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Background: Although aggregate data (AD) from randomised clinical trials (RCTs) are used in the majority of network meta-analyses (NMAs), other study designs (e.g., cohort studies and other non-randomised studies, NRS) can be informative about relative treatment effects. The individual participant data (IPD) of the study, when available, are preferred to AD for adjusting for important participant characteristics and to better handle heterogeneity and inconsistency in the network. Results: We developed the R package crossnma to perform cross-format (IPD and AD) and cross-design (RCT and NRS) NMA and network meta-regression (NMR). The models are implemented as Bayesian three-level hierarchical models using Just Another Gibbs Sampler (JAGS) software within the R environment. The R package crossnma includes functions to automatically create the JAGS model, reformat the data (based on user input), assess convergence and summarize the results. We demonstrate the workflow within crossnma by using a network of six trials comparing four treatments. Conclusions: The R package crossnma enables the user to perform NMA and NMR with different data types in a Bayesian framework and facilitates the inclusion of all types of evidence recognising differences in risk of bias. [ABSTRACT FROM AUTHOR]

The LFK index has been promoted as an improved method to detect bias in meta‐analysis. Putatively, its performance does not depend on the number of studies in the meta‐analysis. We conducted a simulation study, comparing the LFK index test to three standard tests for funnel plot asymmetry in settings with smaller or larger group sample sizes. In general, false positive rates of the LFK index test markedly depended on the number and size of studies as well as the between‐study heterogeneity with values between 0% and almost 30%. Egger's test adhered well to the pre‐specified significance level of 5% under homogeneity, but was too liberal (smaller groups) or conservative (larger groups) under heterogeneity. The rank test was too conservative for most simulation scenarios. The Thompson–Sharp test was too conservative under homogeneity, but adhered well to the significance level in case of heterogeneity. The true positive rate of the LFK index test was only larger compared with classic tests if the false positive rate was inflated. The power of classic tests was similar or larger than the LFK index test if the false positive rate of the LFK index test was used as significance level for the classic tests. Under ideal conditions, the false positive rate of the LFK index test markedly and unpredictably depends on the number and sample size of studies as well as the extent of between‐study heterogeneity. The LFK index test in its current implementation should not be used to assess funnel plot asymmetry in meta‐analysis. [ABSTRACT FROM AUTHOR]

The Peto odds ratio is a well‐known effect measure in meta‐analysis of binary outcomes. For pairwise comparisons, the Peto odds ratio estimator can be severely biased in the situation of unbalanced sample sizes in the two treatment groups or large treatment effects. In this publication, we evaluate Peto odds ratio estimators in the setting of multi‐arm studies and in network meta‐analysis using illustrative examples. We observe that Peto odds ratio estimators in a multi‐arm study are inconsistent if the observed event probabilities are different or the sample sizes of treatment groups are unbalanced. The same problem emerges in network meta‐analysis including only two‐arm studies and translates to indirect comparisons of pairwise meta‐analyses. We conclude that the Peto odds ratio should not be used as effect measure in network meta‐analysis or indirect comparisons of pairwise meta‐analyses. [ABSTRACT FROM AUTHOR]

Updating systematic reviews is often a time‐consuming process that involves a lot of human effort and is therefore not conducted as often as it should be. The aim of our research project was to explore the potential of machine learning methods to reduce human workload. Furthermore, we evaluated the performance of deep learning methods in comparison to more established machine learning methods. We used three available reviews of diagnostic test studies as the data set. In order to identify relevant publications, we used typical text pre‐processing methods. The reference standard for the evaluation was the human‐consensus based on binary classification (inclusion, exclusion). For the evaluation of the models, various scenarios were generated using a grid of combinations of data preprocessing steps. Moreover, we evaluated each machine learning approach with an approach‐specific predefined grid of tuning parameters using the Brier score metric. The best performance was obtained with an ensemble method for two of the reviews, and by a deep learning approach for the other review. Yet, the final performance of approaches strongly depends on data preparation. Overall, machine learning methods provided reasonable classification. It seems possible to reduce human workload in updating systematic reviews by using machine learning methods. Yet, as the influence of data preprocessing on the final performance seems to be at least as important as choosing the specific machine learning approach, users should not blindly expect a good performance by solely using approaches from a popular class, such as deep learning. [ABSTRACT FROM AUTHOR]

In the era of the "reproducibility crisis" and the "P‐value controversy" new ways of presentation and interpretation of the results of a meta‐analysis are desirable. One suggestion that has been made for single studies almost six decades ago and taken up now and then is the P‐value function. For a given outcome, this function assigns a P‐value to each possible hypothetical value, given the data. Moreover, the P‐value function simultaneously provides two‐sided confidence intervals for all possible alpha levels. An application to meta‐analysis, while suggested early, has not been widely established. We introduce the drapery plot that presents the P‐value function for all individual studies and pooled estimates in a meta‐analysis as curves and the prediction range for a single future study. We also present a scaled variant with the test statistic on the y‐axis. Both plots visualize the full information of a pairwise meta‐analysis. We see a drapery plot as a complementary figure to a forest plot. It may be even an alternative in meta‐analyses with many studies where forest plots tend to become very large and complex. [ABSTRACT FROM AUTHOR]

Standard generic inverse variance methods for the combination of single proportions are based on transformed proportions using the logit, arcsine, and Freeman‐Tukey double arcsine transformations. Generalized linear mixed models are another more elaborate approach. Irrespective of the approach, meta‐analysis results are typically back‐transformed to the original scale in order to ease interpretation. Whereas the back‐transformation of meta‐analysis results is straightforward for most transformations, this is not the case for the Freeman‐Tukey double arcsine transformation, albeit possible. In this case study with five studies, we demonstrate how seriously misleading the back‐transformation of the Freeman‐Tukey double arcsine transformation can be. We conclude that this transformation should only be used with special caution for the meta‐analysis of single proportions due to potential problems with the back‐transformation. Generalized linear mixed models seem to be a promising alternative. [ABSTRACT FROM AUTHOR]

The Handbook of Meta-Analysis, edited by three outstanding statisticians with contributions by the editors and many other meta-analysis experts, provides a detailed overview of the current state of meta-analysis on more than 500 pages.1 It consists of an introduction, three chapters on general themes and methods in meta-analysis, followed by 19 more special chapters, covering exact likelihood methods, Bayesian methods, meta-regression, multivariate meta-analysis including network meta-analysis, and individual participant data. Interestingly, in their recent RSM paper two authors of this chapter discuss all these methods.7 Chapter 19 (Meta-analysis of diagnostic test accuracy studies, MA-DT) is restricted to a binary diagnostic test (positive or negative) and a binary disease status (present or absent). Unfortunately, re-running the available R and Stata code was not possible for seven chapters: the data set (with aggregate data, not individual participant data) was missing for three chapters and running the code resulted in an error message for four chapters. [Extracted from the article]

Network meta-analysis has evolved into a core method for evidence synthesis in health care. In network meta-analysis, 3 or more treatments for a given medical condition are compared, based on a number of clinical studies, usually randomized controlled trials. Often, many different endpoints are investigated, related to different aspects of the patient's outcome, such as efficacy, safety, acceptability, or costs of a treatment. Different outcomes may lead to different rankings of the treatments. We use the existing theory of partially ordered sets and show how the relations between the treatments in a network meta-analysis can be illustrated by Hasse diagrams, that is, directed graphs showing the partial order relations, and by structured scatter plots and biplots. [ABSTRACT FROM AUTHOR]

In systematic reviews based on network meta-analysis, the network structure should be visualized. Network plots often have been drawn by hand using generic graphical software. A typical way of drawing networks, also implemented in statistical software for network meta-analysis, is a circular representation, often with many crossing lines. We use methods from graph theory in order to generate network plots in an automated way. We give a number of requirements for graph drawing and present an algorithm that fits prespecified ideal distances between the nodes representing the treatments. The method was implemented in the function netgraph of the R package netmeta and applied to a number of networks from the literature. We show that graph representations with a small number of crossing lines are often preferable to circular representations. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background: Network meta-analysis is used to compare three or more treatments for the same condition. Within a Bayesian framework, for each treatment the probability of being best, or, more general, the probability that it has a certain rank can be derived from the posterior distributions of all treatments. The treatments can then be ranked by the surface under the cumulative ranking curve (SUCRA). For comparing treatments in a network meta-analysis, we propose a frequentist analogue to SUCRA which we call P-score that works without resampling. Methods: P-scores are based solely on the point estimates and standard errors of the frequentist network meta-analysis estimates under normality assumption and can easily be calculated as means of one-sided p-values. They measure the mean extent of certainty that a treatment is better than the competing treatments. Results: Using case studies of network meta-analysis in diabetes and depression, we demonstrate that the numerical values of SUCRA and P-Score are nearly identical. Conclusions: Ranking treatments in frequentist network meta-analysis works without resampling. Like the SUCRA values, P-scores induce a ranking of all treatments that mostly follows that of the point estimates, but takes precision into account. However, neither SUCRA nor P-score offer a major advantage compared to looking at credible or confidence intervals. [ABSTRACT FROM AUTHOR]

Background Statisticians investigate new methods in simulations to evaluate their properties for future real data applications. Results are often presented in a number of figures, e.g., Trellis plots. We had conducted a simulation study on six statistical methods for estimating the treatment effect in binary outcome meta-analyses, where selection bias (e.g., publication bias) was suspected because of apparent funnel plot asymmetry. We varied five simulation parameters: true treatment effect, extent of selection, event proportion in control group, heterogeneity parameter, and number of studies in meta-analysis. In combination, this yielded a total number of 768 scenarios. To present all results using Trellis plots, 12 figures were needed. Methods Choosing bias as criterion of interest, we present a 'nested loop plot', a diagram type that aims to have all simulation results in one plot. The idea was to bring all scenarios into a lexicographical order and arrange them consecutively on the horizontal axis of a plot, whereas the treatment effect estimate is presented on the vertical axis. Results The plot illustrates how parameters simultaneously influenced the estimate. It can be combined with a Trellis plot in a so-called hybrid plot. Nested loop plots may also be applied to other criteria such as the variance of estimation. Conclusion The nested loop plot, similar to a time series graph, summarizes all information about the results of a simulation study with respect to a chosen criterion in one picture and provides a suitable alternative or an addition to Trellis plots. [ABSTRACT FROM AUTHOR]

Network meta-analysis is a statistical method combining information from randomised trials that compare two or more treatments for a given medical condition. Consistent treatment effects are estimated for all possible treatment comparisons. For estimation, weighted least squares regression that in a natural way generalises standard pairwise meta-analysis can be used. Typically, as part of the network, multi-arm studies are found. In a multi-arm study, observed pairwise comparisons are correlated, which must be accounted for. To this aim, two methods have been proposed, a standard regression approach and a new approach coming from graph theory and based on contrast-based data (Rücker 2012). In the standard approach, the dimension of the design matrix is appropriately reduced until it is invertible ('reduce dimension'). In the alternative approach, the weights of comparisons coming from multi-arm studies are appropriately reduced ('reduce weights'). As it was unclear, to date, how these approaches are related to each other, we give a mathematical proof that both approaches lead to identical estimates. The 'reduce weights' approach can be interpreted as the construction of a network of independent two-arm studies, which is basically equivalent to the given network with multi-arm studies. Thus, a simple random-effects model is obtained, with one additional parameter for a common heterogeneity variance. This is applied to a systematic review in depression. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Statistical heterogeneity and small-study effects are 2 major issues affecting the validity of meta-analysis. In this article, we introduce the concept of a limit meta-analysis, which leads to shrunken, empirical Bayes estimates of study effects after allowing for small-study effects. This in turn leads to 3 model-based adjusted pooled treatment-effect estimators and associated confidence intervals. We show how visualizing our estimators using the radial plot indicates how they can be calculated using existing software. The concept of limit meta-analysis also gives rise to a new measure of heterogeneity, termed G2, for heterogeneity that remains after small-study effects are accounted for. In a simulation study with binary data and small-study effects, we compared our proposed estimators with those currently used together with a recent proposal by Moreno and others. Our criteria were bias, mean squared error (MSE), variance, and coverage of 95% confidence intervals. Only the estimators arising from the limit meta-analysis produced approximately unbiased treatment-effect estimates in the presence of small-study effects, while the MSE was acceptably small, provided that the number of studies in the meta-analysis was not less than 10. These limit meta-analysis estimators were also relatively robust against heterogeneity and one of them had a relatively small coverage error. [ABSTRACT FROM AUTHOR]

Abstract: Objective: Meta-analysis yields a biased result if published studies represent a biased selection of the evidence. Copas proposed a selection model to assess the sensitivity of meta-analysis conclusions to possible selection bias. An alternative proposal is the trim-and-fill method. This article reports an empirical comparison of the two methods. Study Design and Setting: We took 157 meta-analyses with binary outcomes, analyzed each one using both methods, then performed an automated comparison of the results. We compared the treatment estimates, standard errors, associated P-values, and number of missing studies estimated by both methods. Results: Both methods give similar point estimates, but standard errors and P-values are systematically larger for the trim-and-fill method. Furthermore, P-values from the trim-and-fill method are typically larger than those from the usual random effects model when no selection bias is detected. By contrast, P-values from the Copas selection model and the usual random effects model are similar in this setting. The trim-and-fill method reports more missing studies than the Copas selection model, unless selection bias is detected when the position is reversed. Conclusions: The assumption that the most extreme studies are missing leads to excessively conservative inference in practice for the trim-and-fill method. The Copas selection model appears to be the preferable approach. [Copyright &y& Elsevier]

For clinical trials with binary endpoints there are a variety of effect measures, for example risk difference, risk ratio and odds ratio (OR). The choice of metric is not always straightforward and should reflect the clinical question. Additional issues arise if the event of interest is rare. In systematic reviews, trials with zero events in both arms are encountered and often excluded from the meta-analysis. The arcsine difference (AS) is a measure which is rarely considered in the medical literature. It appears to have considerable promise, because it handles zeros naturally, and its asymptotic variance does not depend on the event probability. This paper investigates the pros and cons of using the AS as a measure of intervention effect. We give a pictorial representation of its meaning and explore its properties in relation to other measures. Based on analytical calculation of the variance of the arcsine transformation, a more conservative variance estimate for the rare event setting is proposed. Motivated by a published meta-analysis in cardiac surgery, we examine the statistical properties of the various metrics in the rare event setting. We find the variance estimate of the AS to be more stable than that of the log-OR, even if events are rare. However, parameter estimation is biased if the groups are markedly unbalanced. Though, from a theoretical viewpoint, the AS is a natural choice, its practical use is likely to continue to be limited by its less direct interpretation. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In meta-analyses, it sometimes happens that smaller trials show different, often larger, treatment effects. One possible reason for such 'small study effects' is publication bias. This is said to occur when the chance of a smaller study being published is increased if it shows a stronger effect. Assuming no other small study effects, under the null hypothesis of no publication bias, there should be no association between effect size and effect precision (e.g. inverse standard error) among the trials in a meta-analysis. A number of tests for small study effects/publication bias have been developed. These use either a non-parametric test or a regression test for association between effect size and precision. However, when the outcome is binary, the effect is summarized by the log-risk ratio or log-odds ratio (log OR). Unfortunately, these measures are not independent of their estimated standard error. Consequently, established tests reject the null hypothesis too frequently. We propose new tests based on the arcsine transformation, which stabilizes the variance of binomial random variables. We report results of a simulation study under the Copas model (on the log OR scale) for publication bias, which evaluates tests so far proposed in the literature. This shows that: (i) the size of one of the new tests is comparable to those of the best existing tests, including those recently published; and (ii) among such tests it has slightly greater power, especially when the effect size is small and heterogeneity is present. Arcsine tests have additional advantages that they can include trials with zero events in both arms and that they can be very easily performed using the existing software for regression tests. Copyright © 2007 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A new test for the detection of publication bias in meta-analysis with sparse binary data is proposed. The test statistic is based on observed and expected cell frequencies, and the variance of the observed cell frequencies. These quantities are utilized in a rank correlation test. Type I error rate and power of the test are evaluated in simulations; results are compared to those of two other commonly used test procedures. Sample sizes were generated according to findings in a survey of eight German medical journals. Simulation results indicate that, in contrast to existing test procedures, the new test holds the prescribed significance level when data are sparse. However, the power of all tests is low in many situations of practical importance. Copyright © 2006 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background: In order to reduce systematic errors (such as language bias) and increase the precision of the summary treatment effect estimate, a comprehensive identification of randomised controlled trials (RCT), irrespective of publication language, is crucial in systematic reviews and meta-analyses. We identified trials in the German general health care literature. Methods: Eight German language general health care journals were searched for randomised controlled trials and analysed with respect to the number of published RCTs each year and the size of trials. Results: A total of 1618 trials were identified with a median total number of 43 patients per trial. Between 1970 and 2004 a small but constant rise in sample size from a median number of 30 to 60 patients per trial can be observed. The number of published trials was very low between 1948 and 1970, but increased between 1970 and 1986 to a maximum of 11.2 RCTs per journal and year. In the following time period a striking decline of the number of RCTs was observed. Between 1999 and 2001 only 0.8 RCTs per journal and year were published, in the next three years, the number of published trials increased to 1.7 RCTs per journal and year. Conclusion: German language general health care journals no longer have a role in the dissemination of trial results. The slight rise in the number of published RCTs in the last three years can be explained by a change of publication language from German to English of three of the analysed journals. [ABSTRACT FROM AUTHOR]

Quantifying the contributions, or weights, of comparisons or single studies to the estimates in a network meta‐analysis (NMA) is an active area of research. We extend this work to include the contributions of paths of evidence. We present a general framework, based on the path‐design matrix, that describes the problem of finding path contributions as a linear equation. The resulting solutions may have negative coefficients. We show that two known approaches, called shortestpath and randomwalk, are special solutions of this equation, and both meet an optimization criterion, as they minimize the sum of absolute path contributions. In general, there is an infinite set of solutions, which can be identified using the generalized inverse (Moore‐Penrose pseudoinverse). We consider two further special approaches. For large networks we find that shortestpath is superior with respect to run time and variability, compared to the other approaches, and is thus recommended in practice. The path‐weights framework also has the potential to answer more general research questions in NMA. [ABSTRACT FROM AUTHOR]

Objective: The aim of this paper was to provide a systematic review and update on the pharmacotherapy of social anxiety disorder (SAD), including the efficacy and tolerability of these agents, the ranking of interventions, and the grading of results by quality of evidence. Methods: The Common Mental Disorder Controlled Trial Register and two trial registries were searched for randomised controlled trials (RCTs) comparing any pharmacological intervention or placebo in the treatment of SAD. We performed a standard pairwise meta-analysis using a random effects model and carried out a network meta-analysis (NMA) using the statistical package, R. Quality of evidence was also assessed. Results: We included 67 RCTs in the review and 21 to 45 interventions in the NMA. Paroxetine was most effective in the reduction of symptom severity as compared to placebo. Superior response to treatment was also observed for paroxetine, brofaromine, bromazepam, clonazepam, escitalopram, fluvoxamine, phenelzine, and sertraline. Higher dropout rates were found for fluvoxamine. Brofaromine, escitalopram, fluvoxamine, paroxetine, pregabalin, sertraline, and venlafaxine performed worse in comparison to placebo for the outcome of dropouts due to adverse events. Olanzapine yielded a relatively high rank for treatment efficacy and buspirone the worse rank for dropouts due to any cause. Conclusion: The differences between drugs and placebo were small, apart from a significant reduction in symptom severity and response for paroxetine. We suggest paroxetine as a first-line treatment of SAD, with the consideration of future research on the drug olanzapine as well as brofaromine, bromazepam, clonazepam, escitalopram, fluvoxamine, phenelzine, and sertraline because we observed a response to treatment. [ABSTRACT FROM AUTHOR]

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

Abstract Background Depression independently predicts poor outcomes in heart failure (HF) patients, including increased mortality, morbidity and 30-day re-hospitalization. In this network meta-analysis, we compared different interventions designed to treat depression in HF. Materials and methods Electronic searches were conducted using Ovid MEDLINE, EMBASE, CINAHL, Web of Science, and PsycINFO up to November 2016. Included randomized clinical trials (RCTs) compared interventions (Exercise therapy (ET), cognitive behavioral therapy (CBT) or antidepressant (AD) medications) for depression in heart failure patients. The primary outcome was change in depressive symptoms based on validated measures of depression. Network meta-analysis based on random effects model estimating standardized mean difference (SMD) with 95% confidence interval (CI), compared the effects of the 3 classes of interventions with respect to usual care or placebo control conditions. Results A total of 21 RCTs (including 4563 HF patients) reporting the effects of treating depression in HF patients were included in the analysis. In comparison to placebo or usual standard of care, ET (SMD -0.38; 95% CI -0.54 to −0.22) and CBT (SMD -0.29; 95% CI -0.58 to −0.01) were associated with reduction in depressive symptoms whereas AD (SMD -0.16; 95% CI -0.44 to 0.11) was less effective. Conclusions This meta-analysis is suggestive of therapeutic benefit of ET and CBT in comparison to usual standard of care in treating depression in HF patients. However, comparison among the three interventions was not conclusive. Future randomized clinical trials are warranted to compare the therapeutic effects of ET, CBT and AD in such patients. Highlights • Question : What is the comparative efficacy of Antidepressants (AD), Exercise Training (ET), and Cognitive Behavioral Therapy (CBT) in treating depression in heart failure (HF) patients? • Findings : In this network meta-analysis, ET and CBT offers significant therapeutic benefits in comparison to usual standard of care in treating depression in HF patients. • Meaning : ET with/without CBT, appears to be a reasonable intervention to treat depression in HF. Large randomized clinical trials are warranted to compare the therapeutic effects of these three interventions in such patients. [ABSTRACT FROM AUTHOR]

Despite the development of new technologies and multimodal therapies, improving the prognosis of patients with UICC stage III right colon adenocarcinoma remains challenging. Several randomized controlled trials have shown the oncological non-inferiority of minimally invasive surgery compared to open surgery for colon cancer patients. However, for UICC stage III patients, carrying the highest risk for local recurrence and the worst survival, the evidence remains inconclusive. The aim of this systematic review and individual patient data meta-analysis is to improve the scarce evidence regarding minimally invasive surgery for this subgroup of patients. Data from adult patients with pathologically UICC stage III right adenocarcinoma of the colon will be included. The intervention to be assessed is the minimally invasive right hemicolectomy in comparison with the open procedure. The primary outcome will be the 5-year overall survival. Secondary outcomes will include further long-term outcomes, such as disease-free survival, short term, and histological outcomes. Only randomized controlled trials and quasi-randomized controlled clinical trials will be included. The literature search will be conducted in the following databases: PubMed, CINAHL, Cochrane Trials, ClinicalTrials.gov, and Web of Science. The review will be performed using the Cochrane methodology including GRADE tools. The findings of this meta-analysis will be important for choosing optimal treatment pathways and tailoring of surgical therapy in patients with locally advanced UICC stage III right colon cancer. [ABSTRACT FROM AUTHOR]

Systematic reviewers conducting pairwise meta-analyses sometimes encounter multi-arm studies. To include these studies, and to avoid a unit-of-analysis error, often two or more arms are combined or the control arm is split. In this tutorial, we present 5 different approaches that can be used. Particularly, we present a novel approach (method 4) that to the best of our knowledge has not been presented before. We demonstrate their application on 3 selected data sets, discuss their scope of application and their advantages and limitations, and give recommendations. [ABSTRACT FROM AUTHOR]

Background: Network meta-analysis (NMA) allows estimating and ranking the effects of several interventions for a clinical condition. Component network meta-analysis (CNMA) is an extension of NMA which considers the individual components of multicomponent interventions. CNMA allows to "reconnect" a disconnected network with common components in subnetworks. An additive CNMA assumes that component effects are additive. This assumption can be relaxed by including interaction terms in the CNMA. Methods: We evaluate a forward model selection strategy for component network meta-analysis to relax the additivity assumption that can be used in connected or disconnected networks. In addition, we describe a procedure to create disconnected networks in order to evaluate the properties of the model selection in connected and disconnected networks. We apply the methods to simulated data and a Cochrane review on interventions for postoperative nausea and vomiting in adults after general anaesthesia. Model performance is compared using average mean squared errors and coverage probabilities. Results: CNMA models provide good performance for connected networks and can be an alternative to standard NMA if additivity holds. For disconnected networks, we recommend to use additive CNMA only if strong clinical arguments for additivity exist. Conclusions: CNMA methods are feasible for connected networks but questionable for disconnected networks. [ABSTRACT FROM AUTHOR]

Abstract: Objective: Although using meta-analysis to combine evidence from a number of studies should reduce both bias and uncertainty, it is sometimes not the case, because published studies represent a biased selection of the evidence. Copas proposed a selection model to assess the sensitivity of meta-analysis conclusions to possible selection bias. However, this relatively complex model awaits both reliable software and an empirical evaluation. This article reports work addressing both these issues. Study Design and Setting: We took 157 meta-analyses with binary outcomes, analyzed each one using the Copas selection model, and evaluated each analysis using a prespecified protocol. The evaluation aimed to assess the usefulness of the Copas selection model to a typical Cochrane reviewer. Results: In approximately 80% of meta-analyses, the overall interpretation of the Copas selection model was clear, with better results among the 22 with evidence of selection bias. However, as with the “Trim and Fill” method, allowing for selection bias can result in smaller standard errors for the treatment estimate. Conclusion: When a reliable test for selection bias is significant, we recommend systematic reviewers to try the Copas selection model, although the results should be interpreted cautiously. [Copyright &y& Elsevier]

A widely used method in classic random-effects meta-analysis is the DerSimonian-Laird method. An alternative meta-analytical approach is the Hartung-Knapp method. This article reports results of an empirical comparison and a simulation study of these two methods and presents corresponding analytical results. For the empirical evaluation, we took 157 meta-analyses with binary outcomes, analysed each one using both methods and performed a comparison of the results based on treatment estimates, standard errors and associated P-values. In several simulation scenarios, we systematically evaluated coverage probabilities and confidence interval lengths. Generally, results are more conservative with the Hartung-Knapp method, giving wider confidence intervals and larger P-values for the overall treatment effect. However, in some meta-analyses with very homogeneous individual treatment results, the Hartung-Knapp method yields narrower confidence intervals and smaller P-values than the classic random-effects method, which in this situation, actually reduces to a fixed-effect meta-analysis. Therefore, it is recommended to conduct a sensitivity analysis based on the fixed-effect model instead of solely relying on the result of the Hartung-Knapp random-effects meta-analysis. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A considerable proportion of quantitative research remains unpublished once completed. Little research has documented non‐dissemination and dissemination bias in qualitative research. This study aimed to generate evidence on the extent of non‐dissemination in qualitative research. We followed a cohort of qualitative studies presented as conference abstracts to ascertain their subsequent publication status. We searched for subsequent full publication in MEDLINE, in the Cumulative Index to Nursing & Allied Health Literature and in Google Scholar. We matched abstracts to subsequent publications according to authors, method of data collection and phenomenon of interest. Fisher's exact test was calculated to examine associations between study characteristics and publication. Factors potentially associated with time to publication were evaluated with Cox regression analysis. For 91 of 270 included abstracts (33.70%; 95% CI 28.09%–39.68%), no full publication was identified. Factors that were found to be associated with subsequent full publication were oral presentation (OR 4.62; 95% CI 2.43–8.94) and university affiliation (OR 1.96; 95% CI 1.05–3.66). Compared to oral presentations, studies presented as posters took longer time to reach full publication (hazard ratio 0.35, 95% CI 0.21–0.58). This study shows that it was not possible to retrieve a full publication for over one‐third of abstracts. Our findings suggest that where this non‐dissemination is systematic, it may lead to distortions of the qualitative evidence‐base for decision‐making through dissemination bias. Our findings are congruent with those of other studies. Further research might investigate non‐dissemination of qualitative studies in other disciplines to consolidate our findings. [ABSTRACT FROM AUTHOR]

Network meta‐analysis can synthesize evidence from studies comparing multiple treatments for the same disease. Sometimes the treatments of a network are complex interventions, comprising several independent components in different combinations. A component network meta‐analysis (CNMA) can be used to analyze such data and can in principle disentangle the individual effect of each component. However, components may interact with each other, either synergistically or antagonistically. Deciding which interactions, if any, to include in a CNMA model may be difficult, especially for large networks with many components. In this article, we present two Bayesian CNMA models that can be used to identify prominent interactions between components. Our models utilize Bayesian variable selection methods, namely the stochastic search variable selection and the Bayesian LASSO, and can benefit from the inclusion of prior information about important interactions. Moreover, we extend these models to combine data from studies providing aggregate information and studies providing individual patient data (IPD). We illustrate our models in practice using three real datasets, from studies in panic disorder, depression, and multiple myeloma. Finally, we describe methods for developing web‐applications that can utilize results from an IPD‐CNMA, to allow for personalized estimates of relative treatment effects given a patient's characteristics. [ABSTRACT FROM AUTHOR]

Background: Tricyclic antidepressants (TCA) continue to be an important group of drugs, but it is unclear whether a dose-response relationship is supported by high-level evidence.Methods: Systematic review in the Cochrane Collaboration's Central Register of Controlled Trials (CENTRAL) of studies randomizing patients to at least two doses of one TCA, complemented by searches in Medline, Embase, and PsycInfo. In multilevel regression, we calculated the standardized mean difference (SMD) in antidepressant efficacy per mg TCA dose increase, and we analyzed drop-outs due to adverse events. Finally, we computed random effects meta-analyses of all dose comparisons investigated in a minimum of two studies.Results: Out of 5365 studies screened, we included 15 randomized trials on 24 comparisons of 14 different dose contrasts. We found a statistically non-significant positive effect of increasing the dose: 0.34 SMD with 100 mg/d dose increase ([-0.03; 0.70] p = 0.073). While several comparisons showed no clear signal of a dose gradient, 300 mg of imipramine/desipramine is statistically significantly superior to 150 mg (SMD: 0.80 [0.28; 1.33], p = 0.003, I2: 0%). Drop-outs increased with higher doses, albeit not statistically significantly: Odds ratio (OR) of 1.44 with 100 mg dose increase [0.54; 3.86]. Overall, risk of bias was high.Limitations: Limited number of studies with mainly high risk of bias.Conclusions: So far, data on a dose-response relationship in TCAs from direct dose comparisons are inconclusive. Clinically, escalation to high doses may be justified if side effects are bearable. [ABSTRACT FROM AUTHOR]

Using the Hartung–Knapp method and 95% prediction intervals (PIs) in random‐effects meta‐analyses is recommended by experts but rarely applied. Therefore, we aimed to reevaluate statistically significant meta‐analyses using the Hartung–Knapp method and 95% PIs. In this methodological study, three databases were searched from January 2010 to July 2019. We included systematic reviews reporting a statistically significant meta‐analysis of at least four randomized controlled trials in advanced cancer patients using either a fixed‐effect or random‐effects model. We investigated the impact of switching from fixed‐effect to random‐effects meta‐analysis and of using the recommended Hartung–Knapp method in random‐effects meta‐analyses. Furthermore, we calculated 95% PIs for all included meta‐analyses. We identified 6234 hits, of which 261 statistically significant meta‐analyses were included. Our recalculations of these 261 meta‐analyses produced statistically significant results in 132 of 138 fixed‐effect and 114 of 123 random‐effects meta‐analyses. When switching to a random‐effects model, 19 of 132 fixed‐effect meta‐analyses (14.4%) were no longer statistically significant. Using the Hartung–Knapp method in random‐effects meta‐analyses resulted in 34 of 114 nonsignificant meta‐analyses (29.8%). In the full sample (N = 261), the null effect was included by the 95% PI in 195 (74.7%) and the opposite effect (e.g., hazard ratio 0.5, opposite effect 2) in 98 meta‐analyses (37.5%). Using the Hartung–Knapp method and PIs substantially influenced the interpretation of many published, statistically significant meta‐analyses. We strongly encourage researchers to check if using the Hartung–Knapp method and reporting 95% PIs is appropriate in random‐effects meta‐analyses. [ABSTRACT FROM AUTHOR]

Summary Evidence synthesis, both qualitatively and quantitatively through meta-analysis, is central to the development of evidence-based medicine. Unfortunately, meta-analysis is often complicated by the suspicion that the available studies represent a biased subset of the evidence, possibly due to publication bias or other systematically different effects in small studies. A number of statistical methods have been proposed to address this, among which the trim-and-fill method and the Copas selection model are two of the most widely discussed. However, both methods have drawbacks: the trim-and-fill method is based on strong assumptions about the symmetry of the funnel plot; the Copas selection model is less accessible to systematic reviewers, and sometimes encounters estimation problems. In this article, we adopt a logistic selection model, and show how treatment effects can be rapidly estimated via multiple imputation. Specifically, we impute studies under a missing at random assumption, and then reweight to obtain estimates under nonrandom selection. Our proposal is computationally straightforward. It allows users to increase selection while monitoring the extent of remaining funnel plot asymmetry, and also visualize the results using the funnel plot. We illustrate our approach using a small meta-analysis of benign prostatic hyperplasia. [ABSTRACT FROM AUTHOR]

Anemia associated with cancer and cancer therapy is a common and important issue in the treatment of patients with malignant disease. Conventionally, blood transfusions are used to treat severe cancer-related anemia. Short- and long-acting preparations of recombinant human erythropoiesis-stimulating agents (ESAs) offer an alternative treatment option. Multiple studies and subsequent meta-analyses have demonstrated that ESA treatment increases hemoglobin levels and reduces the likelihood of transfusion for a proportion of treated patients. However, studies that attempted to evaluate whether ESAs improve tumor response and survival have generated conflicting evidence. Results of smaller trials reporting improved survival outcomes were contradicted by large randomized controlled trials that reported more deaths in patients receiving ESAs. In addition, there is strong evidence that cancer patients receiving ESAs have an increased risk of thromboembolic and cardiovascular events. We herein review the main meta-analyses published in the field, their strengths and weaknesses, their contribution to patient management and future perspectives for systematic reviews. Copyright © 2010 S. Karger AG, Basel [ABSTRACT FROM AUTHOR]

We congratulate the authors on their extension of the univariate Egger test for publication bias to the multivariate setting. On the other hand, outcome reporting bias means that, perhaps in response to the trial results, the authors switch the primary and secondary outcomes around. Second the authors' approach is essentially a score test variant of performing multiple Egger tests in one step using Generalized Estimating Equations, assuming independence across outcomes for estimation, and using robust standard errors (clustered on studies). [Extracted from the article]

Background: Anemia associated with cancer and cancer therapy is an important clinical and economic factor in the treatment of malignant diseases. Methods: We conducted a systematic literature review to assess the efficacy of erythropoietin to prevent or treat anemia in cancer patients with regard to red blood cell transfusions, hematologic response, adverse events, and overall survival. We searched the Cochrane Library, Medline, EMBASE, and other databases for relevant articles published from January 1985 to December 2001. We included all randomized controlled trials that compared the use of recombinant human erythropoietin (plus transfusion, if needed) with no erythropoietin treatment (plus transfusion, if needed). Relative risks (RRs) and 95% confidence intervals (CIs) were calculated under a fixed-effects model. Clinical and statistical heterogeneity were examined with sensitivity analyses and meta-regression. Statistical tests for effect estimates were two-sided. Results: We identified 27 trials involving 3287 adult patients. Patients treated with erythropoietin had a lower relative risk of having a blood transfusion than untreated patients (RR = 0.67, 95% CI = 0.62 to 0.73). Erythropoietin-treated patients with baseline hemoglobin levels lower than 10 g/dL were more likely to have a hematologic response than untreated patients (RR = 3.60, 95% CI = 3.07 to 4.23). The relative risk for thromboembolic complications after erythropoietin treatment was not statistically significantly increased (RR = 1.58, 95% CI = 0.94 to 2.66) compared with that of untreated patients. There is suggestive but inconclusive evidence that erythropoietin may improve overall survival (adjusted data: hazard ratio [HR] = 0.81, 95% CI = 0.67 to 0.99; unadjusted data: HR = 0.84, 95% CI = 0.69 to 1.02). Conclusions: Erythropoietin treatment may reduce the risk for blood transfusions and improve hematologic response in cancer patients. However, our favorable survival outcome is in contrast to two large (N = 351 and 939) recently published randomized controlled trials in which erythropoietin-treated patients had statistically significantly worse survival than untreated patients. Possible reasons for the disparity with our results include differences in study population and design, higher target hemoglobin levels and higher risk of thromboembolic complications, and concerns that erythropoietin may stimulate tumor growth. [ABSTRACT FROM AUTHOR]

Background: Network meta-analysis estimates all relative effects between competing treatments and can produce a treatment hierarchy from the most to the least desirable option according to a health outcome. While about half of the published network meta-analyses present such a hierarchy, it is rarely the case that it is related to a clinically relevant decision question.Methods: We first define treatment hierarchy and treatment ranking in a network meta-analysis and suggest a simulation method to estimate the probability of each possible hierarchy to occur. We then propose a stepwise approach to express clinically relevant decision questions as hierarchy questions and quantify the uncertainty of the criteria that constitute them. The steps of the approach are summarized as follows: a) a question of clinical relevance is defined, b) the hierarchies that satisfy the defined question are collected and c) the frequencies of the respective hierarchies are added; the resulted sum expresses the certainty of the defined set of criteria to hold. We then show how the frequencies of all possible hierarchies relate to common ranking metrics.Results: We exemplify the method and its implementation using two networks. The first is a network of four treatments for chronic obstructive pulmonary disease where the most probable hierarchy has a frequency of 28%. The second is a network of 18 antidepressants, among which Vortioxetine, Bupropion and Escitalopram occupy the first three ranks with frequency 19%.Conclusions: The developed method offers a generalised approach of producing treatment hierarchies in network meta-analysis, which moves towards attaching treatment ranking to a clear decision question, relevant to all or a subset of competing treatments. [ABSTRACT FROM AUTHOR]

Summary. The granulopoiesis-stimulating factors, granulocyte colony-stimulating factor (G-CSF) and granulocyte–macrophage colony-stimulating factor (GM-CSF), are used to prevent neutropenia and febrile neutropenia in patients with malignant lymphoma. The question as to whether G-CSF/GM-CSF improves dose-intensity, tumour response and overall survival (OS) has not yet been answered. As the results from single studies are inconclusive, a systematic review was performed to determine the effectiveness of G-CSF/GM-CSF. Randomized controlled trials comparing prophylaxis with G-CSF/GM-CSF versus no prophylaxis in adult patients with malignant lymphoma undergoing conventional chemotherapy were included. Medical databases (Cochrane Library, Medline, Embase) and conference proceedings were searched. All authors were contacted to obtain missing data. We included 11 studies making a total of 1434 patients. Compared with no prophylaxis, G-CSF/GM-CSF significantly reduced the relative risk (RR) for neutropenia {RR 0·64 [95% confidence interval (CI) 0·55–0·75]} febrile neutropenia (RR 0·74 [95% CI 0·62–0·89]) and infection (RR 0·74 [95% CI 0·64–0·85]). G-CSF/GM-CSF did not decrease infection-related mortality (RR 2·07 [95% CI 0·81–5·34]), improve complete remission (CR) (RR 1·06 [95% CI 0·96–1·16]) or OS (HR 0·98 [95% CI 0·81–1·18]). In conclusion, G-CSF/GM-CSF given during conventional chemotherapy in malignant lymphoma patients reduced the RR of neutropenia, febrile neutropenia and infection. However, there is no evidence that G-CSF/GM-CSF improved CR and OS in this clinical setting. [ABSTRACT FROM AUTHOR]

In a quantitative synthesis of studies via meta‐analysis, it is possible that some studies provide a markedly different relative treatment effect or have a large impact on the summary estimate and/or heterogeneity. Extreme study effects (outliers) can be detected visually with forest/funnel plots and by using statistical outlying detection methods. A forward search (FS) algorithm is a common outlying diagnostic tool recently extended to meta‐analysis. FS starts by fitting the assumed model to a subset of the data which is gradually incremented by adding the remaining studies according to their closeness to the postulated data‐generating model. At each step of the algorithm, parameter estimates, measures of fit (residuals, likelihood contributions), and test statistics are being monitored and their sharp changes are used as an indication for outliers. In this article, we extend the FS algorithm to network meta‐analysis (NMA). In NMA, visualization of outliers is more challenging due to the multivariate nature of the data and the fact that studies contribute both directly and indirectly to the network estimates. Outliers are expected to contribute not only to heterogeneity but also to inconsistency, compromising the NMA results. The FS algorithm was applied to real and artificial networks of interventions that include outliers. We developed an R package (NMAoutlier) to allow replication and dissemination of the proposed method. We conclude that the FS algorithm is a visual diagnostic tool that helps to identify studies that are a potential source of heterogeneity and inconsistency. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Back pain is a widespread health problem that accounts for substantial disability and high costs. The workplace is considered to critically affect the occurrence and persistence of back pain and therefore offers an important opportunity for preventive interventions. Various work-related intervention strategies including both single- and multicomponent interventions have been developed and evaluated so far. To determine their effectiveness, a method of analysis is needed that particularly meets the challenges of the multidimensionality and diversity of these interventions. This planned systematic review and network meta-analysis aims to compare the effects of different work-related interventions for preventing non-specific back pain in people within a formal employment-related context. Methods: We will search the following databases: CENTRAL, MEDLINE, Web of Science, CINAHL, PsycINFO, PEDro, SPORTDiscus, and Academic Search Premier from their inception onwards, as well as additional sources. Randomized controlled trials (RCTs) and cluster-RCTs will be considered if they (1) include people within a formal employment-related context, (2) include people without back pain or mixed samples (i.e., people with and without back pain), (3) compare one or more work-related preventive intervention(s) to a control condition, and (4) assess non-specific back pain (incidence or/and pain intensity), ability to work (numbers of participants or/and numbers of days absent from work), intervention-related adverse events or/and self-reported satisfaction with the intervention. Random-effects pairwise meta-analyses and frequentist network meta-analyses will be conducted where appropriate. We will calculate summary effect sizes for each comparison of interventions and rank interventions according to their P scores. If feasible, we will conduct additional component network meta-analyses. We plan to conduct subgroup analyses for job exposure, intervention duration, baseline back pain, different localizations of back pain, and gender. Risk of bias will be assessed using RoB 2 and the certainty of the evidence will be rated using the GRADE approach. Discussion: This systematic review aims to identify work-related intervention strategies as well as components within work-related interventions that are effective for preventing back pain. We expect the results to provide guidance for selecting the most promising interventions and foster the purposeful use of resources. Additionally, they may inform the development and implementation of work-related interventions as well as the design of future research in this field. Trial registration: PROSPERO CRD42021232469 [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the meta-analysis conducted by William G. Cochran on U.S. Surgeon General Luther L. Terry's January 11, 1964 report on smoking and health.

CONTEXT: Migraine is a common neurologic disorder in children and adolescents. However, a comparison of multiple nonpharmacological treatments is lacking. OBJECTIVE: To examine whether nonpharmacological treatments are more effective than waiting list and whether there are differences between interventions regarding efficacy. DATA SOURCES: Systematic review and network meta-analysis of studies in Medline, Cochrane, Embase, and PsycINFO published through August 5, 2019. STUDY SELECTION: Randomized controlled trials of nonpharmacological treatments in children and adolescents diagnosed with episodic migraine. DATA EXTRACTION: Effect sizes, calculated as standardized mean differences (SMDs) for the primary outcome efficacy, were assessed in a random-effects model. RESULTS: Twelve studies (N = 576) were included. When interventions were classified into groups on the basis of similarity of treatment components, self-administered treatments, biofeedback, relaxation, psychological treatments, and psychological placebos were significantly more effective than waiting list with effect sizes ranging between SMD = 1.14 (95% confidence interval, 0.09 to 2.19) for long-term psychological placebos to SMD = 1.44 (95% confidence interval, 0.26 to 2.62) for short-term self-administered treatments. However, when all interventions were examined individually (ie, 1 node per intervention), none were significantly more effective compared with waiting list, mainly because of lack of statistical power. LIMITATIONS: Because of our focus on pediatric migraine, only a small number of studies could be included. CONCLUSIONS: Our findings reveal that components of nonpharmacological interventions are effective in treating pediatric migraine. Some effects have to be interpreted carefully because they are based on small studies. Future researchers should identify factors associated with individual responses in large, multicentered studies. [ABSTRACT FROM AUTHOR]

Background: Mental burden due to the SARS-CoV-2 pandemic has been widely reported for the general public and specific risk groups like healthcare workers and different patient populations. We aimed to assess its impact on mental health during the early phase by comparing pandemic with prepandemic data and to identify potential risk and protective factors.Methods: For this systematic review and meta-analyses, we systematically searched PubMed, PsycINFO, and Web of Science from January 1, 2019 to May 29, 2020, and screened reference lists of included studies. In addition, we searched PubMed and PsycINFO for prepandemic comparative data. Survey studies assessing mental burden by the SARS-CoV-2 pandemic in the general population, healthcare workers, or any patients (eg, COVID-19 patients), with a broad range of eligible mental health outcomes, and matching studies evaluating prepandemic comparative data in the same population (if available) were included. We used multilevel meta-analyses for main, subgroup, and sensitivity analyses, focusing on (perceived) stress, symptoms of anxiety and depression, and sleep-related symptoms as primary outcomes.Results: Of 2429 records retrieved, 104 were included in the review (n = 208,261 participants), 43 in the meta-analysis (n = 71,613 participants). While symptoms of anxiety (standardized mean difference [SMD] 0.40; 95% CI 0.15-0.65) and depression (SMD 0.67; 95% CI 0.07-1.27) were increased in the general population during the early phase of the pandemic compared with prepandemic conditions, mental burden was not increased in patients as well as healthcare workers, irrespective of COVID-19 patient contact. Specific outcome measures (eg, Patient Health Questionnaire) and older comparative data (published ≥5 years ago) were associated with increased mental burden. Across the three population groups, existing mental disorders, female sex, and concerns about getting infected were repeatedly reported as risk factors, while older age, a good economic situation, and education were protective.Conclusions: This meta-analysis paints a more differentiated picture of the mental health consequences in pandemic situations than previous reviews. High-quality, representative surveys, high granular longitudinal studies, and more research on protective factors are required to better understand the psychological impacts of the SARS-CoV-2 pandemic and to help design effective preventive measures and interventions that are tailored to the needs of specific population groups. [ABSTRACT FROM AUTHOR]

Many healthcare interventions are complex, consisting of multiple, possibly interacting, components. Several methodological articles addressing complex interventions in the meta-analytical context have been published. We hereby provide an overview of methods used to evaluate the effects of complex interventions with meta-analytical models. We summarized the methodology, highlighted new developments, and described the benefits, drawbacks, and potential challenges of each identified method. We expect meta-analytical methods focusing on components of several multicomponent interventions to become increasingly popular due to recently developed, easy-to-use, software tools that can be used to conduct the relevant analyses. The different meta-analytical methods are illustrated through two examples comparing psychotherapies for panic disorder. [ABSTRACT FROM AUTHOR]

Introduction: Achieving tuberculosis (TB) elimination in low TB incidence countries requires identification and treatment of individuals at risk for latent TB infection (LTBI). Persons travelling to high TB incidence countries are potentially at risk for TB exposure. This systematic review and meta-analysis estimates incident LTBI and active TB among individuals travelling from low to higher TB incidence countries.Methods: Five electronic databases were searched from inception to 18 February 2020. We identified incident LTBI and active TB among individuals travelling from low (<10 cases/100 000 population) to intermediate (10-100/100 000) or high (>100/100 000) TB incidence countries. We conducted a meta-analysis and meta-regression using a random effects model of log-transformed proportions (cumulative incidence). Subgroup analyses investigated the impact of travel duration, travel purpose and TB incidence in the destination country.Results: Our search identified 799 studies, 120 underwent full-text review, and 10 studies were included. These studies included 1 154 673 travellers observed between 1994 and 2013, comprising 443 health care workers (HCW), 1 068 636 military personnel and 85 594 general travellers/volunteers. We did not identify any studies that estimated incidence of LTBI or active TB among people travelling to visit friends and relatives (VFRs). The overall cumulative incidence of LTBI was 2.3%, with considerable heterogeneity. Among individuals travelling for a mean/median of up to 6 months, HCWs had the highest cumulative incidence of LTBI (4.3%), whereas the risk was lower for military (2.5%) and general travellers/volunteers (1.6%). Meta-regression did not identify a difference in incident LTBI based on travel duration and TB incidence in the destination country. Five studies reported cases of active TB, with an overall pooled estimate of 120.7 cases per 100 000 travellers.Conclusions: We found that travelling HCWs were at highest risk of developing LTBI. Individual risk activities and travel purpose were most associated with risk of TB infection acquired during travel. [ABSTRACT FROM AUTHOR]

Objective: Open angle glaucoma is one of the most common causes of blindness in industrialised nations. Treatments to lower ocular pressure are widely used in glaucoma prevention and treatment, despite conflicting evidence. Design: We performed meta-analyses to reassess the effectiveness of pressure lowering treatment to delay the development of glaucoma in ocular hypertension, as well as progression of manifest open angle glaucoma. Data sources: Medline, Embase, and the Cochrane Library. Selection of studies: Eligible studies were randomised controlled trials with a concurrent untreated control group and information on time to glaucomatous changes to visual field and optic disc. Trial reports were reviewed independently by two investigators in an unblinded standardised manner. Results: Meta-analysis of trials in ocular hypertension showed a significant preventive effect of reducing intraocular pressure on progression to glaucoma (hazard ratio 0.56, 95% confidence interval 0.39 to 0.81, P = 0.01; number needed to treat 12). Pooled data of studies in manifest glaucoma showed a significant delay of visual field deterioration (0.65, 0.49 to 0.87, P = 0.003; NNT = 7), with subgroup analysis showing a larger effect in patients with raised pressure and a reduced effect in normal tension glaucoma (subgroup comparison: not significant). Conclusions: Lowering intraocular pressure in patients with ocular hypertension or manifest glaucoma is beneficial in reducing the risk of visual field loss in the long term. [ABSTRACT FROM AUTHOR]

Background: In pairwise meta-analysis, the contribution of each study to the pooled estimate is given by its weight, which is based on the inverse variance of the estimate from that study. For network meta-analysis (NMA), the contribution of direct (and indirect) evidence is easily obtained from the diagonal elements of a hat matrix. It is, however, not fully clear how to generalize this to the percentage contribution of each study to a NMA estimate. Methods: We define the importance of each study for a NMA estimate by the reduction of the estimate's variance when adding the given study to the others. An equivalent interpretation is the relative loss in precision when the study is left out. Importances are values between 0 and 1. An importance of 1 means that the study is an essential link of the pathway in the network connecting one of the treatments with another. Results: Importances can be defined for two-stage and one-stage NMA. These numbers in general do not add to one and thus cannot be interpreted as 'percentage contributions'. After briefly discussing other available approaches, we question whether it is possible to obtain unique percentage contributions for NMA. Conclusions: Importances generalize the concept of weights in pairwise meta-analysis in a natural way. Moreover, they are uniquely defined, easily calculated, and have an intuitive interpretation. We give some real examples for illustration. [ABSTRACT FROM AUTHOR]