 1.71). We also found evidence that PSQ is more prognostic than IHC for MGMT protein at various thresholds (RHR 1.36, 95% CI 1.01 to 1.84). The data suggest that PSQ (mainly at CpG sites 74 to 78, using various thresholds) is slightly more prognostic than MSP at sites 76 to 80 and 84 to 87 (RHR 1.14, 95% CI 0.87 to 1.48). Many variants of PSQ have been compared, although we did not see any strong and consistent messages from the results. Targeting multiple CpG sites is likely to be more prognostic than targeting just one. In addition, we identified and summarised 190 articles describing a single method for measuring MGMT promoter methylation status. AUTHORS' CONCLUSIONS: PSQ and MSP appear more prognostic for overall survival than IHC. Strong evidence is not available to draw conclusions with confidence about the best CpG sites or thresholds for quantitative methods. MSP has been studied mainly for CpG sites 76 to 80 and 84 to 87 and PSQ at CpG sites ranging from 72 to 95. A threshold of 9% for CpG sites 74 to 78 performed better than higher thresholds of 28% or 29% in two of three good-quality studies making such comparisons.

Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement.

Importance: Mendelian randomization (MR) studies use genetic variation associated with modifiable exposures to assess their possible causal relationship with outcomes and aim to reduce potential bias from confounding and reverse causation. Objective: To develop the STROBE-MR Statement as a stand-alone extension to the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) guideline for the reporting of MR studies. Design, Setting, and Participants: The development of the STROBE-MR Statement followed the Enhancing the Quality and Transparency of Health Research (EQUATOR) framework guidance and used the STROBE Statement as a starting point to draft a checklist tailored to MR studies. The project was initiated in 2018 by reviewing the literature on the reporting of instrumental variable and MR studies. A group of 17 experts, including MR methodologists, MR study design users, developers of previous reporting guidelines, and journal editors, participated in a workshop in May 2019 to define the scope of the Statement and draft the checklist. The draft checklist was published as a preprint in July 2019 and discussed on the preprint platform, in social media, and at the 4th Mendelian Randomization Conference. The checklist was then revised based on comments, further refined through 2020, and finalized in July 2021. Findings: The STROBE-MR checklist is organized into 6 sections (Title and Abstract, Introduction, Methods, Results, Discussion, and Other Information) and includes 20 main items and 30 subitems. It covers both 1-sample and 2-sample MR studies that assess 1 or multiple exposures and outcomes, and addresses MR studies that follow a genome-wide association study and are reported in the same article. The checklist asks authors to justify why MR is a helpful method to address the study question and state prespecified causal hypotheses. The measurement, quality, and selection of genetic variants must be described and attempts to assess validity of MR-specific assumptions should be well reported. An item on data sharing includes reporting when the data and statistical code required to replicate the analyses can be accessed. Conclusions and Relevance: STROBE-MR provides guidelines for reporting MR studies. Improved reporting of these studies could facilitate their evaluation by editors, peer reviewers, researchers, clinicians, and other readers, and enhance the interpretation of their results.

Association Between Administration of IL-6 Antagonists and Mortality Among Patients Hospitalized for COVID-19: A Meta-analysis.

Importance: Clinical trials assessing the efficacy of IL-6 antagonists in patients hospitalized for COVID-19 have variously reported benefit, no effect, and harm. Objective: To estimate the association between administration of IL-6 antagonists compared with usual care or placebo and 28-day all-cause mortality and other outcomes. Data Sources: Trials were identified through systematic searches of electronic databases between October 2020 and January 2021. Searches were not restricted by trial status or language. Additional trials were identified through contact with experts. Study Selection: Eligible trials randomly assigned patients hospitalized for COVID-19 to a group in whom IL-6 antagonists were administered and to a group in whom neither IL-6 antagonists nor any other immunomodulators except corticosteroids were administered. Among 72 potentially eligible trials, 27 (37.5%) met study selection criteria. Data Extraction and Synthesis: In this prospective meta-analysis, risk of bias was assessed using the Cochrane Risk of Bias Assessment Tool. Inconsistency among trial results was assessed using the I2 statistic. The primary analysis was an inverse variance-weighted fixed-effects meta-analysis of odds ratios (ORs) for 28-day all-cause mortality. Main Outcomes and Measures: The primary outcome measure was all-cause mortality at 28 days after randomization. There were 9 secondary outcomes including progression to invasive mechanical ventilation or death and risk of secondary infection by 28 days. Results: A total of 10 930 patients (median age, 61 years [range of medians, 52-68 years]; 3560 [33%] were women) participating in 27 trials were included. By 28 days, there were 1407 deaths among 6449 patients randomized to IL-6 antagonists and 1158 deaths among 4481 patients randomized to usual care or placebo (summary OR, 0.86 [95% CI, 0.79-0.95]; P = .003 based on a fixed-effects meta-analysis). This corresponds to an absolute mortality risk of 22% for IL-6 antagonists compared with an assumed mortality risk of 25% for usual care or placebo. The corresponding summary ORs were 0.83 (95% CI, 0.74-0.92; P < .001) for tocilizumab and 1.08 (95% CI, 0.86-1.36; P = .52) for sarilumab. The summary ORs for the association with mortality compared with usual care or placebo in those receiving corticosteroids were 0.77 (95% CI, 0.68-0.87) for tocilizumab and 0.92 (95% CI, 0.61-1.38) for sarilumab. The ORs for the association with progression to invasive mechanical ventilation or death, compared with usual care or placebo, were 0.77 (95% CI, 0.70-0.85) for all IL-6 antagonists, 0.74 (95% CI, 0.66-0.82) for tocilizumab, and 1.00 (95% CI, 0.74-1.34) for sarilumab. Secondary infections by 28 days occurred in 21.9% of patients treated with IL-6 antagonists vs 17.6% of patients treated with usual care or placebo (OR accounting for trial sample sizes, 0.99; 95% CI, 0.85-1.16). Conclusions and Relevance: In this prospective meta-analysis of clinical trials of patients hospitalized for COVID-19, administration of IL-6 antagonists, compared with usual care or placebo, was associated with lower 28-day all-cause mortality. Trial Registration: PROSPERO Identifier: CRD42021230155.

CINeMA: An approach for assessing confidence in the results of a network meta-analysis.

BACKGROUND: The evaluation of the credibility of results from a meta-analysis has become an important part of the evidence synthesis process. We present a methodological framework to evaluate confidence in the results from network meta-analyses, Confidence in Network Meta-Analysis (CINeMA), when multiple interventions are compared. METHODOLOGY: CINeMA considers 6 domains: (i) within-study bias, (ii) reporting bias, (iii) indirectness, (iv) imprecision, (v) heterogeneity, and (vi) incoherence. Key to judgments about within-study bias and indirectness is the percentage contribution matrix, which shows how much information each study contributes to the results from network meta-analysis. The contribution matrix can easily be computed using a freely available web application. In evaluating imprecision, heterogeneity, and incoherence, we consider the impact of these components of variability in forming clinical decisions. CONCLUSIONS: Via 3 examples, we show that CINeMA improves transparency and avoids the selective use of evidence when forming judgments, thus limiting subjectivity in the process. CINeMA is easy to apply even in large and complicated networks.

Methodological features of clinical pharmacokinetic-pharmacodynamic studies of antibacterials and antifungals: a systematic review.

BACKGROUND: Pharmacokinetic (PK)-pharmacodynamic (PD) indices relate measures of drug exposure to antibacterial effect. Clinical PK-PD studies aim to correlate PK-PD indices with outcomes in patients. Optimization of dosing based on pre-clinical studies means that PK-PD relationships are difficult to establish; therefore studies need to be designed and reported carefully to validate pre-clinical findings. OBJECTIVES: To describe the methodological features of clinical antibacterial and antifungal PK-PD studies that reported the relationship between PK-PD indices and clinical or microbiological responses. METHODS: Studies published between 1980 and 2015 were identified through systematic searches. Methodological features of eligible studies were extracted. RESULTS: We identified 85 publications containing 97 PK-PD analyses. Most studies were small, with fewer than 100 patients. Around a quarter were performed on patients with infections due to a single specific pathogen. In approximately one-third of studies, patients received concurrent antibiotics/antifungals and in some other studies patients received other treatments that may confound the PK-PD-outcome relationship. Most studies measured antimicrobial concentrations in blood/serum and only four measured free concentrations. Most performed some form of regression, time-to-event analysis or used the Hill/Emax equation to examine the association between PK-PD index and outcome. Target values of PK-PD indices that predict outcomes were investigated in 52% of studies. Target identification was most commonly done using recursive partitioning or logistic regression. CONCLUSIONS: Given the variability in conduct and reporting, we suggest that an agreed set of standards for the conduct and reporting of studies should be developed.

COVID-19 in older people: a rapid clinical review.

INTRODUCTION: the COVID-19 pandemic poses a high risk to older people. The aim of this article is to provide a rapid overview of the COVID-19 literature, with a specific focus on older adults. We frame our findings within an overview of the disease and have also evaluated the inclusion of older people within forthcoming clinical trials. METHODS: we searched PubMed and bioRxiv/medRxiv to identify English language papers describing the testing, treatment and prognosis of COVID-19. PubMed and bioRxiv/medRxiv searches took place on 20 and 24 March 2020, respectively. RESULTS: screening of over 1,100 peer-reviewed and pre-print papers yielded n = 22 on COVID-19 testing, n = 15 on treatment and n = 13 on prognosis. Viral polymerase chain reaction (PCR) and serology are the mainstays of testing, but a positive diagnosis may be increasingly supported by radiological findings. The current evidence for the effectiveness of antiviral, corticosteroid and immunotherapies is inconclusive, although trial data are largely based on younger people. In addition to age, male gender and comorbidities, specific laboratory and radiology findings are important prognostic factors. Evidence suggests that social distancing policies could have important negative consequences, particularly if in place for an extended period. CONCLUSION: given the established association between increasing age and poor prognosis in COVID-19, we anticipate that this rapid review of the current and emergent evidence might form a basis on which future work can be established. Exclusion of older people, particularly those with comorbidities, from clinical trials is well recognised and is potentially being perpetuated in the field of current COVID-19 research.

Association Between Administration of Systemic Corticosteroids and Mortality Among Critically Ill Patients With COVID-19: A Meta-analysis.

Importance: Effective therapies for patients with coronavirus disease 2019 (COVID-19) are needed, and clinical trial data have demonstrated that low-dose dexamethasone reduced mortality in hospitalized patients with COVID-19 who required respiratory support. Objective: To estimate the association between administration of corticosteroids compared with usual care or placebo and 28-day all-cause mortality. Design, Setting, and Participants: Prospective meta-analysis that pooled data from 7 randomized clinical trials that evaluated the efficacy of corticosteroids in 1703 critically ill patients with COVID-19. The trials were conducted in 12 countries from February 26, 2020, to June 9, 2020, and the date of final follow-up was July 6, 2020. Pooled data were aggregated from the individual trials, overall, and in predefined subgroups. Risk of bias was assessed using the Cochrane Risk of Bias Assessment Tool. Inconsistency among trial results was assessed using the I2 statistic. The primary analysis was an inverse variance-weighted fixed-effect meta-analysis of overall mortality, with the association between the intervention and mortality quantified using odds ratios (ORs). Random-effects meta-analyses also were conducted (with the Paule-Mandel estimate of heterogeneity and the Hartung-Knapp adjustment) and an inverse variance-weighted fixed-effect analysis using risk ratios. Exposures: Patients had been randomized to receive systemic dexamethasone, hydrocortisone, or methylprednisolone (678 patients) or to receive usual care or placebo (1025 patients). Main Outcomes and Measures: The primary outcome measure was all-cause mortality at 28 days after randomization. A secondary outcome was investigator-defined serious adverse events. Results: A total of 1703 patients (median age, 60 years [interquartile range, 52-68 years]; 488 [29%] women) were included in the analysis. Risk of bias was assessed as "low" for 6 of the 7 mortality results and as "some concerns" in 1 trial because of the randomization method. Five trials reported mortality at 28 days, 1 trial at 21 days, and 1 trial at 30 days. There were 222 deaths among the 678 patients randomized to corticosteroids and 425 deaths among the 1025 patients randomized to usual care or placebo (summary OR, 0.66 [95% CI, 0.53-0.82]; P < .001 based on a fixed-effect meta-analysis). There was little inconsistency between the trial results (I2 = 15.6%; P = .31 for heterogeneity) and the summary OR was 0.70 (95% CI, 0.48-1.01; P = .053) based on the random-effects meta-analysis. The fixed-effect summary OR for the association with mortality was 0.64 (95% CI, 0.50-0.82; P < .001) for dexamethasone compared with usual care or placebo (3 trials, 1282 patients, and 527 deaths), the OR was 0.69 (95% CI, 0.43-1.12; P = .13) for hydrocortisone (3 trials, 374 patients, and 94 deaths), and the OR was 0.91 (95% CI, 0.29-2.87; P = .87) for methylprednisolone (1 trial, 47 patients, and 26 deaths). Among the 6 trials that reported serious adverse events, 64 events occurred among 354 patients randomized to corticosteroids and 80 events occurred among 342 patients randomized to usual care or placebo. Conclusions and Relevance: In this prospective meta-analysis of clinical trials of critically ill patients with COVID-19, administration of systemic corticosteroids, compared with usual care or placebo, was associated with lower 28-day all-cause mortality.

Comparative efficacy and acceptability of 21 antidepressant drugs for the acute treatment of adults with major depressive disorder: a systematic review and network meta-analysis.

BACKGROUND: Major depressive disorder is one of the most common, burdensome, and costly psychiatric disorders worldwide in adults. Pharmacological and non-pharmacological treatments are available; however, because of inadequate resources, antidepressants are used more frequently than psychological interventions. Prescription of these agents should be informed by the best available evidence. Therefore, we aimed to update and expand our previous work to compare and rank antidepressants for the acute treatment of adults with unipolar major depressive disorder. METHODS: We did a systematic review and network meta-analysis. We searched Cochrane Central Register of Controlled Trials, CINAHL, Embase, LILACS database, MEDLINE, MEDLINE In-Process, PsycINFO, the websites of regulatory agencies, and international registers for published and unpublished, double-blind, randomised controlled trials from their inception to Jan 8, 2016. We included placebo-controlled and head-to-head trials of 21 antidepressants used for the acute treatment of adults (≥18 years old and of both sexes) with major depressive disorder diagnosed according to standard operationalised criteria. We excluded quasi-randomised trials and trials that were incomplete or included 20% or more of participants with bipolar disorder, psychotic depression, or treatment-resistant depression; or patients with a serious concomitant medical illness. We extracted data following a predefined hierarchy. In network meta-analysis, we used group-level data. We assessed the studies' risk of bias in accordance to the Cochrane Handbook for Systematic Reviews of Interventions, and certainty of evidence using the Grading of Recommendations Assessment, Development and Evaluation framework. Primary outcomes were efficacy (response rate) and acceptability (treatment discontinuations due to any cause). We estimated summary odds ratios (ORs) using pairwise and network meta-analysis with random effects. This study is registered with PROSPERO, number CRD42012002291. FINDINGS: We identified 28 552 citations and of these included 522 trials comprising 116 477 participants. In terms of efficacy, all antidepressants were more effective than placebo, with ORs ranging between 2·13 (95% credible interval [CrI] 1·89-2·41) for amitriptyline and 1·37 (1·16-1·63) for reboxetine. For acceptability, only agomelatine (OR 0·84, 95% CrI 0·72-0·97) and fluoxetine (0·88, 0·80-0·96) were associated with fewer dropouts than placebo, whereas clomipramine was worse than placebo (1·30, 1·01-1·68). When all trials were considered, differences in ORs between antidepressants ranged from 1·15 to 1·55 for efficacy and from 0·64 to 0·83 for acceptability, with wide CrIs on most of the comparative analyses. In head-to-head studies, agomelatine, amitriptyline, escitalopram, mirtazapine, paroxetine, venlafaxine, and vortioxetine were more effective than other antidepressants (range of ORs 1·19-1·96), whereas fluoxetine, fluvoxamine, reboxetine, and trazodone were the least efficacious drugs (0·51-0·84). For acceptability, agomelatine, citalopram, escitalopram, fluoxetine, sertraline, and vortioxetine were more tolerable than other antidepressants (range of ORs 0·43-0·77), whereas amitriptyline, clomipramine, duloxetine, fluvoxamine, reboxetine, trazodone, and venlafaxine had the highest dropout rates (1·30-2·32). 46 (9%) of 522 trials were rated as high risk of bias, 380 (73%) trials as moderate, and 96 (18%) as low; and the certainty of evidence was moderate to very low. INTERPRETATION: All antidepressants were more efficacious than placebo in adults with major depressive disorder. Smaller differences between active drugs were found when placebo-controlled trials were included in the analysis, whereas there was more variability in efficacy and acceptability in head-to-head trials. These results should serve evidence-based practice and inform patients, physicians, guideline developers, and policy makers on the relative merits of the different antidepressants. FUNDING: National Institute for Health Research Oxford Health Biomedical Research Centre and the Japan Society for the Promotion of Science.

Network meta-analysis of rare events using the Mantel-Haenszel method.

The Mantel-Haenszel (MH) method has been used for decades to synthesize data obtained from studies that compare two interventions with respect to a binary outcome. It has been shown to perform better than the inverse-variance method or Peto's odds ratio when data is sparse. Network meta-analysis (NMA) is increasingly used to compare the safety of medical interventions, synthesizing, eg, data on mortality or serious adverse events. In this setting, sparse data occur often and yet there is to-date, no extension of the MH method for the case of NMA. In this paper, we fill this gap by presenting a MH-NMA method for odds ratios. Similarly to the pairwise MH method, we assume common treatment effects. We implement our approach in R, and we provide freely available easy-to-use routines. We illustrate our approach using data from two previously published networks. We compare our results to those obtained from three other approaches to NMA, namely, NMA with noncentral hypergeometric likelihood, an inverse-variance NMA, and a Bayesian NMA with a binomial likelihood. We also perform simulations to assess the performance of our method and compare it with alternative methods. We conclude that our MH-NMA method offers a reliable approach to the NMA of binary outcomes, especially in the case or sparse data, and when the assumption of methodological and clinical homogeneity is justifiable.

Altering the availability or proximity of food, alcohol, and tobacco products to change their selection and consumption.

BACKGROUND: Overconsumption of food, alcohol, and tobacco products increases the risk of non-communicable diseases. Interventions to change characteristics of physical micro-environments where people may select or consume these products - including shops, restaurants, workplaces, and schools - are of considerable public health policy and research interest. This review addresses two types of intervention within such environments: altering the availability (the range and/or amount of options) of these products, or their proximity (the distance at which they are positioned) to potential consumers. OBJECTIVES: 1. To assess the impact on selection and consumption of altering the availability or proximity of (a) food (including non-alcoholic beverages), (b) alcohol, and (c) tobacco products.2. To assess the extent to which the impact of these interventions is modified by characteristics of: i. studies, ii. interventions, and iii. SEARCH METHODS: We searched CENTRAL, MEDLINE, Embase, PsycINFO, and seven other published or grey literature databases, as well as trial registries and key websites, up to 23 July 2018, followed by citation searches. SELECTION CRITERIA: We included randomised controlled trials with between-participants (parallel group) or within-participants (cross-over) designs. Eligible studies compared effects of exposure to at least two different levels of availability of a product or its proximity, and included a measure of selection or consumption of the manipulated product. DATA COLLECTION AND ANALYSIS: We used a novel semi-automated screening workflow and applied standard Cochrane methods to select eligible studies, collect data, and assess risk of bias. In separate analyses for availability interventions and proximity interventions, we combined results using random-effects meta-analysis and meta-regression models to estimate summary effect sizes (as standardised mean differences (SMDs)) and to investigate associations between summary effect sizes and selected study, intervention, or participant characteristics. We rated the certainty of evidence for each outcome using GRADE. MAIN RESULTS: We included 24 studies, with the majority (20/24) giving concerns about risk of bias. All of the included studies investigated food products; none investigated alcohol or tobacco. The majority were conducted in laboratory settings (14/24), with adult participants (17/24), and used between-participants designs (19/24). All studies were conducted in high-income countries, predominantly in the USA (14/24).Six studies investigated availability interventions, of which two changed the absolute number of different options available, and four altered the relative proportion of less-healthy (to healthier) options. Most studies (4/6) manipulated snack foods or drinks. For selection outcomes, meta-analysis of three comparisons from three studies (n = 154) found that exposure to fewer options resulted in a large reduction in selection of the targeted food(s): SMD -1.13 (95% confidence interval (CI) -1.90 to -0.37) (low certainty evidence). For consumption outcomes, meta-analysis of three comparisons from two studies (n = 150) found that exposure to fewer options resulted in a moderate reduction in consumption of those foods, but with considerable uncertainty: SMD -0.55 (95% CI -1.27 to 0.18) (low certainty evidence).Eighteen studies investigated proximity interventions. Most (14/18) changed the distance at which a snack food or drink was placed from the participants, whilst four studies changed the order of meal components encountered along a line. For selection outcomes, only one study with one comparison (n = 41) was identified, which found that food placed farther away resulted in a moderate reduction in its selection: SMD -0.65 (95% CI -1.29 to -0.01) (very low certainty evidence). For consumption outcomes, meta-analysis of 15 comparisons from 12 studies (n = 1098) found that exposure to food placed farther away resulted in a moderate reduction in its consumption: SMD -0.60 (95% CI -0.84 to -0.36) (low certainty evidence). Meta-regression analyses indicated that this effect was greater: the farther away the product was placed; when only the targeted product(s) was available; when participants were of low deprivation status; and when the study was at high risk of bias. AUTHORS' CONCLUSIONS: The current evidence suggests that changing the number of available food options or altering the positioning of foods could contribute to meaningful changes in behaviour, justifying policy actions to promote such changes within food environments. However, the certainty of this evidence as assessed by GRADE is low or very low. To enable more certain and generalisable conclusions about these potentially important effects, further research is warranted in real-world settings, intervening across a wider range of foods - as well as alcohol and tobacco products - and over sustained time periods.