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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 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 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.

Network meta-analysis compares different interventions for the same condition, by combining direct and indirect evidence derived from all eligible studies. Network metaanalysis has been increasingly used by applied scientists and it is a major research topic for methodologists. This article describes the R package netmeta, which adopts frequentist methods to fit network meta-analysis models. We provide a roadmap to perform network meta-analysis, along with an overview of the main functions of the package. We present three worked examples considering different types of outcomes and different data formats to facilitate researchers aiming to conduct network meta-analysis with netmeta.

Purpose: Chemotherapy, when added to radiotherapy, improves survival in locally advanced nasopharyngeal carcinoma (NPC). This article presents the second update of the Meta-Analysis of Chemotherapy in NPC. Methods: Published or unpublished randomized trials assessing radiotherapy (±a second chemotherapy timing) with/without chemotherapy in non-metastatic NPC patients were identified. Updated data were sought for studies included in the previous rounds of the meta-analysis. The primary endpoint was overall survival. All trials were analyzed following the intent-to-treat principle using a fixed-effects model. Treatments were classified in five subsets according to chemotherapy timing. The statistical analysis plan was pre-specified. Results: Eighteen new trials were identified. Individual patient data were available for seven. In total, the meta-analysis now included 26 trials and 7,080 patients. The addition of chemotherapy reduced the risk of death, with a hazard ratio (HR) of 0.79 (95% confidence interval (CI) [0.73; 0.85]), and an absolute survival increase at 5 and 10 years of 6.1% [+3.9; +8.3] and + 8.4% [+5.7; +11.1], respectively. The largest effect was observed for concomitant + adjuvant, induction (with concomitant in both arms) and concomitant chemotherapy, with respective HR [95%CI] of 0.68 [0.59; 0.79] (absolute survival increase at 5 years: 12.3% (7.0%;17.6%)), 0.73 [0.63; 0.86] (6.0% (2.5%;9.5%)) and 0.81 [0.70; 0.92] (5.2% (0.8%;9.6%)). The benefit of chemotherapy was also demonstrated by improvement in progression-free survival, cancer mortality, locoregional control and distant control. There was a significant interaction between patient age and chemotherapy effect. Conclusion: This updated meta-analysis confirms the benefit of concomitant chemotherapy and concomitant + adjuvant chemotherapy, and suggests that addition of induction or adjuvant chemotherapy to concomitant chemotherapy improves tumor control and survival. The benefit of chemotherapy decreases with increasing patient age.

Abstract 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 Background Healthcare decisions are ideally based on clinical trial results, published in study registries, as journal articles or summarized in secondary research articles. In this research project, we investigated the impact of academically and commercially sponsored clinical trials on medical practice by measuring the proportion of trials published and cited by systematic reviews and clinical guidelines. Methods We examined 691 multicenter, randomized controlled trials that started in 2005 or later and were completed by the end of 2016. To determine whether sponsorship/funding and place of conduct influence a trial’s impact, we created four sub-cohorts of investigator initiated trials (IITs) and industry sponsored trials (ISTs): 120 IITs and 171 ISTs with German contribution compared to 200 IITs and 200 ISTs without German contribution. We balanced the groups for study phase and place of conduct. German IITs were funded by the German Research Foundation (DFG), the Federal Ministry of Education and Research (BMBF), or by another non-commercial research organization. All other trials were drawn from the German Clinical Trials Register or ClinicalTrials.gov. We investigated, to what extent study characteristics were associated with publication and impact using multivariable logistic regressions. Results For 80% of the 691 trials, results were published as result articles in a medical journal and/or study registry, 52% were cited by a systematic review, and 26% reached impact in a clinical guideline. Drug trials and larger trials were associated with a higher probability to be published and to have an impact than non-drug trials and smaller trials. Results of IITs were more often published as a journal article while results of ISTs were more often published in study registries. International ISTs less often gained impact by inclusion in systematic reviews or guidelines than IITs. Conclusion An encouraging high proportion of the clinical trials were published, and a considerable proportion gained impact on clinical practice. However, there is still room for improvement. For publishing study results, study registries have become an alternative or complement to journal articles, especially for ISTs. IITs funded by governmental bodies in Germany reached an impact that is comparable to international IITs and ISTs.

Abstract Background Overweight and obesity in children and adolescents are major public health challenges affecting quality of life and representing important risk factors in the development of non-communicable diseases. School environments provide great possibilities for the primary prevention of overweight and obesity and different school-based nutrition interventions are available. However, existing research on school-based nutrition interventions has important limitations and no network meta-analysis (NMA) has been performed yet to compare all available interventions. Therefore, the present research project aims to investigate the impact of different nutrition interventions in the school setting by comparing and ranking them using NMA methodology. Methods/design A systematic literature search will be performed in 11 electronic databases (PubMed, the Cochrane Library, Web of Science, ERIC, PsycINFO, CAB Abstracts, Campbell Library, BiblioMap EPPI, Australian Education Index, Joanna Briggs Institute Evidence-Based Practice Database and Practice-based Evidence in Nutrition Database). Parallel or cluster randomized controlled trials (RCTs) meeting the following criteria will be included: (1) generally healthy school students aged 4–18 years, (2) school-based intervention with ≥ 1 nutrition component, and (3) assessed anthropometric (overweight/obesity risk, body weight change, weight Z-score, [standardized] body mass index, body fat, waist circumference) and/or diet-quality measures (daily intake of fruits and vegetables, fat, and sugar-sweetened beverages). Random effects pairwise and NMA will be performed for these outcomes and surface under the cumulative ranking curve (SUCRA) estimated (P-score). Where possible, component NMA (CNMA) will be used additionally. Subgroup analyses are carried out for intervention duration, gender, age of school students, socioeconomic status, and geographical location, and sensitivity analyses by excluding high risk of bias RCTs. Discussion This systematic review and NMA will be the first to both directly and indirectly compare and rank different school-based nutrition interventions for the primary prevention of overweight and obesity in childhood and adolescence. Our analyses will provide important insights about the effects of the different interventions and show which are the most promising. The results of our study can help inform the design of new studies and will be of value to anyone interested in developing successful, evidence-based nutrition interventions in school settings. Systematic review registration PROSPERO: CRD42020220451 .

Breast cancer (BC) represents the most common type of cancer in females worldwide. Endocrine therapy evolved as one of the main concepts in treatment of hormone-receptor positive BC. Current research focuses on the elucidation of tumour resistance mechanisms against endocrine therapy. In a translational in vitro approach, potential regulatory effects of clinically implemented BC anti-oestrogens on ERα, its coactivators DDX5, DDX17 and other DEADbox proteins as well as on the proliferation markers cyclin D1 and Ki67 were investigated on both the RNA and protein level. BC in vitro models for hormone-receptor positive (MCF-7, T-47D) and hormone-receptor negative cells (BT-20) were subjected to endocrine therapy. Anti-oestrogen-dependent expression regulation of target genes on the transcriptional and translational level was quantified an d statistically assessed. Endocrine therapy decreases the expression levels of Ki67, cyclin D1 and ERα in hormone-receptor positive cells. In the hormone-receptor negative cells, the three parameters remained stable after endocrine therapy. Endoxifen triggers a downregulation of DDX5 and DDX23 in MCF-7 cells. Fulvestrant treatment downregulates the expression levels of all investigated DEADbox proteins in MCF-7 cells. In T-47D cells, endoxifen and fulvestrant lead to a decrease of all target gene expression levels. Interestingly, endocrine therapy affects DEADbox RNA expression levels in BT-20 cells, too. Howev er, this result could only be confirmed for DDX1, immunocytologically. The investigated DEA Dbox proteins appear to correlate with the oestrogen-dependent tumourigenesis in hormone-receptor positive BC and show expression alterations after endocrine treatment.

Abstract 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 Background Length of stay evaluations are very common to determine the burden of nosocomial infections. However, there exist fundamentally different methods to quantify the prolonged length of stay associated with nosocomial infections. Previous methodological studies emphasized the need to account for the timing of infection in order to differentiate the length of stay before and after the infection. Methods We derive four different approaches in a simple multi-state framework, display their mathematical relationships in a multiplicative as well as additive way and apply them to a real cohort study (n=756 German intensive-care unit patients of whom 124 patients acquired a nosocomial infection). Results The first approach ignores the timing of infection and quantifies the difference of eventually infected and eventually uninfected; it is 12.31 days in the real data. The second approach compares the average sojourn time with infection with the average sojourn time of being hypothetically uninfected; it is 2.12 days. The third one compares the average length of stay of a population in a world with nosocomial infections with a population in a hypothetical world without nosocomial infections; it is 0.35 days. Finally, approach four compares the mean residual length of stay between currently infected and uninfected patients on a daily basis; the difference is 1.77 days per infected patient. Conclusions The first approach should be avoided because it compares the eventually infected with the eventually uninfected, but has no prospective interpretation. The other approaches differ in their interpretation but are suitable because they explicitly distinguish between the pre- and post-time of the nosocomial infection.

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.

Introduction Obesity is highly prevalent in older adults aged 65 years or older. Different lifestyle interventions (diet, exercise, self-management) are available but benefits and harms have not been fully quantified comparing all available health promotion interventions. Special consideration must be given to functional outcomes and possible adverse effects (loss of muscle and bone mass, hypoglycaemia) of weight loss interventions in this age group. The objective of this study is to synthesise the evidence regarding the effects of different types and modalities of lifestyle interventions, or their combinations, on physical function and obesity-related outcomes such as body composition in older adults with obesity.Methods and analyses Six databases (Medline, Embase, Cochrane Central Register of Controlled Trials, Cumulated Index to Nursing and Allied Health Literature (CINAHL), Psychinfo and Web of Science) and two trial registries (Clinicaltrials.gov and the WHO International Clinical Trials Registry Platform) will be searched for randomised controlled trials of lifestyle interventions in older adults with obesity. Screening (title/abstract and full-text) and data extraction of references as well as assessment of risk of bias and rating of the certainty of evidence (Grading of Recommendations, Assessment, Development and Evaluation for network meta-analyses) will be performed by two reviewers independently. Frequentist random-effects network meta-analyses will be conducted to determine the pooled effects from each intervention.Ethics and dissemination We will submit our findings to peer-reviewed journals and present at national and international conferences as well as in scientific medical societies. Patient-targeted dissemination will involve local and national advocate groups.PROSPERO registration number CRD42019147286.

BackgroundResults of medical research should be made publicly available in a timely manner to enable patients and health professionals to make informed decisions about health issues. We aimed to apply a multi-state model to analyze the overall time needed to publish study results, and to examine predictors of the timing of transitions within the research process from study initiation through completion/discontinuation to eventual publication.MethodsUsing a newly developed multi-state model approach, we analysed the effect of different study-related factors on each of the transitions from study approval to eventual publication, using a data set of clinical studies approved by a German research ethics committee between 2000 and 2002.ResultsOf 917 approved studies, 806 were included in our analyses. About half of the clinical studies which began were subsequently published as full articles, and the median time from study approval to publication was 10 years. Differences across model states were apparent; several factors were predictive of the transition from study approval to completion, while funding source and collaboration were predictive of the transition from completion to publication.ConclusionsThe proposed multi-state model approach permits a more comprehensive analysis of time to publication than a simple examination of the transition from approval to publication, and thus the findings represent an advance on previous studies of this aspect of the research process.

The objective of this study is to describe the general approaches to network meta-analysis that are available for quantitative data synthesis using R software. We conducted a network meta-analysis using two approaches: Bayesian and frequentist methods. The corresponding R packages were “gemtc” for the Bayesian approach and “netmeta” for the frequentist approach. In estimating a network meta-analysis model using a Bayesian framework, the “rjags” package is a common tool. “rjags” implements Markov chain Monte Carlo simulation with a graphical output. The estimated overall effect sizes, test for heterogeneity, moderator effects, and publication bias were reported using R software. The authors focus on two flexible models, Bayesian and frequentist, to determine overall effect sizes in network meta-analysis. This study focused on the practical methods of network meta-analysis rather than theoretical concepts, making the material easy to understand for Korean researchers who did not major in statistics. The authors hope that this study will help many Korean researchers to perform network meta-analyses and conduct related research more easily with R software.

In network meta-analysis, it is important to assess the influence of the limitations or other characteristics of individual studies on the estimates obtained from the network. The proportion contribution matrix, which shows how much each direct treatment effect contributes to each treatment effect estimate from network meta-analysis, is crucial in this context. We use ideas from graph theory to derive the proportion that is contributed by each direct treatment effect. We start with the ‘projection’ matrix in a two-step network meta-analysis model, called the H matrix, which is analogous to the hat matrix in a linear regression model. We develop a method to translate H entries to proportion contributions based on the observation that the rows of H can be interpreted as flow networks, where a stream is defined as the composition of a path and its associated flow. We present an algorithm that identifies the flow of evidence in each path and decomposes it into direct comparisons. To illustrate the methodology, we use two published networks of interventions. The first compares no treatment, quinolone antibiotics, non-quinolone antibiotics and antiseptics for underlying eardrum perforations and the second compares 14 antimanic drugs. We believe that this approach is a useful and novel addition to network meta-analysis methodology, which allows the consistent derivation of the proportion contributions of direct evidence from individual studies to network treatment effects.

In network meta-analysis, it is important to assess the influence of the limitations or other characteristics of individual studies on the estimates obtained from the network. The percentage contribution matrix, which shows how much each direct treatment effect contributes to each treatment effect estimate from network meta-analysis, is crucial in this context. We use ideas from graph theory to derive the percentage that is contributed by each direct treatment effect. We start with the ‘projection’ matrix in a two-step network meta-analysis model, called the H matrix, which is analogous to the hat matrix in a linear regression model. We develop a method to translate H entries to percentage contributions based on the observation that the rows of H can be interpreted as flow networks, where a stream is defined as the composition of a path and its associated flow. We present an algorithm that identifies the flow of evidence in each path and decomposes it into direct comparisons. To illustrate the methodology, we use two published networks of interventions. The first compares no treatment, quinolone antibiotics, non-quinolone antibiotics and antiseptics for underlying eardrum perforations and the second compares 14 antimanic drugs. We believe that this approach is a useful and novel addition to network meta-analysis methodology, which allows the consistent derivation of the percentage contributions of direct evidence from individual studies to network treatment effects.

In network meta-analysis, it is important to assess the influence of the limitations or other characteristics of individual studies on the estimates obtained from the network. The percentage contribution matrix, which shows how much each direct treatment effect contributes to each treatment effect estimate from network meta-analysis, is crucial in this context. We use ideas from graph theory to derive the percentage that is contributed by each direct treatment effect. We start with the ‘projection’ matrix in a two-step network meta-analysis model, called the H matrix, which is analogous to the hat matrix in a linear regression model. We develop a method to translate H entries to percentage contributions based on the observation that the rows of H can be interpreted as flow networks, where a stream is defined as the composition of a path and its associated flow. We present an algorithm that identifies the flow of evidence in each path and decomposes it into direct comparisons. To illustrate the methodology, we use two published networks of interventions. The first compares no treatment, quinolone antibiotics, non-quinolone antibiotics and antiseptics for underlying eardrum perforations and the second compares 14 antimanic drugs. We believe that this approach is a useful and novel addition to network meta-analysis methodology, which allows the consistent derivation of the percentage contributions of direct evidence from individual studies to network treatment effects.

Abstract Background In meta-analyses of diagnostic test accuracy, routinely only one pair of sensitivity and specificity per study is used. However, for tests based on a biomarker or a questionnaire often more than one threshold and the corresponding values of true positives, true negatives, false positives and false negatives are known. Methods We present a new meta-analysis approach using this additional information. It is based on the idea of estimating the distribution functions of the underlying biomarker or questionnaire within the non-diseased and diseased individuals. Assuming a normal or logistic distribution, we estimate the distribution parameters in both groups applying a linear mixed effects model to the transformed data. The model accounts for across-study heterogeneity and dependence of sensitivity and specificity. In addition, a simulation study is presented. Results We obtain a summary receiver operating characteristic (SROC) curve as well as the pooled sensitivity and specificity at every specific threshold. Furthermore, the determination of an optimal threshold across studies is possible through maximization of the Youden index. We demonstrate our approach using two meta-analyses of B type natriuretic peptide in heart failure and procalcitonin as a marker for sepsis. Conclusions Our approach uses all the available information and results in an estimation not only of the performance of the biomarker but also of the threshold at which the optimal performance can be expected.

Aim: The value of 18F-fluorodeoxyglucose (FDG) PET for the prognosis of conversion from mild cognitive impairment (MCI) to Alzheimer's dementia (AD) is controversial. In the present work, the identification of cerebral metabolic patterns with significant prognostic value for conversion of MCI patients to AD is investigated with voxel-based Cox regression, which in contrast to common categorical comparisons also utilizes time information. Methods: FDG PET data of 544 MCI patients from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database were randomly split into two equally-sized datasets (training and test). Within a median follow-up duration of 47 months (95% CI: 46–48 months) 181 patients developed AD. In the training dataset, voxel-wise Cox regressions were used to identify regions associated with conversion of MCI to AD. These were compared to regions identified by a classical group comparison (analysis of covariance (ANCOVA) with statistical parametric mapping (SPM) 8) between converters and non-converters (both adjusted for apolipoprotein E (APOE) genotype, mini-mental state examination (MMSE) score, age, sex and education). In the test dataset, normalized FDG uptake within significant brain regions from voxel-wise Cox- and ANCOVA analyses (Cox- and ANCOVA- regions of interest (ROI), respectively) and clinical variables APOE status, MMSE score and education were tested in different Cox models (adjusted for age, sex) including: (1) only clinical variables, (2) only normalized FDG uptake in ANCOVA-ROI, (3) only normalized FDG uptake from Cox-ROI, (4) clinical variables plus FDG uptake in ANCOVA-ROI, (5) clinical variables plus FDG uptake from Cox-ROI. Results: Conversion-related regions with relative hypometabolism comprised parts of the temporo-parietal and posterior cingulate cortex/precuneus for voxel-wise ANCOVA, plus frontal regions for voxel-wise Cox regression (both p