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Julian P T Higgins - One of the best experts on this subject based on the ideXlab platform.
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predictive distributions for between Study Heterogeneity and simple methods for their application in bayesian meta analysis
Statistics in Medicine, 2015Co-Authors: Rebecca M Turner, Dan Jackson, Yinghui Wei, Simon G Thompson, Julian P T HigginsAbstract:Numerous meta-analyses in healthcare research combine results from only a small number of studies, for which the variance representing between-Study Heterogeneity is estimated imprecisely. A Bayesian approach to estimation allows external evidence on the expected magnitude of Heterogeneity to be incorporated. The aim of this paper is to provide tools that improve the accessibility of Bayesian meta-analysis. We present two methods for implementing Bayesian meta-analysis, using numerical integration and importance sampling techniques. Based on 14,886 binary outcome meta-analyses in the Cochrane Database of Systematic Reviews, we derive a novel set of predictive distributions for the degree of Heterogeneity expected in 80 settings depending on the outcomes assessed and comparisons made. These can be used as prior distributions for Heterogeneity in future meta-analyses. The two methods are implemented in R, for which code is provided. Both methods produce equivalent results to standard but more complex Markov chain Monte Carlo approaches. The priors are derived as log-normal distributions for the between-Study variance, applicable to meta-analyses of binary outcomes on the log odds-ratio scale. The methods are applied to two example meta-analyses, incorporating the relevant predictive distributions as prior distributions for between-Study Heterogeneity. We have provided resources to facilitate Bayesian meta-analysis, in a form accessible to applied researchers, which allow relevant prior information on the degree of Heterogeneity to be incorporated.
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predictive distributions for between Study Heterogeneity and simple methods for their application in bayesian meta analysis
Statistics in Medicine, 2015Co-Authors: Rebecca M Turner, Dan Jackson, Simon G Thompson, Julian P T HigginsAbstract:Numerous meta-analyses in healthcare research combine results from only a small number of studies, for which the variance representing between-Study Heterogeneity is estimated imprecisely. A Bayesian approach to estimation allows external evidence on the expected magnitude of Heterogeneity to be incorporated. The aim of this paper is to provide tools that improve the accessibility of Bayesian meta-analysis. We present two methods for implementing Bayesian meta-analysis, using numerical integration and importance sampling techniques. Based on 14 886 binary outcome meta-analyses in the Cochrane Database of Systematic Reviews, we derive a novel set of predictive distributions for the degree of Heterogeneity expected in 80 settings depending on the outcomes assessed and comparisons made. These can be used as prior distributions for Heterogeneity in future meta-analyses. The two methods are implemented in R, for which code is provided. Both methods produce equivalent results to standard but more complex Markov chain Monte Carlo approaches. The priors are derived as log-normal distributions for the between-Study variance, applicable to meta-analyses of binary outcomes on the log odds-ratio scale. The methods are applied to two example meta-analyses, incorporating the relevant predictive distributions as prior distributions for between-Study Heterogeneity. We have provided resources to facilitate Bayesian meta-analysis, in a form accessible to applied researchers, which allow relevant prior information on the degree of Heterogeneity to be incorporated. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
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a design by treatment interaction model for network meta analysis with random inconsistency effects
Statistics in Medicine, 2014Co-Authors: Dan Jackson, Ian R White, Julian P T Higgins, Jessica K Barrett, Stephen RiceAbstract:Network meta-analysis is becoming more popular as a way to analyse multiple treatments simultaneously and, in the right circumstances, rank treatments. A difficulty in practice is the possibility of ‘inconsistency’ or ‘incoherence’, where direct evidence and indirect evidence are not in agreement. Here, we develop a random-effects implementation of the recently proposed design-by-treatment interaction model, using these random effects to model inconsistency and estimate the parameters of primary interest. Our proposal is a generalisation of the model proposed by Lumley and allows trials with three or more arms to be included in the analysis. Our methods also facilitate the ranking of treatments under inconsistency. We derive R and I2 statistics to quantify the impact of the between-Study Heterogeneity and the inconsistency. We apply our model to two examples. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.
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predicting the extent of Heterogeneity in meta analysis using empirical data from the cochrane database of systematic reviews
International Journal of Epidemiology, 2012Co-Authors: Rebecca M Turner, Simon G Thompson, Jonathan Davey, Mike Clarke, Julian P T HigginsAbstract:Background Many meta-analyses contain only a small number of studies, which makes it difficult to estimate the extent of between-Study Heterogeneity. Bayesian meta-analysis allows incorporation of external evidence on Heterogeneity, and offers advantages over conventional random-effects meta-analysis. To assist in this, we provide empirical evidence on the likely extent of Heterogeneity in particular areas of health care. Methods Our analyses included 14 886 meta-analyses from the Cochrane Database of Systematic Reviews. We classified each meta-analysis according to the type of outcome, type of intervention comparison and medical specialty. By modelling the Study data from all meta-analyses simultaneously, using the log odds ratio scale, we investigated the impact of meta-analysis characteristics on the underlying between-Study Heterogeneity variance. Predictive distributions were obtained for the Heterogeneity expected in future meta-analyses. Results Between-Study Heterogeneity variances for meta-analyses in which the outcome was all-cause mortality were found to be on average 17% (95% CI 10-26) of variances for other outcomes. In meta-analyses comparing two active pharmacological interventions, Heterogeneity was on average 75% (95% CI 58-95) of variances for non-pharmacological interventions. Meta-analysis size was found to have only a small effect on Heterogeneity. Predictive distributions are presented for nine different settings, defined by type of outcome and type of intervention comparison. For example, for a planned meta-analysis comparing a pharmacological intervention against placebo or control with a subjectively measured outcome, the predictive distribution for Heterogeneity is a log-normal (-2.13, 1.58(2)) distribution, which has a median value of 0.12. In an example of meta-analysis of six studies, incorporating external evidence led to a smaller Heterogeneity estimate and a narrower confidence interval for the combined intervention effect. Conclusions Meta-analysis characteristics were strongly associated with the degree of between-Study Heterogeneity, and predictive distributions for Heterogeneity differed substantially across settings. The informative priors provided will be very beneficial in future meta-analyses including few studies.
John P A Ioannidis - One of the best experts on this subject based on the ideXlab platform.
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an overview of methods for network meta analysis using individual participant data when do benefits arise
Statistical Methods in Medical Research, 2018Co-Authors: Thomas P A Debray, John P A Ioannidis, Ewoud Schuit, Orestis Efthimiou, Johannes B Reitsma, Georgia Salanti, Karel G M MoonsAbstract:Network meta-analysis (NMA) is a common approach to summarizing relative treatment effects from randomized trials with different treatment comparisons. Most NMAs are based on published aggregate data (AD) and have limited possibilities for investigating the extent of network consistency and between-Study Heterogeneity. Given that individual participant data (IPD) are considered the gold standard in evidence synthesis, we explored statistical methods for IPD-NMA and investigated their potential advantages and limitations, compared with AD-NMA. We discuss several one-stage random-effects NMA models that account for within-trial imbalances, treatment effect modifiers, missing response data and longitudinal responses. We illustrate all models in a case Study of 18 antidepressant trials with a continuous endpoint (the Hamilton Depression Score). All trials suffered from drop-out; missingness of longitudinal responses ranged from 21 to 41% after 6 weeks follow-up. Our results indicate that NMA based on IPD may lead to increased precision of estimated treatment effects. Furthermore, it can help to improve network consistency and explain between-Study Heterogeneity by adjusting for participant-level effect modifiers and adopting more advanced models for dealing with missing response data. We conclude that implementation of IPD-NMA should be considered when trials are affected by substantial drop-out rate, and when treatment effects are potentially influenced by participant-level covariates.
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systematic evaluation of the associations between environmental risk factors and dementia an umbrella review of systematic reviews and meta analyses
Alzheimers & Dementia, 2017Co-Authors: Vanesa Bellou, John P A Ioannidis, Evangelos Evangelou, Lazaros Belbasis, Ioanna Tzoulaki, Lefkos T MiddletonAbstract:Abstract Introduction Dementia is a heterogeneous neurodegenerative disease, whose etiology results from a complex interplay between environmental and genetic factors. Methods We searched PubMed to identify meta-analyses of observational studies that examined associations between nongenetic factors and dementia. We estimated the summary effect size using random-effects and fixed-effects model, the 95% CI, and the 95% prediction interval. We assessed the between-Study Heterogeneity (I-square), evidence of small-Study effects, and excess significance. Results A total of 76 unique associations were examined. By applying standardized criteria, seven associations presented convincing evidence. These associations pertained to benzodiazepines use, depression at any age, late-life depression, and frequency of social contacts for all types of dementia; late-life depression for Alzheimer's disease; and type 2 diabetes mellitus for vascular dementia and Alzheimer's disease. Discussion Several risk factors present substantial evidence for association with dementia and should be assessed as potential targets for interventions, but these associations may not necessarily be causal.
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the hartung knapp sidik jonkman method for random effects meta analysis is straightforward and considerably outperforms the standard dersimonian laird method
BMC Medical Research Methodology, 2014Co-Authors: Joanna Inthout, John P A Ioannidis, George F BormAbstract:The DerSimonian and Laird approach (DL) is widely used for random effects meta-analysis, but this often results in inappropriate type I error rates. The method described by Hartung, Knapp, Sidik and Jonkman (HKSJ) is known to perform better when trials of similar size are combined. However evidence in realistic situations, where one trial might be much larger than the other trials, is lacking. We aimed to evaluate the relative performance of the DL and HKSJ methods when studies of different sizes are combined and to develop a simple method to convert DL results to HKSJ results. We evaluated the performance of the HKSJ versus DL approach in simulated meta-analyses of 2–20 trials with varying sample sizes and between-Study Heterogeneity, and allowing trials to have various sizes, e.g. 25% of the trials being 10-times larger than the smaller trials. We also compared the number of “positive” (statistically significant at p = 3 studies of interventions from the Cochrane Database of Systematic Reviews. The simulations showed that the HKSJ method consistently resulted in more adequate error rates than the DL method. When the significance level was 5%, the HKSJ error rates at most doubled, whereas for DL they could be over 30%. DL, and, far less so, HKSJ had more inflated error rates when the combined studies had unequal sizes and between-Study Heterogeneity. The empirical data from 689 meta-analyses showed that 25.1% of the significant findings for the DL method were non-significant with the HKSJ method. DL results can be easily converted into HKSJ results. Our simulations showed that the HKSJ method consistently results in more adequate error rates than the DL method, especially when the number of studies is small, and can easily be applied routinely in meta-analyses. Even with the HKSJ method, extra caution is needed when there are = <5 studies of very unequal sizes.
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susceptibility variants for rheumatoid arthritis in the traf1 c5 and 6q23 loci a meta analysis
Annals of the Rheumatic Diseases, 2010Co-Authors: Nikolaos A Patsopoulos, John P A IoannidisAbstract:Objectives: Genome-wide association studies have proposed susceptibility variants for rheumatoid arthritis in the TRAF1-C5 locus and 6q23 region. Furthermore, additional independent studies have investigated the same or highly linked polymorphisms in the same regions. We meta-analyzed the available evidence on these proposed associations. Methods: Data were synthesized for four polymorphisms: rs3761847 (n=12 datasets) and rs2900180 (n=9 datasets) in the TRAF1-C5 locus, and rs10499194 (n=5 datasets) and rs6920220 (n=7 datasets) in the 6q23 region. We also performed meta-analyses for subgroups defined by anti-CCP and RF status. Results: The polymorphism rs6920220 reached genome-wide statistically significance with p=7.9x10 -17 and an allelic odds ratio of 1.24 (95% CI: 1.18-1.30) and no between-Study Heterogeneity (I 2 =0%). The risk was significantly stronger in patients with anti-CCP antibodies and in patients with rheumatoid factor (RF). The other three variants showed large between-Study Heterogeneity across datasets (I 2 range 74-82%); rs10499194 was nominally statistically significant after exclusion of the discovery data. Two variants had genome-wide statistical significance in subgroups defined by the presence of RF (rs3761847 and rs6920220) or anti-CCP (rs6920220). Conclusions: Genetic markers in the 6q23 region and TRAF1-C5 are associated with rheumatoid arthritis, in particular with positive anti-CCP and rheumatoid factor profile. With the exception of rs6920220 that shows highly consistent results, other proposed markers have high between-Study Heterogeneity that may reflect unrecognized phenotypic or genetic variability (e.g. gene environment interactions) within rheumatoid arthritis. Furthermore, these markers may not be the true causative loci but rather be in linkage disequilibrium with the true ones.
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sensitivity of between Study Heterogeneity in meta analysis proposed metrics and empirical evaluation
International Journal of Epidemiology, 2008Co-Authors: Nikolaos A Patsopoulos, John P A Ioannidis, Evangelos EvangelouAbstract:BACKGROUND:Several approaches are available for evaluating Heterogeneity in meta-analysis. Sensitivity analyses are often used, but these are often implemented in various non-standardized ways. METHODS:We developed and implemented sequential and combinatorial algorithms that evaluate the change in between-Study Heterogeneity as one or more studies are excluded from the calculations. The algorithms exclude studies aiming to achieve either the maximum or the minimum final I(2) below a desired pre-set threshold. We applied these algorithms in databases of meta-analyses of binary outcome and >/=4 studies from Cochrane Database of Systematic Reviews (Issue 4, 2005, n = 1011) and meta-analyses of genetic associations (n = 50). Two I(2) thresholds were used (50% and 25%). RESULTS:Both algorithms have succeeded in achieving the pre-specified final I(2) thresholds. Differences in the number of excluded studies varied from 0% to 6% depending on the database and the Heterogeneity threshold, while it was common to exclude different specific studies. Among meta-analyses with initial I(2) > 50%, in the large majority [19 (90.5%) and 208 (85.9%) in genetic and Cochrane meta-analyses, respectively] exclusion of one or two studies sufficed to decrease I(2) 25%, in most cases [16 (57.1%) and 382 (81.3%), respectively) exclusion of one or two studies sufficed to decrease Heterogeneity even <25%. The number of excluded studies correlated modestly with initial estimated I(2) (correlation coefficients 0.52-0.68 depending on algorithm used). CONCLUSIONS:The proposed algorithms can be routinely applied in meta-analyses as standardized sensitivity analyses for Heterogeneity. Caution is needed evaluating post hoc which specific studies are responsible for the Heterogeneity.
Dan Jackson - One of the best experts on this subject based on the ideXlab platform.
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a matrix based method of moments for fitting multivariate network meta analysis models with multiple outcomes and random inconsistency effects
arXiv: Methodology, 2017Co-Authors: Dan Jackson, Sylwia Bujkiewicz, Martin Law, Richard D Riley, Ian R WhiteAbstract:Random-effects meta-analyses are very commonly used in medical statistics. Recent methodological developments include multivariate (multiple outcomes) and network (multiple treatments) meta-analysis. Here we provide a new model and corresponding estimation procedure for multivariate network meta-analysis, so that multiple outcomes and treatments can be included in a single analysis. Our new multivariate model is a direct extension of a univariate model for network meta-analysis that has recently been proposed. We allow two types of unknown variance parameters in our model, which represent between-Study Heterogeneity and inconsistency. Inconsistency arises when different forms of direct and indirect evidence are not in agreement, even having taken between-Study Heterogeneity into account. However the consistency assumption is often assumed in practice and so we also explain how to fit a reduced model which makes this assumption. Our estimation method extends several other commonly used methods for meta-analysis, including the method proposed by DerSimonian and Laird (1986). We investigate the use of our proposed methods in the context of a real example.
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predictive distributions for between Study Heterogeneity and simple methods for their application in bayesian meta analysis
Statistics in Medicine, 2015Co-Authors: Rebecca M Turner, Dan Jackson, Yinghui Wei, Simon G Thompson, Julian P T HigginsAbstract:Numerous meta-analyses in healthcare research combine results from only a small number of studies, for which the variance representing between-Study Heterogeneity is estimated imprecisely. A Bayesian approach to estimation allows external evidence on the expected magnitude of Heterogeneity to be incorporated. The aim of this paper is to provide tools that improve the accessibility of Bayesian meta-analysis. We present two methods for implementing Bayesian meta-analysis, using numerical integration and importance sampling techniques. Based on 14,886 binary outcome meta-analyses in the Cochrane Database of Systematic Reviews, we derive a novel set of predictive distributions for the degree of Heterogeneity expected in 80 settings depending on the outcomes assessed and comparisons made. These can be used as prior distributions for Heterogeneity in future meta-analyses. The two methods are implemented in R, for which code is provided. Both methods produce equivalent results to standard but more complex Markov chain Monte Carlo approaches. The priors are derived as log-normal distributions for the between-Study variance, applicable to meta-analyses of binary outcomes on the log odds-ratio scale. The methods are applied to two example meta-analyses, incorporating the relevant predictive distributions as prior distributions for between-Study Heterogeneity. We have provided resources to facilitate Bayesian meta-analysis, in a form accessible to applied researchers, which allow relevant prior information on the degree of Heterogeneity to be incorporated.
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predictive distributions for between Study Heterogeneity and simple methods for their application in bayesian meta analysis
Statistics in Medicine, 2015Co-Authors: Rebecca M Turner, Dan Jackson, Simon G Thompson, Julian P T HigginsAbstract:Numerous meta-analyses in healthcare research combine results from only a small number of studies, for which the variance representing between-Study Heterogeneity is estimated imprecisely. A Bayesian approach to estimation allows external evidence on the expected magnitude of Heterogeneity to be incorporated. The aim of this paper is to provide tools that improve the accessibility of Bayesian meta-analysis. We present two methods for implementing Bayesian meta-analysis, using numerical integration and importance sampling techniques. Based on 14 886 binary outcome meta-analyses in the Cochrane Database of Systematic Reviews, we derive a novel set of predictive distributions for the degree of Heterogeneity expected in 80 settings depending on the outcomes assessed and comparisons made. These can be used as prior distributions for Heterogeneity in future meta-analyses. The two methods are implemented in R, for which code is provided. Both methods produce equivalent results to standard but more complex Markov chain Monte Carlo approaches. The priors are derived as log-normal distributions for the between-Study variance, applicable to meta-analyses of binary outcomes on the log odds-ratio scale. The methods are applied to two example meta-analyses, incorporating the relevant predictive distributions as prior distributions for between-Study Heterogeneity. We have provided resources to facilitate Bayesian meta-analysis, in a form accessible to applied researchers, which allow relevant prior information on the degree of Heterogeneity to be incorporated. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
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a design by treatment interaction model for network meta analysis with random inconsistency effects
Statistics in Medicine, 2014Co-Authors: Dan Jackson, Ian R White, Julian P T Higgins, Jessica K Barrett, Stephen RiceAbstract:Network meta-analysis is becoming more popular as a way to analyse multiple treatments simultaneously and, in the right circumstances, rank treatments. A difficulty in practice is the possibility of ‘inconsistency’ or ‘incoherence’, where direct evidence and indirect evidence are not in agreement. Here, we develop a random-effects implementation of the recently proposed design-by-treatment interaction model, using these random effects to model inconsistency and estimate the parameters of primary interest. Our proposal is a generalisation of the model proposed by Lumley and allows trials with three or more arms to be included in the analysis. Our methods also facilitate the ranking of treatments under inconsistency. We derive R and I2 statistics to quantify the impact of the between-Study Heterogeneity and the inconsistency. We apply our model to two examples. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.
Rebecca M Turner - One of the best experts on this subject based on the ideXlab platform.
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predictive distributions for between Study Heterogeneity and simple methods for their application in bayesian meta analysis
Statistics in Medicine, 2015Co-Authors: Rebecca M Turner, Dan Jackson, Yinghui Wei, Simon G Thompson, Julian P T HigginsAbstract:Numerous meta-analyses in healthcare research combine results from only a small number of studies, for which the variance representing between-Study Heterogeneity is estimated imprecisely. A Bayesian approach to estimation allows external evidence on the expected magnitude of Heterogeneity to be incorporated. The aim of this paper is to provide tools that improve the accessibility of Bayesian meta-analysis. We present two methods for implementing Bayesian meta-analysis, using numerical integration and importance sampling techniques. Based on 14,886 binary outcome meta-analyses in the Cochrane Database of Systematic Reviews, we derive a novel set of predictive distributions for the degree of Heterogeneity expected in 80 settings depending on the outcomes assessed and comparisons made. These can be used as prior distributions for Heterogeneity in future meta-analyses. The two methods are implemented in R, for which code is provided. Both methods produce equivalent results to standard but more complex Markov chain Monte Carlo approaches. The priors are derived as log-normal distributions for the between-Study variance, applicable to meta-analyses of binary outcomes on the log odds-ratio scale. The methods are applied to two example meta-analyses, incorporating the relevant predictive distributions as prior distributions for between-Study Heterogeneity. We have provided resources to facilitate Bayesian meta-analysis, in a form accessible to applied researchers, which allow relevant prior information on the degree of Heterogeneity to be incorporated.
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predictive distributions for between Study Heterogeneity and simple methods for their application in bayesian meta analysis
Statistics in Medicine, 2015Co-Authors: Rebecca M Turner, Dan Jackson, Simon G Thompson, Julian P T HigginsAbstract:Numerous meta-analyses in healthcare research combine results from only a small number of studies, for which the variance representing between-Study Heterogeneity is estimated imprecisely. A Bayesian approach to estimation allows external evidence on the expected magnitude of Heterogeneity to be incorporated. The aim of this paper is to provide tools that improve the accessibility of Bayesian meta-analysis. We present two methods for implementing Bayesian meta-analysis, using numerical integration and importance sampling techniques. Based on 14 886 binary outcome meta-analyses in the Cochrane Database of Systematic Reviews, we derive a novel set of predictive distributions for the degree of Heterogeneity expected in 80 settings depending on the outcomes assessed and comparisons made. These can be used as prior distributions for Heterogeneity in future meta-analyses. The two methods are implemented in R, for which code is provided. Both methods produce equivalent results to standard but more complex Markov chain Monte Carlo approaches. The priors are derived as log-normal distributions for the between-Study variance, applicable to meta-analyses of binary outcomes on the log odds-ratio scale. The methods are applied to two example meta-analyses, incorporating the relevant predictive distributions as prior distributions for between-Study Heterogeneity. We have provided resources to facilitate Bayesian meta-analysis, in a form accessible to applied researchers, which allow relevant prior information on the degree of Heterogeneity to be incorporated. © 2014 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
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predicting the extent of Heterogeneity in meta analysis using empirical data from the cochrane database of systematic reviews
International Journal of Epidemiology, 2012Co-Authors: Rebecca M Turner, Simon G Thompson, Jonathan Davey, Mike Clarke, Julian P T HigginsAbstract:Background Many meta-analyses contain only a small number of studies, which makes it difficult to estimate the extent of between-Study Heterogeneity. Bayesian meta-analysis allows incorporation of external evidence on Heterogeneity, and offers advantages over conventional random-effects meta-analysis. To assist in this, we provide empirical evidence on the likely extent of Heterogeneity in particular areas of health care. Methods Our analyses included 14 886 meta-analyses from the Cochrane Database of Systematic Reviews. We classified each meta-analysis according to the type of outcome, type of intervention comparison and medical specialty. By modelling the Study data from all meta-analyses simultaneously, using the log odds ratio scale, we investigated the impact of meta-analysis characteristics on the underlying between-Study Heterogeneity variance. Predictive distributions were obtained for the Heterogeneity expected in future meta-analyses. Results Between-Study Heterogeneity variances for meta-analyses in which the outcome was all-cause mortality were found to be on average 17% (95% CI 10-26) of variances for other outcomes. In meta-analyses comparing two active pharmacological interventions, Heterogeneity was on average 75% (95% CI 58-95) of variances for non-pharmacological interventions. Meta-analysis size was found to have only a small effect on Heterogeneity. Predictive distributions are presented for nine different settings, defined by type of outcome and type of intervention comparison. For example, for a planned meta-analysis comparing a pharmacological intervention against placebo or control with a subjectively measured outcome, the predictive distribution for Heterogeneity is a log-normal (-2.13, 1.58(2)) distribution, which has a median value of 0.12. In an example of meta-analysis of six studies, incorporating external evidence led to a smaller Heterogeneity estimate and a narrower confidence interval for the combined intervention effect. Conclusions Meta-analysis characteristics were strongly associated with the degree of between-Study Heterogeneity, and predictive distributions for Heterogeneity differed substantially across settings. The informative priors provided will be very beneficial in future meta-analyses including few studies.
Koichi Suehiro - One of the best experts on this subject based on the ideXlab platform.
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Accuracy and precision of minimally-invasive cardiac output monitoring in children: a systematic review and meta-analysis
Journal of Clinical Monitoring and Computing, 2016Co-Authors: Koichi Suehiro, Alexandre Joosten, Linda Suk-ling Murphy, Olivier Desebbe, Brenton Alexander, Maxime CannessonAbstract:Several minimally-invasive technologies are available for cardiac output (CO) measurement in children, but the accuracy and precision of these devices have not yet been evaluated in a systematic review and meta-analysis. We conducted a comprehensive search of the medical literature in PubMed, Cochrane Library of Clinical Trials, Scopus, and Web of Science from its inception to June 2014 assessing the accuracy and precision of all minimally-invasive CO monitoring systems used in children when compared with CO monitoring reference methods. Pooled mean bias, standard deviation, and mean percentage error of included studies were calculated using a random-effects model. The inter-Study Heterogeneity was also assessed using an I^2 statistic. A total of 20 studies (624 patients) were included. The overall random-effects pooled bias, and mean percentage error were 0.13 ± 0.44 l min^−1 and 29.1 %, respectively. Significant inter-Study Heterogeneity was detected ( P
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accuracy and precision of minimally invasive cardiac output monitoring in children a systematic review and meta analysis
Journal of Clinical Monitoring and Computing, 2016Co-Authors: Koichi Suehiro, Alexandre Joosten, Linda Suk-ling Murphy, Olivier Desebbe, Brenton Alexander, Sanghyun Kim, Maxime CannessonAbstract:Several minimally-invasive technologies are available for cardiac output (CO) measurement in children, but the accuracy and precision of these devices have not yet been evaluated in a systematic review and meta-analysis. We conducted a comprehensive search of the medical literature in PubMed, Cochrane Library of Clinical Trials, Scopus, and Web of Science from its inception to June 2014 assessing the accuracy and precision of all minimally-invasive CO monitoring systems used in children when compared with CO monitoring reference methods. Pooled mean bias, standard deviation, and mean percentage error of included studies were calculated using a random-effects model. The inter-Study Heterogeneity was also assessed using an I(2) statistic. A total of 20 studies (624 patients) were included. The overall random-effects pooled bias, and mean percentage error were 0.13 ± 0.44 l min(-1) and 29.1 %, respectively. Significant inter-Study Heterogeneity was detected (P < 0.0001, I(2) = 98.3 %). In the sub-analysis regarding the device, electrical cardiometry showed the smallest bias (-0.03 l min(-1)) and lowest percentage error (23.6 %). Significant residual Heterogeneity remained after conducting sensitivity and subgroup analyses based on the various Study characteristics. By meta-regression analysis, we found no independent effects of Study characteristics on weighted mean difference between reference and tested methods. Although the pooled bias was small, the mean pooled percentage error was in the gray zone of clinical applicability. In the sub-group analysis, electrical cardiometry was the device that provided the most accurate measurement. However, a high Heterogeneity between studies was found, likely due to a wide range of Study characteristics.
