scholarly journals Assessing Confidence in the Results of Network Meta-Analysis (Cinema)

2019 ◽  
Author(s):  
Adriani Nikolakopoulou ◽  
Julian PT Higgins ◽  
Theodore Papakonstantinou ◽  
Anna Chaimani ◽  
Cinzia Del Giovane ◽  
...  
Keyword(s):  
Web Application ◽  
Evidence Synthesis ◽  
Meta Analysis ◽  
Antidepressant Drugs ◽  
Synthesis Process ◽  
Clinical Decisions ◽  
Methodological Framework ◽  
Percentage Contribution ◽  
The Impact ◽  
Study Bias

AbstractEvaluation of the credibility of results from a meta-analysis has become an intrinsic part of the evidence synthesis process. We present a methodological framework to evaluate Confidence In the results from Network Meta-Analysis (CINeMA) when multiple interventions are compared. CINeMA considers six domains and we outline the methods used to form judgements about within-study bias, across-studies bias, indirectness, imprecision, heterogeneity and incoherence. Key to judgements 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 use of contribution matrix allows the semi-automation of the process, implemented in a freely available web application (cinema.ispm.ch). In evaluating imprecision, heterogeneity and inconsistency we consider the impact of these components of variability in forming clinical decisions. Via three examples, we show that CINeMA improves transparency and avoids the selective use of evidence when forming judgements, thus limiting subjectivity in the process. CINeMA is easy to apply even in large and complicated networks, like a network involving 18 different antidepressant drugs.

scholarly journals Sleep in youth with autism spectrum disorders: systematic review and meta-analysis of subjective and objective studies

2018 ◽  
Vol 21 (4) ◽  
pp. 146-154 ◽  
Author(s):  
Amparo Díaz-Román ◽  
Junhua Zhang ◽  
Richard Delorme ◽  
Anita Beggiato ◽  
Samuele Cortese
Keyword(s):  
Systematic Review ◽  
Autism Spectrum Disorders ◽  
Evidence Synthesis ◽  
Meta Analysis ◽  
Autism Spectrum ◽  
Newcastle Ottawa Scale ◽  
Spectrum Disorders ◽  
Sleep Alterations ◽  
The Impact ◽  
Ottawa Scale

BackgroundSleep problems are common and impairing in individuals with autism spectrum disorders (ASD). Evidence synthesis including both subjective (ie, measured with questionnaires) and objective (ie, quantified with neurophysiological tools) sleep alterations in youth with ASD is currently lacking.ObjectiveWe conducted a systematic review and meta-analysis of subjective and objective studies sleep studies in youth with ASD.MethodsWe searched the following electronic databases with no language, date or type of document restriction up to 23 May 2018: PubMed, PsycInfo, Embase+Embase Classic, Ovid Medline and Web of Knowledge. Random-effects models were used. Heterogeneity was assessed with Cochran’s Q and I2 statistics. Publication (small studies) bias was assessed with final plots and the Egger’s test. Study quality was evaluated with the Newcastle Ottawa Scale. Analyses were conducted using Review Manager and Comprehensive Meta-Analysis.FindingsFrom a pool of 3359 non-duplicate potentially relevant references, 47 datasets were included in the meta-analyses. Subjective and objective sleep outcome measures were extracted from 37 and 15 studies, respectively. Only five studies were based on comorbidity free, medication-naïve participants. Compared with typically developing controls, youth with ASD significantly differed in 10/14 subjective parameters and in 7/14 objective sleep parameters. The average quality score in the Newcastle-Ottawa Scale was 5.9/9.Discussion and clinical implicationsA number of subjective and, to a less extent, objective sleep alterations might characterise youth with ASD, but future studies should assess the impact of pharmacological treatment and psychiatric comorbidities.

scholarly journals Meta-analysis of transcriptomic data reveals clusters of consistently deregulated gene and disease ontologies in Down syndrome

2021 ◽  
Vol 17 (9) ◽  
pp. e1009317
Author(s):  
Ilario De Toma ◽  
Cesar Sierra ◽  
Mara Dierssen
Keyword(s):  
Down Syndrome ◽  
Differential Expression ◽  
Web Application ◽  
Meta Analysis ◽  
Chromosome 21 ◽  
Complex Disorders ◽  
Transcriptomic Data ◽  
Genome Wide ◽  
A Genome ◽  
The Impact

Trisomy of human chromosome 21 (HSA21) causes Down syndrome (DS). The trisomy does not simply result in the upregulation of HSA21--encoded genes but also leads to a genome-wide transcriptomic deregulation, which affect differently each tissue and cell type as a result of epigenetic mechanisms and protein-protein interactions. We performed a meta-analysis integrating the differential expression (DE) analyses of all publicly available transcriptomic datasets, both in human and mouse, comparing trisomic and euploid transcriptomes from different sources. We integrated all these data in a “DS network”. We found that genome wide deregulation as a consequence of trisomy 21 is not arbitrary, but involves deregulation of specific molecular cascades in which both HSA21 genes and HSA21 interactors are more consistently deregulated compared to other genes. In fact, gene deregulation happens in “clusters”, so that groups from 2 to 13 genes are found consistently deregulated. Most of these events of “co-deregulation” involve genes belonging to the same GO category, and genes associated with the same disease class. The most consistent changes are enriched in interferon related categories and neutrophil activation, reinforcing the concept that DS is an inflammatory disease. Our results also suggest that the impact of the trisomy might diverge in each tissue due to the different gene set deregulation, even though the triplicated genes are the same. Our original method to integrate transcriptomic data confirmed not only the importance of known genes, such as SOD1, but also detected new ones that could be extremely useful for generating or confirming hypotheses and supporting new putative therapeutic candidates. We created “metaDEA” an R package that uses our method to integrate every kind of transcriptomic data and therefore could be used with other complex disorders, such as cancer. We also created a user-friendly web application to query Ensembl gene IDs and retrieve all the information of their differential expression across the datasets.

scholarly journals The Impact of Green Space on Violent Crime in Urban Environments: An Evidence Synthesis

2019 ◽  
Vol 16 (24) ◽  
pp. 5119 ◽  
Author(s):  
Mardelle Shepley ◽  
Naomi Sachs ◽  
Hessam Sadatsafavi ◽  
Christine Fournier ◽  
Kati Peditto
Keyword(s):  
Violent Crime ◽  
Quantitative Methods ◽  
Green Space ◽  
Evidence Synthesis ◽  
Meta Analysis ◽  
Urban Environments ◽  
Tree Cover ◽  
Future Research ◽  
Community Perception ◽  
The Impact

Can the presence of green space in urban environments reduce the frequency of violent crime? To ascertain the evidence on this topic, we conducted an in-depth literature review using the PRISMA checklist. The search parameters included US articles written in English and published since 2000. More than 30,000 potential paper titles were identified and ultimately, 45 papers were selected for inclusion. Green spaces typically comprised tree cover, parks and ground cover. Criminal behaviors typically included murder, assault, and theft. The majority of the research reviewed involved quantitative methods (e.g., comparison of green space area to crime data). We extracted multiple mechanisms from the literature that may account for the impact of green space on crime including social interaction and recreation, community perception, biophilic stress reduction, climate modulation, and spaces expressing territorial definition. Recommendations are made for future research, such as meta-analysis of existing data and the development of grounded theory through qualitative data-gathering methods. By providing evidence that access to nature has a mitigating impact on violence in urban settings, city governments and communities are empowered to support these interventions.

VP82 Impact of Evidence Synthesis Methods on Outcome of Economic Evaluation

2019 ◽  
Vol 35 (S1) ◽  
pp. 93-94
Author(s):  
Claire Gorry ◽  
Joy Leahy ◽  
Felicity Lamrock ◽  
Cathal Walsh ◽  
Arthur White ◽  
...  
Keyword(s):  
Economic Evaluation ◽  
Proportional Hazards ◽  
Failure Time ◽  
Evidence Synthesis ◽  
Meta Analysis ◽  
Trial Data ◽  
Alternative Methods ◽  
Synthesis Methods ◽  
Proportional Hazards Assumption ◽  
The Impact

IntroductionEvidence synthesis (ES) is often required for economic evaluation (EE) of pharmaceuticals. Commonly used methods are based on the assumption of proportional hazards in trial data, using the hazard ratio (HR). Alternative methods for ES are increasingly used in EE, in situations where the pattern of hazards in the trial data indicates that the proportional hazards assumption may be violated. The impact of these methodological choices on model outcomes is explored.MethodsA network of trials of BRAF-targeted treatments for advanced melanoma, derived using a systematic review of the literature, is chosen for the study. Guyot's method is used to create individual-patient Kaplan-Meier (K-M) data from published survival curves. Log-cumulative hazard plots and Schoenfeld residuals are derived to examine patterns in hazards within the trial data. All analyses are conducted in R version 3.5.0©. Three alternative methods for ES are tested: 1) Network meta-analysis (NMA) based on published HRs and the assumption of proportional hazards. 2) NMA using fractional polynomials (FP) based on digitised K-M data, allowing the relaxation of the proportional hazards assumption. 3) NMA using an accelerated failure time (AFT) model based on digitised K-M data, allowing the relaxation of the proportional hazards assumption. The derived estimates of relative efficacy from each method are applied in a partitioned survival cost-effectiveness model programmed in Microsoft Excel™.ResultsThe model outcomes predicted by each method (HR, FP and AFT) are presented and compared. Both deterministic and probabilistic results are presented, alongside a discussion around how the uncertainty in these structural assumptions may be captured in EE.ConclusionsStructural assumptions in ES may lead to differences in model outcomes. The impact of these differences may be important in situations where decision uncertainty is high. Methods should be chosen and justified based on patterns of hazard present in the trial data.

scholarly journals Methods for the inclusion of real-world evidence in network meta-analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
David A. Jenkins ◽  
Humaira Hussein ◽  
Reynaldo Martina ◽  
Pascale Dequen-O’Byrne ◽  
Keith R. Abrams ◽  
...  
Keyword(s):  
Real World ◽  
Evidence Synthesis ◽  
Meta Analysis ◽  
Bayesian Hierarchical ◽  
Real World Evidence ◽  
Direct Head ◽  
Power Prior ◽  
Credible Intervals ◽  
Randomised Controlled ◽  
The Impact

Abstract Background Network Meta-Analysis (NMA) is a key component of submissions to reimbursement agencies world-wide, especially when there is limited direct head-to-head evidence for multiple technologies from randomised controlled trials (RCTs). Many NMAs include only data from RCTs. However, real-world evidence (RWE) is also becoming widely recognised as a valuable source of clinical data. This study aims to investigate methods for the inclusion of RWE in NMA and its impact on the level of uncertainty around the effectiveness estimates, with particular interest in effectiveness of fingolimod. Methods A range of methods for inclusion of RWE in evidence synthesis were investigated by applying them to an illustrative example in relapsing remitting multiple sclerosis (RRMS). A literature search to identify RCTs and RWE evaluating treatments in RRMS was conducted. To assess the impact of inclusion of RWE on the effectiveness estimates, Bayesian hierarchical and adapted power prior models were applied. The effect of the inclusion of RWE was investigated by varying the degree of down weighting of this part of evidence by the use of a power prior. Results Whilst the inclusion of the RWE led to an increase in the level of uncertainty surrounding effect estimates in this example, this depended on the method of inclusion adopted for the RWE. ‘Power prior’ NMA model resulted in stable effect estimates for fingolimod yet increasing the width of the credible intervals with increasing weight given to RWE data. The hierarchical NMA models were effective in allowing for heterogeneity between study designs, however, this also increased the level of uncertainty. Conclusion The ‘power prior’ method for the inclusion of RWE in NMAs indicates that the degree to which RWE is taken into account can have a significant impact on the overall level of uncertainty. The hierarchical modelling approach further allowed for accommodating differences between study types. Consequently, further work investigating both empirical evidence for biases associated with individual RWE studies and methods of elicitation from experts on the extent of such biases is warranted.

scholarly journals Overview of Federated Facility to Harmonize, Analyze and Management of Missing Data in Cohorts

2019 ◽  
Vol 9 (19) ◽  
pp. 4103 ◽  
Author(s):  
Hema Sekhar Reddy Rajula ◽  
Veronika Odintsova ◽  
Mirko Manchia ◽  
Vassilios Fanos
Keyword(s):  
Missing Data ◽  
Cohort Studies ◽  
Fixed Effects ◽  
Statistical Power ◽  
Evidence Synthesis ◽  
Meta Analysis ◽  
Database Systems ◽  
Contributing Factors ◽  
Meta Analyses ◽  
The Impact

Cohorts are instrumental for epidemiologically oriented observational studies. Cohort studies usually observe large groups of individuals for a specific period of time to identify the contributing factors to a specific outcome (for instance an illness) and create associations between risk factors and the outcome under study. In collaborative projects, federated data facilities are meta-database systems that are distributed across multiple locations that permit to analyze, combine, or harmonize data from different sources making them suitable for mega- and meta-analyses. The harmonization of data can increase the statistical power of studies through maximization of sample size, allowing for additional refined statistical analyses, which ultimately lead to answer research questions that could not be addressed while using a single study. Indeed, harmonized data can be analyzed through mega-analysis of raw data or fixed effects meta-analysis. Other types of data might be analyzed by e.g., random-effects meta-analyses or Bayesian evidence synthesis. In this article, we describe some methodological aspects related to the construction of a federated facility to optimize analyses of multiple datasets, the impact of missing data, and some methods for handling missing data in cohort studies.

scholarly journals Comparative efficacy and side-effect profile of ketamine and esketamine in the treatment of unipolar and bipolar depression: protocol for a systematic review and network meta-analysis

BMJ Open ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. e043457
Author(s):  
Zhiqing Zhan ◽  
Xichao Wang ◽  
Qing Chen ◽  
Zhidai Xiao ◽  
Bin Zhang
Keyword(s):  
Side Effect ◽  
Web Application ◽  
Bipolar Depression ◽  
Data Extraction ◽  
Meta Analysis ◽  
Antidepressant Drugs ◽  
Side Effect Profile ◽  
Open Label ◽  
Double Blind ◽  
Reference Selection

IntroductionDespite a range of antidepressant drugs and therapies, approximately one-third of patients fail to achieve meaningful recovery, prompting the urgent need for more effective treatment for depression. Several open-label studies randomised controlled trials (RCTs) and meta-analyses have been conducted to confirm the therapeutic efficacy and side effects of ketamine and esketamine. Esketamine is (S)- enantiomer of ketamine; however, there is limited evidence comparing esketamine and ketamine in treating unipolar and bipolar depression have been published so far.Methods and analysisWe will include all double-blind RCTs comparing efficacy and side-effect profile of ketamine and esketamine in the treatment of unipolar and bipolar depression. Our primary outcomes will be study-defined response at endpoint assessment; dropouts due to adverse events and other adverse drug reactions. Published studies will be retrieved through relevant database searches. Reference selection and data extraction will be independently completed by two investigators, resolving inconsistencies by consensus or a discussion with the third investigator. For each outcome, we will undertake a network meta-analysis to synthesise all evidence. Local and global methods will be used to evaluate consistency. We will assess the quality of evidence contributing to network estimates with the Confidence in Network Meta-Analysis web application.Ethics and disseminationThis work does not require ethics approval as it will be based on published studies. This review will be published in peer-reviewed journals.PROSPERO registration numberCRD42020201559.

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

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Virginia Chiocchia ◽  
Adriani Nikolakopoulou ◽  
Julian P. T. Higgins ◽  
Matthew J. Page ◽  
Theodoros Papakonstantinou ◽  
...  
Keyword(s):  
Web Application ◽  
Clinical Decision Making ◽  
Meta Analysis ◽  
Pairwise Comparison ◽  
Risk Of Bias ◽  
Low Risk ◽  
Pairwise Comparisons ◽  
Coronary Syndrome ◽  
Meta Analyses ◽  
The Impact

Abstract 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.

Interventions for students with autism in inclusive settings: A best-evidence synthesis and meta-analysis.

2019 ◽  
Vol 145 (5) ◽  
pp. 490-507 ◽  
Author(s):  
Laci Watkins ◽  
Katherine Ledbetter-Cho ◽  
Mark O'Reilly ◽  
Lucy Barnard-Brak ◽  
Pau Garcia-Grau
Keyword(s):  
Evidence Synthesis ◽  
Meta Analysis ◽  
Best Evidence Synthesis ◽  
Inclusive Settings ◽  
Students With Autism
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