Publications by authors named "Julian P T Higgins"

201 Publications

Could Reducing Body Fatness Reduce the Risk of Aggressive Prostate Cancer via the Insulin Signalling Pathway? A Systematic Review of the Mechanistic Pathway.

Metabolites 2021 Oct 23;11(11). Epub 2021 Oct 23.

Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2BN, UK.

Excess body weight is thought to increase the risk of aggressive prostate cancer (PCa), although the biological mechanism is currently unclear. Body fatness is positively associated with a diminished cellular response to insulin and biomarkers of insulin signalling have been positively associated with PCa risk. We carried out a two-pronged systematic review of (a) the effect of reducing body fatness on insulin biomarker levels and (b) the effect of insulin biomarkers on PCa risk, to determine whether a reduction in body fatness could reduce PCa risk via effects on the insulin signalling pathway. We identified seven eligible randomised controlled trials of interventions designed to reduce body fatness which measured insulin biomarkers as an outcome, and six eligible prospective observational studies of insulin biomarkers and PCa risk. We found some evidence that a reduction in body fatness improved insulin sensitivity although our confidence in this evidence was low based on GRADE (Grading of Recommendations, Assessment, Development and Evaluations). We were unable to reach any conclusions on the effect of insulin sensitivity on PCa risk from the few studies included in our systematic review. A reduction in body fatness may reduce PCa risk via insulin signalling, but more high-quality evidence is needed before any conclusions can be reached regarding PCa.
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http://dx.doi.org/10.3390/metabo11110726DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8625823PMC
October 2021

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

BMC Med 2021 Nov 23;19(1):304. Epub 2021 Nov 23.

Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.

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.
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http://dx.doi.org/10.1186/s12916-021-02166-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8609747PMC
November 2021

Use of external evidence for design and Bayesian analysis of clinical trials: a qualitative study of trialists' views.

Trials 2021 Nov 8;22(1):789. Epub 2021 Nov 8.

Department of Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.

Background: Evidence from previous studies is often used relatively informally in the design of clinical trials: for example, a systematic review to indicate whether a gap in the current evidence base justifies a new trial. External evidence can be used more formally in both trial design and analysis, by explicitly incorporating a synthesis of it in a Bayesian framework. However, it is unclear how common this is in practice or the extent to which it is considered controversial. In this qualitative study, we explored attitudes towards, and experiences of, trialists in incorporating synthesised external evidence through the Bayesian design or analysis of a trial.

Methods: Semi-structured interviews were conducted with 16 trialists: 13 statisticians and three clinicians. Participants were recruited across several universities and trials units in the United Kingdom using snowball and purposeful sampling. Data were analysed using thematic analysis and techniques of constant comparison.

Results: Trialists used existing evidence in many ways in trial design, for example, to justify a gap in the evidence base and inform parameters in sample size calculations. However, no one in our sample reported using such evidence in a Bayesian framework. Participants tended to equate Bayesian analysis with the incorporation of prior information on the intervention effect and were less aware of the potential to incorporate data on other parameters. When introduced to the concepts, many trialists felt they could be making more use of existing data to inform the design and analysis of a trial in particular scenarios. For example, some felt existing data could be used more formally to inform background adverse event rates, rather than relying on clinical opinion as to whether there are potential safety concerns. However, several barriers to implementing these methods in practice were identified, including concerns about the relevance of external data, acceptability of Bayesian methods, lack of confidence in Bayesian methods and software, and practical issues, such as difficulties accessing relevant data.

Conclusions: Despite trialists recognising that more formal use of external evidence could be advantageous over current approaches in some areas and useful as sensitivity analyses, there are still barriers to such use in practice.
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http://dx.doi.org/10.1186/s13063-021-05759-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8577005PMC
November 2021

Prevalence of BRAFV600 in glioma and use of BRAF Inhibitors in patients with BRAFV600 mutation-positive glioma: systematic review.

Neuro Oncol 2021 Oct 28. Epub 2021 Oct 28.

MRC Integrative Epidemiology Unit (IEU), Bristol Medical School, University of Bristol, Bristol, UK.

Background: Detailed prevalence estimates of BRAFV600 mutations and BRAF inhibitor (BRAFi) treatment responses in V600-mutant glioma will inform trial development.

Methods: Our systematic review analysed overall prevalence of BRAFV600 mutations in glioma and BRAFi treatment response.

Results: Based on 13,682 patients in 182 publications, the prevalence of BRAFV600 in epithelioid glioblastoma (eGBM) was 69% [95% CI: 45-89%]; pleomorphic xanthoastrocytoma (PXA): 56% [48-64%] anaplastic pleomorphic xanthoastrocytoma (aPXA): 38% [23-54%], ganglioglioma (GG): 40% [33-46%], and anaplastic ganglioglioma (aGG): 46% [18-76%]. Prevalence in astroblastoma was 24% [8-43%], desmoplastic infantile astrocytoma (DIA): 16% [0-57%], subependymal giant cell astrocytoma (SEGA): 8% [0-37%], dysembryoplastic neuroepithelial tumour (DNET): 3% [0-11%], diffuse astrocytoma (DA): 3% [0-9%], and pilocytic astrocytoma (PA): 3% [2-5%].We reviewed 394 V600-mutant gliomas treated with BRAFi from 130 publications. 129 paediatric low-grade gliomas showed 4 (3.1%) complete response (CR); 53 (41.1%) partial response (PR); 64 (49.6%) stable disease (SD) and 8 (6.2%) progressive disease (PD). 25 paediatric high-grade gliomas showed CR; PR; SD; PD in 4 (16.0%); 10 (40.0%), 4 (16.0%); and 7 (28.0%) respectively. 39 adult low-grade gliomas showed CR; PR; SD; PD of 4 (10.3%); 17 (43.6%); 16 (41.0%) and 2 (5.1%) respectively. 97 adult high-grade gliomas showed CR; PR; SD; PD of 6 (6.2%); 31 (32.0%); 27 (27.8%); and 33 (34.0%) respectively.

Conclusions: BRAFV600 prevalence is highest in eGBM, PXA, aPXA, GG, aGG and lower in astroblastoma, DIA, SEGA, DNET, DA and PA. Our data provides the rationale for adjuvant clinical trials of BRAFi in V600-mutant glioma.
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http://dx.doi.org/10.1093/neuonc/noab247DOI Listing
October 2021

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

JAMA 2021 10;326(16):1614-1621

Departments of Medicine, Human Genetics, Epidemiology, & Biostatistics, Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Quebec, Canada.

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.
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http://dx.doi.org/10.1001/jama.2021.18236DOI Listing
October 2021

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

BMC Med Res Methodol 2021 10 25;21(1):224. Epub 2021 Oct 25.

Institute of Social and Preventive Medicine (ISPM), University of Bern, Mittelstrasse 43, CH-3012, Bern, Switzerland.

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.
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http://dx.doi.org/10.1186/s12874-021-01401-yDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8543923PMC
October 2021

MGMT promoter methylation testing to predict overall survival in people with glioblastoma treated with temozolomide: a comprehensive meta-analysis based on a Cochrane Systematic Review.

Neuro Oncol 2021 09;23(9):1457-1469

Bristol Medical School, Brain Tumour Research Centre, Population Health Sciences, University of Bristol, Bristol, UK.

Background: The DNA repair protein O6-methylguanine-DNA methyltransferase (MGMT) causes resistance of tumor cells to alkylating agents. It is a predictive biomarker in high-grade gliomas treated with temozolomide, however, there is no consensus on which test method, methylation sites, and cutoff values to use.

Methods: We performed a Cochrane Review to examine studies using different techniques to measure MGMT and predict survival in glioblastoma patients treated with temozolomide. Eligible longitudinal studies included (i) adults with glioblastoma treated with temozolomide with or without radiotherapy, or surgery; (ii) where MGMT status was determined in tumor tissue, and assessed by 1 or more technique; and (iii) where overall survival was an outcome parameter, with sufficient information to estimate hazard ratios (HRs). Two or more methods were compared in 32 independent cohorts with 3474 patients.

Results: Methylation-specific PCR (MSP) and pyrosequencing (PSQ) techniques were more prognostic than immunohistochemistry for MGMT protein, and PSQ is a slightly better predictor than MSP.

Conclusions: We cannot draw strong conclusions about use of frozen tissue vs formalin-fixed paraffin-embedded in MSP and PSQ. Also, our meta-analysis does not provide strong evidence about the best CpG sites or threshold. MSP has been studied mainly for CpG sites 76-80 and 84-87 and PSQ at CpG sites ranging from 72 to 95. A cutoff threshold of 9% for CpG sites 74-78 performed better than higher thresholds of 28% or 29% in 2 of the 3 good-quality studies. About 190 studies were identified presenting HRs from survival analysis in patients in which MGMT methylation was measured by 1 technique only.
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http://dx.doi.org/10.1093/neuonc/noab105DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8408882PMC
September 2021

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

Epidemiology 2021 11;32(6):846-854

From the Medical Research Council Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, Bristol, United Kingdom.

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.
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http://dx.doi.org/10.1097/EDE.0000000000001401DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8478324PMC
November 2021

Data extraction methods for systematic review (semi)automation: A living systematic review.

F1000Res 2021 19;10:401. Epub 2021 May 19.

Bristol Medical School, University of Bristol, Bristol, BS8 2PS, UK.

The reliable and usable (semi)automation of data extraction can support the field of systematic review by reducing the workload required to gather information about the conduct and results of the included studies. This living systematic review examines published approaches for data extraction from reports of clinical studies. We systematically and continually search MEDLINE, Institute of Electrical and Electronics Engineers (IEEE), arXiv, and the databases. Full text screening and data extraction are conducted within an open-source living systematic review application created for the purpose of this review. This iteration of the living review includes publications up to a cut-off date of 22 April 2020. In total, 53 publications are included in this version of our review. Of these, 41 (77%) of the publications addressed extraction of data from abstracts, while 14 (26%) used full texts. A total of 48 (90%) publications developed and evaluated classifiers that used randomised controlled trials as the main target texts. Over 30 entities were extracted, with PICOs (population, intervention, comparator, outcome) being the most frequently extracted. A description of their datasets was provided by 49 publications (94%), but only seven (13%) made the data publicly available. Code was made available by 10 (19%) publications, and five (9%) implemented publicly available tools. This living systematic review presents an overview of (semi)automated data-extraction literature of interest to different types of systematic review. We identified a broad evidence base of publications describing data extraction for interventional reviews and a small number of publications extracting epidemiological or diagnostic accuracy data. The lack of publicly available gold-standard data for evaluation, and lack of application thereof, makes it difficult to draw conclusions on which is the best-performing system for each data extraction target. With this living review we aim to review the literature continually.
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http://dx.doi.org/10.12688/f1000research.51117.1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8361807PMC
September 2021

Examining how meta-analytic methods perform in the presence of bias: A simulation study.

Res Synth Methods 2021 Nov 5;12(6):816-830. Epub 2021 Aug 5.

Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.

Standard meta-analysis methods are vulnerable to bias from incomplete reporting of results (both publication and outcome reporting bias) and poor study quality. Several alternative methods have been proposed as being less vulnerable to such biases. To evaluate these claims independently we simulated study results under a broad range of conditions first with no bias, then introducing simulated publication bias, outcome reporting bias, and bias from poor study quality. We then implemented common and the proposed bias robust meta-analysis methods and compared the mean bias and mean squared error (MSE) for four estimates of effect and the coverage probability of seven confidence intervals. We found that no methods perform well in the presence of any substantial bias. A regression based extension to Egger's test gave an estimate of effect with lower mean bias than standard methods in the presence of publication bias or poor study quality, but had a substantially worse MSE except in very specific conditions. Coverage of all 95% confidence intervals was very poor with increasing numbers of studies in biased conditions, often falling below 50%. The Knapp-Hartung interval performed closest to nominal coverage with fewer than 10 studies in most conditions, and the Henmi-Copas interval generally performed best with more than 10 studies. There was no evidence that a multiplicative term for heterogeneity improved coverage. Multiple forms of bias remain problematic for all meta-analysis methods, with very poor performance under conceivable conditions.
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http://dx.doi.org/10.1002/jrsm.1516DOI Listing
November 2021

Examining how meta-analytic methods perform in the presence of bias: A simulation study.

Res Synth Methods 2021 Nov 5;12(6):816-830. Epub 2021 Aug 5.

Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.

Standard meta-analysis methods are vulnerable to bias from incomplete reporting of results (both publication and outcome reporting bias) and poor study quality. Several alternative methods have been proposed as being less vulnerable to such biases. To evaluate these claims independently we simulated study results under a broad range of conditions first with no bias, then introducing simulated publication bias, outcome reporting bias, and bias from poor study quality. We then implemented common and the proposed bias robust meta-analysis methods and compared the mean bias and mean squared error (MSE) for four estimates of effect and the coverage probability of seven confidence intervals. We found that no methods perform well in the presence of any substantial bias. A regression based extension to Egger's test gave an estimate of effect with lower mean bias than standard methods in the presence of publication bias or poor study quality, but had a substantially worse MSE except in very specific conditions. Coverage of all 95% confidence intervals was very poor with increasing numbers of studies in biased conditions, often falling below 50%. The Knapp-Hartung interval performed closest to nominal coverage with fewer than 10 studies in most conditions, and the Henmi-Copas interval generally performed best with more than 10 studies. There was no evidence that a multiplicative term for heterogeneity improved coverage. Multiple forms of bias remain problematic for all meta-analysis methods, with very poor performance under conceivable conditions.
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http://dx.doi.org/10.1002/jrsm.1516DOI Listing
November 2021

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

JAMA 2021 08;326(6):499-518

Medanta-The Medicity, Institute of Liver Transplantation and Regenerative Medicine, Gurugram, India.

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.
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http://dx.doi.org/10.1001/jama.2021.11330DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8261689PMC
August 2021

The REPRISE project: protocol for an evaluation of REProducibility and Replicability In Syntheses of Evidence.

Syst Rev 2021 04 16;10(1):112. Epub 2021 Apr 16.

School of Public Health and Preventive Medicine, Monash University, 553 St. Kilda Road, Melbourne, Victoria, 3004, Australia.

Background: Investigations of transparency, reproducibility and replicability in science have been directed largely at individual studies. It is just as critical to explore these issues in syntheses of studies, such as systematic reviews, given their influence on decision-making and future research. We aim to explore various aspects relating to the transparency, reproducibility and replicability of several components of systematic reviews with meta-analysis of the effects of health, social, behavioural and educational interventions.

Methods: The REPRISE (REProducibility and Replicability In Syntheses of Evidence) project consists of four studies. We will evaluate the completeness of reporting and sharing of review data, analytic code and other materials in a random sample of 300 systematic reviews of interventions published in 2020 (Study 1). We will survey authors of systematic reviews to explore their views on sharing review data, analytic code and other materials and their understanding of and opinions about replication of systematic reviews (Study 2). We will then evaluate the extent of variation in results when we (a) independently reproduce meta-analyses using the same computational steps and analytic code (if available) as used in the original review (Study 3), and (b) crowdsource teams of systematic reviewers to independently replicate a subset of methods (searches for studies, selection of studies for inclusion, collection of outcome data, and synthesis of results) in a sample of the original reviews; 30 reviews will be replicated by 1 team each and 2 reviews will be replicated by 15 teams (Study 4).

Discussion: The REPRISE project takes a systematic approach to determine how reliable systematic reviews of interventions are. We anticipate that results of the REPRISE project will inform strategies to improve the conduct and reporting of future systematic reviews.
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http://dx.doi.org/10.1186/s13643-021-01670-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8052676PMC
April 2021

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

Cochrane Database Syst Rev 2021 03 12;3:CD013316. Epub 2021 Mar 12.

Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.

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 O-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.
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http://dx.doi.org/10.1002/14651858.CD013316.pub2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8078495PMC
March 2021

Triangulating Evidence through the Inclusion of Genetically Informed Designs.

Cold Spring Harb Perspect Med 2021 08 2;11(8). Epub 2021 Aug 2.

MRC Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, United Kingdom.

Much research effort is invested in attempting to determine causal influences on disease onset and progression to inform prevention and treatment efforts. However, this is often dependent on observational data that are prone to well-known limitations, particularly residual confounding and reverse causality. Several statistical methods have been developed to support stronger causal inference. However, a complementary approach is to use design-based methods for causal inference, which acknowledge sources of bias and attempt to mitigate these through the design of the study rather than solely through statistical adjustment. Genetically informed methods provide a novel and potentially powerful extension to this approach, accounting by design for unobserved genetic and environmental confounding. No single approach will be absent from bias. Instead, we should seek and combine evidence from multiple methodologies that each bring different (and ideally uncorrelated) sources of bias. If the results of these different methodologies align-or triangulate-then we can be more confident in our causal inference. To be truly effective, this should ideally be done prospectively, with the sources of evidence specified in advance, to protect against one final source of bias-our own cognitions, expectations, and fondly held beliefs.
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http://dx.doi.org/10.1101/cshperspect.a040659DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8327826PMC
August 2021

Treatment interventions to maintain abstinence from alcohol in primary care: systematic review and network meta-analysis.

BMJ 2020 11 25;371:m3934. Epub 2020 Nov 25.

Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK

Objective: To determine the most effective interventions in recently detoxified, alcohol dependent patients for implementation in primary care.

Design: Systematic review and network meta-analysis.

Data Sources: Medline, Embase, PsycINFO, Cochrane CENTRAL, ClinicalTrials.gov, and the World Health Organization's International Clinical Trials Registry Platform.

Study Selection: Randomised controlled trials comparing two or more interventions that could be used in primary care. The population was patients with alcohol dependency diagnosed by standardised clinical tools and who became detoxified within four weeks.

Data Extraction: Outcomes of interest were continuous abstinence from alcohol (effectiveness) and all cause dropouts (as a proxy for acceptability) at least 12 weeks after start of intervention.

Results: 64 trials (43 interventions) were included. The median probability of abstinence across placebo arms was 25%. Compared with placebo, the only intervention associated with increased probability of abstinence and moderate certainty evidence was acamprosate (odds ratio 1.86, 95% confidence interval 1.49 to 2.33, corresponding to an absolute probability of 38%). Of the 62 included trials that reported all cause dropouts, interventions associated with a reduced number of dropouts compared with placebo (probability 50%) and moderate certainty of evidence were acamprosate (0.73, 0.62 to 0.86; 42%), naltrexone (0.70, 0.50 to 0.98; 41%), and acamprosate-naltrexone (0.30, 0.13 to 0.67; 17%). Acamprosate was the only intervention associated with moderate confidence in the evidence of effectiveness and acceptability up to 12 months. It is uncertain whether other interventions can help maintain abstinence and reduce dropouts because of low confidence in the evidence.

Conclusions: Evidence is lacking for benefit from interventions that could be implemented in primary care settings for alcohol abstinence, other than for acamprosate. More evidence from high quality randomised controlled trials is needed, as are strategies using combined interventions (combinations of drug interventions or drug and psychosocial interventions) to improve treatment of alcohol dependency in primary care.

Systematic Review Registration: PROSPERO CRD42016049779.
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http://dx.doi.org/10.1136/bmj.m3934DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7687021PMC
November 2020

Investigating and dealing with publication bias and other reporting biases in meta-analyses of health research: A review.

Res Synth Methods 2021 Mar 18;12(2):248-259. Epub 2020 Nov 18.

Institute for Social and Preventive Medicine, University of Bern, Bern, Switzerland.

A P value, or the magnitude or direction of results can influence decisions about whether, when, and how research findings are disseminated. Regardless of whether an entire study or a particular study result is unavailable because investigators considered the results to be unfavorable, bias in a meta-analysis may occur when available results differ systematically from missing results. In this article, we summarize the empirical evidence for various reporting biases that lead to study results being unavailable for inclusion in systematic reviews, with a focus on health research. These biases include publication bias and selective nonreporting bias. We describe processes that systematic reviewers can use to minimize the risk of bias due to missing results in meta-analyses of health research, such as comprehensive searches and prospective approaches to meta-analysis. We also outline methods that have been designed for assessing risk of bias due to missing results in meta-analyses of health research, including using tools to assess selective nonreporting of results, ascertaining qualitative signals that suggest not all studies were identified, and generating funnel plots to identify small-study effects, one cause of which is reporting bias. HIGHLIGHTS: Bias in a meta-analysis may occur when available results differ systematically from missing results. Several different tools, plots, and statistical methods have been designed for assessing risk of bias due to missing results in meta-analyses. These include comparison of prespecified analysis plans with completed reports to detect selective nonreporting of results, consideration of qualitative signals that suggest not all studies were identified, and the use of funnel plots to identify small-study effects, for which reporting bias is one of several causes. Information from approaches such as funnel plots and selection models is more difficult to interpret than from less subjective approaches such as detection of incompletely reported results in studies for which prespecified analysis plans were available.
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http://dx.doi.org/10.1002/jrsm.1468DOI Listing
March 2021

Association between opioid agonist therapy use and HIV testing uptake among people who have recently injected drugs: a systematic review and meta-analysis.

Addiction 2021 07 3;116(7):1664-1676. Epub 2021 Feb 3.

Health Protection Research Unit, Population Health Sciences, University of Bristol, Canynge Hall, 39 Whatley Road, Clifton, Bristol, BS8 2PS, UK.

Background And Aim: Globally, nearly one in five people who inject drugs (PWID) are living with HIV, and the rate of new HIV infections in PWID is increasing in some settings. Early diagnosis is crucial for effective HIV control. We reviewed the evidence on the association between opioid agonist therapy (OAT) and HIV testing uptake among PWID.

Methods: We conducted a systematic review searching MEDLINE, Scopus, Web of Science, Cochrane Central Register of Controlled Trials and PsycINFO for studies published from January 2000 to March 2019. Reference lists and conference proceedings were hand-searched. Observational and intervention studies were eligible for inclusion. Risk of bias was assessed using the Risk of Bias in Non-Randomised Studies of Interventions (ROBINS-I) tool. Meta-analyses were conducted using random-effects models.

Results: Of 13 373 records identified, 11 studies from Australia, Europe, Malaysia and the United States were included. All studies had at least a serious risk of bias, largely due to confounding and selection bias, making it difficult to draw causal conclusions from the evidence. Ten studies provided data on the association between current OAT use and recent HIV testing. Six showed a positive association, while four provided little evidence of an association: pooled odds ratio (OR) = 1.71, 95% confidence interval (CI) = 1.28-2.27. Looking at having ever been on OAT and having ever been HIV tested, seven studies showed a positive association and three showed either weak or no evidence of an association: pooled OR = 3.82, 95% CI = 2.96-4.95.

Conclusions: Opioid agonist therapy may increase uptake of HIV testing among people who inject drugs, providing further evidence that opioid agonist therapy improves the HIV treatment care cascade.
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http://dx.doi.org/10.1111/add.15316DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8248165PMC
July 2021

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

JAMA 2020 10;324(13):1330-1341

Australian and New Zealand Intensive Care Research Centre, School of Epidemiology and Preventive Medicine, Monash University, Melbourne, Australia.

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.
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http://dx.doi.org/10.1001/jama.2020.17023DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7489434PMC
October 2020

Corticosteroid therapy for critically ill patients with COVID-19: A structured summary of a study protocol for a prospective meta-analysis of randomized trials.

Trials 2020 Aug 24;21(1):734. Epub 2020 Aug 24.

Li Ka Shing Knowledge Institute, St. Michael's Hospital, University of Toronto, Toronto, Canada.

Objectives: Primary objective: To estimate the effect of corticosteroids compared with usual care or placebo on mortality up to 28 days after randomization. Secondary objectives: To examine whether the effect of corticosteroids compared with usual care or placebo on mortality up to 28 days after randomization varies between subgroups related to treatment characteristics, disease severity at the time of randomization, patient characteristics, or risk of bias. To examine the effect of corticosteroids compared with usual care or placebo on serious adverse events.

Study Design: Prospective meta-analysis of randomized controlled trials. Both placebo-controlled and open-label trials are eligible.

Participants: Hospitalised, critically ill patients with suspected or confirmed COVID-19.

Intervention And Comparator: Intervention groups will have received therapeutic doses of a steroid (dexamethasone, hydrocortisone or methylprednisolone) with IV or oral administration immediately after randomization. The comparator groups will have received standard of care or usual care or placebo.

Main Outcome: All-cause mortality up to 28 days after randomization.

Search Methods: Systematic searching of clinicaltrials.gov , EudraCT, the WHO ISRCTN registry, and the Chinese clinical trials registry. Additionally, research and WHO networks will be asked for relevant trials.

Risk Of Bias Assessments: These will be based on the Cochrane RoB 2 tool, and will use structured information provided by the trial investigators on a form designed for this prospective meta-analysis. We will use GRADE to assess the certainty of the evidence.

Statistical Analyses: Trial investigators will provide data on the numbers of participants who did and did not experience each outcome according to intervention group, overall and in specified subgroups. We will conduct fixed-effect (primary analysis) and random-effects (Paule-Mandel estimate of heterogeneity and Hartung-Knapp adjustment) meta-analyses. We will quantify inconsistency in effects between trials using I statistics. Evidence for subgroup effects will be quantified by ratios of odds ratios comparing effects in the subgroups, and corresponding interaction p-values. Comparisons between subgroups defined by trial characteristics will be made using random-effects meta-regression. Comparisons between subgroups defined by patient characteristics will be made by estimating trial-specific ratios of odds ratios comparing intervention effects between subgroups then combining these using random-effects meta-analysis. Steroid interventions will be classified as high or low dose according to whether the dose is greater or less than or equal to 400 mg hydrocortisone per day or equivalent. We will use network meta-analysis methods to make comparisons between the effects of high and low dose steroid interventions (because one trial randomized participants to both low and high dose steroid arms).

Prospero Registration Number: CRD42020197242 FULL PROTOCOL: The full protocol for this prospective meta-analysis is attached as an additional file, accessible from the Trials website (Additional file 1). To expedite dissemination of this material, the familiar formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol for the systematic review.
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http://dx.doi.org/10.1186/s13063-020-04641-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7443535PMC
August 2020

Data extraction methods for systematic review (semi)automation: A living review protocol.

F1000Res 2020 25;9:210. Epub 2020 Mar 25.

Bristol Medical School, University of Bristol, Bristol, BS8 2PS, UK.

Researchers in evidence-based medicine cannot keep up with the amounts of both old and newly published primary research articles. Support for the early stages of the systematic review process - searching and screening studies for eligibility - is necessary because it is currently impossible to search for relevant research with precision. Better automated data extraction may not only facilitate the stage of review traditionally labelled 'data extraction', but also change earlier phases of the review process by making it possible to identify relevant research. Exponential improvements in computational processing speed and data storage are fostering the development of data mining models and algorithms. This, in combination with quicker pathways to publication, led to a large landscape of tools and methods for data mining and extraction. To review published methods and tools for data extraction to (semi)automate the systematic reviewing process. We propose to conduct a living review. With this methodology we aim to do constant evidence surveillance, bi-monthly search updates, as well as review updates every 6 months if new evidence permits it. In a cross-sectional analysis we will extract methodological characteristics and assess the quality of reporting in our included papers. We aim to increase transparency in the reporting and assessment of automation technologies to the benefit of data scientists, systematic reviewers and funders of health research. This living review will help to reduce duplicate efforts by data scientists who develop data mining methods. It will also serve to inform systematic reviewers about possibilities to support their data extraction.
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http://dx.doi.org/10.12688/f1000research.22781.2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338918PMC
February 2021

Agreement was moderate between data-based and opinion-based assessments of biases affecting randomized trials within meta-analyses.

J Clin Epidemiol 2020 09 13;125:16-25. Epub 2020 May 13.

Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; NIHR Applied Research Collaboration (ARC) West, University Hospitals Bristol NHS Foundation Trust, Bristol, UK.

Background And Objective: Randomized trials included in meta-analyses are often affected by bias caused by methodological flaws or limitations, but the degree of bias is unknown. Two proposed methods adjust the trial results for bias using empirical evidence from published meta-epidemiological studies or expert opinion.

Methods: We investigated agreement between data-based and opinion-based approaches to assessing bias in each of four domains: sequence generation, allocation concealment, blinding, and incomplete outcome data. From each sampled meta-analysis, a pair of trials with the highest and lowest empirical model-based bias estimates was selected. Independent assessors were asked which trial within each pair was judged more biased on the basis of detailed trial design summaries.

Results: Assessors judged trials to be equally biased in 68% of pairs evaluated. When assessors judged one trial as more biased, the proportion of judgments agreeing with the model-based ranking was highest for allocation concealment (79%) and blinding (79%) and lower for sequence generation (59%) and incomplete outcome data (56%).

Conclusion: Most trial pairs found to be discrepant empirically were judged to be equally biased by assessors. We found moderate agreement between opinion and data-based evidence in pairs where assessors ranked one trial as more biased.
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http://dx.doi.org/10.1016/j.jclinepi.2020.05.009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7482431PMC
September 2020

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

Age Ageing 2020 07;49(4):501-515

Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK.

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.
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http://dx.doi.org/10.1093/ageing/afaa093DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7239238PMC
July 2020

Risk-of-bias VISualization (robvis): An R package and Shiny web app for visualizing risk-of-bias assessments.

Res Synth Methods 2021 Jan 6;12(1):55-61. Epub 2020 May 6.

MRC Integrative Epidemiology Unit at the University of Bristol, Bristol, UK.

Despite a major increase in the range and number of software offerings now available to help researchers produce evidence syntheses, there is currently no generic tool for producing figures to display and explore the risk-of-bias assessments that routinely take place as part of systematic review. However, tools such as the R programming environment and Shiny (an R package for building interactive web apps) have made it straightforward to produce new tools to help in producing evidence syntheses. We present a new tool, robvis (Risk-Of-Bias VISualization), available as an R package and web app, which facilitates rapid production of publication-quality risk-of-bias assessment figures. We present a timeline of the tool's development and its key functionality.
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http://dx.doi.org/10.1002/jrsm.1411DOI Listing
January 2021

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

PLoS Med 2020 04 3;17(4):e1003082. Epub 2020 Apr 3.

Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.

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.
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http://dx.doi.org/10.1371/journal.pmed.1003082DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122720PMC
April 2020

The median and the mode as robust meta-analysis estimators in the presence of small-study effects and outliers.

Res Synth Methods 2020 May 10;11(3):397-412. Epub 2020 Mar 10.

MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK.

Meta-analyses based on systematic literature reviews are commonly used to obtain a quantitative summary of the available evidence on a given topic. However, the reliability of any meta-analysis is constrained by that of its constituent studies. One major limitation is the possibility of small-study effects, when estimates from smaller and larger studies differ systematically. Small-study effects may result from reporting biases (ie, publication bias), from inadequacies of the included studies that are related to study size, or from reasons unrelated to bias. We propose two estimators based on the median and mode to increase the reliability of findings in a meta-analysis by mitigating the influence of small-study effects. By re-examining data from published meta-analyses and by conducting a simulation study, we show that these estimators offer robustness to a range of plausible bias mechanisms, without making explicit modelling assumptions. They are also robust to outlying studies without explicitly removing such studies from the analysis. When meta-analyses are suspected to be at risk of bias because of small-study effects, we recommend reporting the mean, median and modal pooled estimates.
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http://dx.doi.org/10.1002/jrsm.1402DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7359861PMC
May 2020

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

J Antimicrob Chemother 2020 06;75(6):1374-1389

Bristol Centre for Antimicrobial Research & Evaluation, Infection Sciences, Pathology Science Quarter, North Bristol NHS Trust, Southmead Hospital, Westbury-on-Trym, Bristol BS10 5NB, UK.

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.
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http://dx.doi.org/10.1093/jac/dkaa005DOI Listing
June 2020
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