Continuously updated network meta-analysis and statistical monitoring for timely decision-making

Impact of SGLT2 inhibitors on patient outcomes: a network meta-analysis

BACKGROUND

A comprehensive network meta-analysis comparing the effects of individual sodium-glucose cotransporter 2 (SGLT2) inhibitors on patients with and without comorbidities including diabetes mellitus (DM), heart failure (HF), and chronic kidney disease (CKD) has not been previously conducted.

METHODS

We searched PubMed, Embase, Cochrane, and ClinicalTrials.gov for randomized controlled trials up to March 28, 2023. Network meta-analysis using a random-effects model was conducted to calculate risk ratios (RRs). Risk of Bias tool 2.0 was used to assess bias, and CINeMA to assess the certainty of evidence. In the subgroup analysis, the SGLT2 inhibitors were classified into highly (dapagliflozin, empagliflozin, and ertugliflozin) and less selective SGLT2 inhibitors (canagliflozin and sotagliflozin).

RESULTS

A total of fourteen trials with 75,334 patients were analyzed. Among these, 40,956 had taken SGLT2 inhibitors and 34,378 had not. One of the main results with particular findings was empagliflozin users had a significantly lower risk of all-cause death compared to dapagliflozin users in DM population (RR: 0.81, 95% CI 0.69-0.96). In HF population, sotagliflozin users had a borderline significantly lower risk of CV death or hospitalization for HF (HHF) than dapagliflozin users (RR: 0.90, 95% CI 0.80-1.01). In non-HF population, those who used canagliflozin had a significantly lower risk of CV death or HHF compared with those who used dapagliflozin (RR: 0.75, 95% CI 0.58-0.98). At last, for HF patients, those who used less selective SGLT2 inhibitors had a significantly lower risk of MACEs compared to those who used highly selective SGLT2 inhibitors (RR: 0.75, 95% CI 0.62-0.90).

CONCLUSIONS

Our network meta-analysis revealed that empagliflozin users with diabetes experienced a lower risk of dying from any cause than those using dapagliflozin. Additionally, canagliflozin users demonstrated a reduced risk of cardiovascular death or HHF compared to dapagliflozin users in those without HF. In HF patients, less selective SGLT2 inhibitors showed superior CV composite outcomes, even surpassing the performance of highly selective SGLT2 inhibitors.

TRIAL REGISTRATION

PROSPERO [CRD42022361906].

How to update a living systematic review and keep it alive during a pandemic: a practical guide

BACKGROUND

The covid-19 pandemic has highlighted the role of living systematic reviews. The speed of evidence generated during the covid-19 pandemic accentuated the challenges of managing high volumes of research literature.

METHODS

In this article, we summarise the characteristics of ongoing living systematic reviews on covid-19, and we follow a life cycle approach to describe key steps in a living systematic review.

RESULTS

We identified 97 living systematic reviews on covid-19, published up to 7th November 2022, which focused mostly on the effects of pharmacological interventions (n = 46, 47%) or the prevalence of associated conditions or risk factors (n = 30, 31%). The scopes of several reviews overlapped considerably. Most living systematic reviews included both observational and randomised study designs (n = 45, 46%). Only one-third of the reviews has been updated at least once (n = 34, 35%). We address practical aspects of living systematic reviews including how to judge whether to start a living systematic review, methods for study identification and selection, data extraction and evaluation, and give recommendations at each step, drawing from our own experience. We also discuss when it is time to stop and how to publish updates.

CONCLUSIONS

Methods to improve the efficiency of searching, study selection, and data extraction using machine learning technologies are being developed, their performance and applicability, particularly for reviews based on observational study designs should improve, and ways of publishing living systematic reviews and their updates will continue to evolve. Finally, knowing when to end a living systematic review is as important as knowing when to start.

When we shouldn't borrow information from an existing network of trials for planning a new trial

Introduction: To achieve higher power or increased precision for a new trial, methods based on updating network meta-analysis (NMA) have been proposed by researchers. However, this approach could potentially lead to misinterpreted results and misstated conclusions. This work aims to investigate the potential inflation of type I error risk when a new trial is conducted only when, based on a p-value of the comparison in the existing network, a "promising" difference between two treatments is noticed. Methods: We use simulations to evaluate the scenarios of interest. In particular, a new trial is to be conducted independently or depending on the results from previous NMA in various scenarios. Three analysis methods are applied to each simulation scenario: with the existing network, sequential analysis and without the existing network. Results: For the scenario that the new trial will be conducted only when a promising finding (p-value <5%) is indicated by the existing network, the type I error risk increased dramatically (38.5% in our example data) when analyzed with the existing network and sequential analysis. The type I error is controlled at 5% when analyzing the new trial without the existing network. Conclusion: If the intention is to combine a trial result with an existing network of evidence, or if it is expected that the trial will eventually be included in a network meta-analysis, then the decision that a new trial is performed should not depend on a statistically "promising" finding indicated by the existing network.

Feasibility study for interactive reporting of network meta-analysis: experiences from the development of the MetaInsight COVID-19 app for stakeholder exploration, re-analysis and sensitivity analysis from living systematic reviews

BACKGROUND

Network meta-analysis (NMA) has been increasingly adopted worldwide by Cochrane reviews, guideline developers and decision-making bodies to identify optimal treatment choices. However, NMA results are often produced statically, not allowing stakeholders to 'dig deeper' and interrogate with their own judgement. Additionally, amid the COVID-19 pandemic, unnecessary or duplicated reviews have been proposed which analyse from the same pool of evidence. We developed the 'MetaInsight COVID-19' app as a prototype for an interactive platform to eliminate such duplicated efforts, by empowering users to freely analyse the data and improve scientific transparency.

METHODS

MetaInsight COVID-19 ( https://crsu.shinyapps.io/metainsightcovid/ ) was developed to conduct NMA with the evolving evidence on treatments for COVID-19. It was updated weekly between 19th May - 19th Oct 2020, incorporating new evidence identified from a living systematic review.

RESULTS

The app includes embedded functions to facilitate study selection based on study characteristics, and displays the synthesised results in real time. It allows both frequentist and Bayesian NMA to be conducted as well as consistency and heterogeneity assessments. A demonstration of the app is provided and experiences of building such a platform are discussed.

CONCLUSIONS

MetaInsight COVID-19 allows users to take control of the evidence synthesis using the analytic approach they deem appropriate to ascertain how robust findings are to alternative analysis strategies and study inclusion criteria. It is hoped that this app will help avoid many of the duplicated efforts when reviewing and synthesising the COVID-19 evidence, and, in addition, establish the desirability of an open platform format such as this for interactive data interrogation, visualisation, and reporting for any traditional or 'living' NMA.

Living Systematic Reviews

Systematic reviews are difficult to keep up to date, but failure to do so leads to poor review currency and accuracy. "Living systematic review" (LSR) is an approach that aims to continually update a review, incorporating relevant new evidence as it becomes available. LSRs may be particularly important in fields where research evidence is emerging rapidly, current evidence is uncertain, and new research may change policy or practice decisions.This chapter describes the concept and processes of living systematic reviews. It describes the general principles of LSRs, when they might be of particular value, and how their procedures differ from conventional systematic reviews. The chapter focuses particularly on two methods of sequential meta-analysis that may be particularly useful for LSRs: Trial Sequential Analysis and Sequential Meta-Analysis, which both control for Type I error, Type II error (failing to detect a genuine effect) and take account of heterogeneity.

Lag times in the publication of network meta-analyses: a survey

OBJECTIVE

We assessed the extent of lag times in the publication and indexing of network meta-analyses (NMAs).

STUDY DESIGN

This was a survey of published NMAs on drug interventions.

SETTING

NMAs indexed in PubMed (searches updated in May 2020).

PRIMARY AND SECONDARY OUTCOME MEASURES

Lag times were measured as the time between the last systematic search and the article submission, acceptance, online publication, indexing and Medical Subject Headings (MeSH) allocation dates. Time-to-event analyses were performed considering independent variables (geographical origin, Journal Impact Factor, Scopus CiteScore, open access status) (SPSS V.24, R/RStudio).

RESULTS

We included 1245 NMAs. The median time from last search to article submission was 6.8 months (204 days (IQR 95-381)), and to publication was 11.6 months. Only 5% of authors updated their search after first submission. There is a very slightly decreasing historical trend of acceptance (rho=-0.087; p=0.010), online publication (rho=-0.080; p=0.008) and indexing (rho=-0.080; p=0.007) lag times. Journal Impact Factor influenced the MeSH allocation process, but not the other lag times. The comparison between open access versus subscription journals confirmed meaningless differences in acceptance, online publication and indexing lag times.

CONCLUSION

Efforts by authors to update their search before submission are needed to reduce evidence production time. Peer reviewers and editors should ensure authors' compliance with NMA standards. The accuracy of these findings depends on the accuracy of the metadata used; as we evaluated only NMA on drug interventions, results may not be generalisable to all types of studies.

A framework for prospective, adaptive meta-analysis (FAME) of aggregate data from randomised trials

BACKGROUND

The vast majority of systematic reviews are planned retrospectively, once most eligible trials have completed and reported, and are based on aggregate data that can be extracted from publications. Prior knowledge of trial results can introduce bias into both review and meta-analysis methods, and the omission of unpublished data can lead to reporting biases. We present a collaborative framework for prospective, adaptive meta-analysis (FAME) of aggregate data to provide results that are less prone to bias. Also, with FAME, we monitor how evidence from trials is accumulating, to anticipate the earliest opportunity for a potentially definitive meta-analysis.

METHODOLOGY

We developed and piloted FAME alongside 4 systematic reviews in prostate cancer, which allowed us to refine the key principles. These are to: (1) start the systematic review process early, while trials are ongoing or yet to report; (2) liaise with trial investigators to develop a detailed picture of all eligible trials; (3) prospectively assess the earliest possible timing for reliable meta-analysis based on the accumulating aggregate data; (4) develop and register (or publish) the systematic review protocol before trials produce results and seek appropriate aggregate data; (5) interpret meta-analysis results taking account of both available and unavailable data; and (6) assess the value of updating the systematic review and meta-analysis. These principles are illustrated via a hypothetical review and their application to 3 published systematic reviews.

CONCLUSIONS

FAME can reduce the potential for bias, and produce more timely, thorough and reliable systematic reviews of aggregate data.

Effects of nutrition intervention strategies in the primary prevention of overweight and obesity in school settings: a protocol for a systematic review and network meta-analysis

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 .

Severe COVID-19 Infection Associated with Endothelial Dysfunction Induces Multiple Organ Dysfunction: A Review of Therapeutic Interventions

Since December 2019, the SARS-CoV-2 (COVID-19) pandemic has transfixed the medical world. COVID-19 symptoms vary from mild to severe and underlying chronic conditions such as pulmonary/cardiovascular disease and diabetes induce excessive inflammatory responses to COVID-19 and these underlying chronic diseases are mediated by endothelial dysfunction. Acute respiratory distress syndrome (ARDS) is the most common cause of death in COVID-19 patients, but coagulation induced by excessive inflammation, thrombosis, and disseminated intravascular coagulation (DIC) also induce death by multiple-organ dysfunction syndrome. These associations imply that maintaining endothelial integrity is crucial for favorable prognoses with COVID-19 and therapeutic intervention to support this may be beneficial. Here, we summarize the extent of heart injuries, ischemic stroke and hemorrhage, acute kidney injury, and liver injury caused by immune-mediated endothelial dysfunction that result in the phenomenon of multi-organ dysfunction seen in COVID-19 patients. Moreover, the potential therapeutic effect of angiotensin receptor blockers and angiotensin-converting enzyme inhibitors that improve endothelial dysfunction as well as the bradykinin storm are discussed.

Antidepressant prescriptions have not fully reflected evolving evidence from cumulative network meta-analyses and guideline recommendations

OBJECTIVES

This study compares three major elements of evidence-based medicine (EBM) practices, namely evidence synthesis, clinical practice guidelines (CPGs), and real-world prescriptions in the United States, regarding antidepressant treatments of major depression over the past 3 decades.

STUDY DESIGN AND SETTING

We conducted network meta-analyses (NMAs) of antidepressants every 5 years up to 2016 based on a comprehensive data set of double-blind randomized controlled trials. We identified CPGs and extracted their recommendations. We surveyed the prescriptions in the United States at 5-year intervals up to 2015.

RESULTS

Most drugs recommended by CPGs presented favorable performance in efficacy and acceptability in NMAs. However, CPG recommendations were often in terms of drug classes rather than individual drugs, whereas NMAs suggested distinctive difference between drugs within the same class. The update intervals of all CPGs were longer than 5 years. All the antidepressants prescribed frequently in the United States were recommended by CPGs. However, changes in prescriptions did not correspond to alterations in CPGs or to apparent changes in the effects indicated by NMAs. Many factors including marketing efforts, regulations, or patient values may have played a role.

CONCLUSION

Enhancements including accelerating CPG updates and monitoring the impact of marketing on prescriptions should be considered in future EBM implementation.

Visualizing the evolution of evidence: Cumulative network meta-analyses of new generation antidepressants in the last 40 years

It is often challenging to present the available evidence in a timely and comprehensible manner. We aimed to visualize the evolution of evidence about antidepressants for depression by conducting cumulative network meta-analyses (NMAs) and to examine whether it could have helped the selection of optimal drugs. We built a Shiny web application that performs and presents cumulative NMAs based on R netmeta. We used a comprehensive dataset of double-blind randomized controlled trials of 21 antidepressants in the acute treatment of major depression. The primary outcomes were efficacy (treatment response) and acceptability (all-cause discontinuation), and treatment effects were summarized via odds ratios. We evaluated the confidence in evidence using the CINeMA (Confidence in Network Meta-Analysis) framework for a series of consecutive NMAs. Users can change several conditions for the analysis, such as the period of synthesis, among the others. We present the league tables and two-dimensional plots that combine efficacy, acceptability and level of confidence in the evidence together, for NMAs conducted in 1990, 1995, 2000, 2005, 2010, and 2016. They reveal that through the past four decades, newly approved drugs often showed initially exaggerated results, which tended to diminish and stabilize after approximately a decade. Over the years, the drugs with relative superiority changed dramatically; but as the evidence network grew larger and better connected, the overall confidence improved. The Shiny app visualizes how evidence evolved over years, emphasizing the need for a careful interpretation of relative effects between drugs, especially for the potentially amplified performance of newly approved drugs. HIGHLIGHTS: Network meta-analysis is considered to be a proper way of demonstrating the available evidence, since it allows comparisons between multiple interventions, and has been proved to be statistically powerful. It is challenging to present the voluminous results of NMA in an efficient and comprehendible manner. Evidence evolution based on the relatively new method NMA has not been investigated yet. The results of NMA should not only include the effects but also the confidence in the evidence, which can help interpret the findings appropriately. Effective use of rapidly developing statistical analysis and presentation tools such as Shiny package in R, may facilitate and simplify the visualization of NMA output. We should stay conservative towards new drugs, as their performance was often shown to be exaggerated initially, and it took time to become stable.

Resuscitation fluid types in sepsis, surgical, and trauma patients: a systematic review and sequential network meta-analyses

Background

Crystalloids and different component colloids, used for volume resuscitation, are sometimes associated with various adverse effects. Clinical trial findings for such fluid types in different patients’ conditions are conflicting. Whether the mortality benefit of balanced crystalloid than saline can be inferred from sepsis to other patient group is uncertain, and adverse effect profile is not comprehensive. This study aims to compare the survival benefits and adverse effects of seven fluid types with network meta-analysis in sepsis, surgical, trauma, and traumatic brain injury patients.

Methods

Searched databases (PubMed, EMBASE, and Cochrane CENTRAL) and reference lists of relevant articles occurred from inception until January 2020. Studies on critically ill adults requiring fluid resuscitation were included. Intervention studies reported on balanced crystalloid, saline, iso-oncotic albumin, hyperoncotic albumin, low molecular weight hydroxyethyl starch (L-HES), high molecular weight HES, and gelatin. Network meta-analyses were conducted using random-effects model to calculate odds ratio (OR) and mean difference. Risk of Bias tool 2.0 was used to assess bias. Confidence in Network Meta-Analysis (CINeMA) web application was used to rate confidence in synthetic evidence.

Results

Fifty-eight trials (n = 26,351 patients) were identified. Seven fluid types were evaluated. Among patients with sepsis and surgery, balanced crystalloids and albumin achieved better survival, fewer acute kidney injury, and smaller blood transfusion volumes than saline and L-HES. In those with sepsis, balanced crystalloids significantly reduced mortality more than saline (OR 0.84; 95% CI 0.74–0.95) and L-HES (OR 0.81; 95% CI 0.69–0.95) and reduced acute kidney injury more than L-HES (OR 0.80; 95% CI 0.65–0.99). However, they required the greatest resuscitation volume among all fluid types, especially in trauma patients. In patients with traumatic brain injury, saline and L-HES achieved lower mortality than albumin and balanced crystalloids; especially saline was significantly superior to iso-oncotic albumin (OR 0.55; 95% CI 0.35–0.87).

Conclusions

Our network meta-analysis found that balanced crystalloids and albumin decreased mortality more than L-HES and saline in sepsis patients; however, saline or L-HES was better than iso-oncotic albumin or balanced crystalloids in traumatic brain injury patients.

Trial registration

PROSPERO website, registration number: CRD42018115641).

A Markov chain approach for ranking treatments in network meta-analysis

When interpreting the relative effects from a network meta-analysis (NMA), researchers are usually aware of the potential limitations that may render the results for some comparisons less useful or meaningless. In the presence of sufficient and appropriate data, some of these limitations (eg, risk of bias, small-study effects, publication bias) can be taken into account in the statistical analysis. Very often, though, the necessary data for applying these methods are missing and data limitations cannot be formally integrated into ranking. In addition, there are other important characteristics of the treatment comparisons that cannot be addressed within a statistical model but only through qualitative judgments; for example, the relevance of data to the research question, the plausibility of the assumptions, and so on. Here, we propose a new measure for treatment ranking called the Probability of Selecting a Treatment to Recommend (POST-R). We suggest that the order of treatments should represent the process of considering treatments for selection in clinical practice and we assign to each treatment a probability of being selected. This process can be considered as a Markov chain model that allows the end-users of NMA to select the most appropriate treatments based not only on the NMA results but also to information external to the NMA. In this way, we obtain rankings that can inform decision-making more efficiently as they represent not only the relative effects but also their potential limitations. We illustrate our approach using a NMA comparing treatments for chronic plaque psoriasis and we provide the Stata commands.

Different techniques for peritoneal dialysis catheter implantation: A systematic review and network meta-analysis

The current consensus recommended the peritoneal dialysis catheter (PDC) techniques based on the patients' anesthesia situation and previous abdominal surgery. However, the research comparing of all the existing PDC techniques is lacking. The objective was to compare the efficacy and safety of PDC techniques by network meta-analysis (NMA). A systematic review of databases was conducted to identify eligible studies. NMA was used to estimate the ranking for endpoints. Our NMA included 41 studies (9 randomized controlled trials (RCTs) and 32 observational trials) and enrolled 3902 patients, comparing three techniques: the laparoscopic catheterization (LC), open surgery catheterization (OSC), and percutaneous catheterization (PC). NMA in RCTs showed OSC had the highest incidence of catheter mechanical dysfunction, PC and LC were very similar, but this result had no statistical difference. NMA in observational studies showed that LC had the highest 1-year catheter survival but without statistical difference (LC vs. OSC: odds ratio (OR) 1.75, 95% credible intervals (CrIs) 0.90-3.40; PC vs. OSC: OR 1.55, 95% CrIs 0.80-2.97; PC vs. LC: OR 0.88, 95% CrIs 0.54-1.44). OSC had the lowest incidence for bleeding. The complications of leakage, peritonitis, and exit/tunnel infection were inconclusive due to the inconsistent results between RCTs and observational studies. Our NMA revealed LC may have the best 1-year catheter survival. PC and LC might be efficacious in lowering the mechanical dysfunction. OSC had the lowest incidence for bleeding. More RCTs with larger scale and higher quality are needed in order to obtain more credible evidence.

Nucleos(t)ide analogues for the treatment of chronic hepatitis B: a systematic review with network meta-analysis

OBJECTIVES

Chronic hepatitis B (CHB) is a major global health problem caused by hepatitis B virus (HBV) infection, and can put patients at high risk of death from cirrhosis and liver cancer. However, CHB can be treated with nucleos(t)ide analogues. We aimed to evaluate the effectiveness and safety of nucleos(t)ide analogues for the treatment of CHB patients.

METHODS

A systematic literature search was performed. Direct comparison meta-analyses and network meta-analysis (NMA) were carried out.

RESULTS

Thirty-six randomized controlled trials (RCTs) met inclusion criteria. Compared with placebo, the nucleos(t)ide analogues were all effective in HBeAg seroconversion, HBeAg loss, and achieving undetectable HBV DNA. Telbivudine was associated with higher HBeAg seroconversion compared with entecavir. For HBeAg loss rate and proportion of achieving undetectable HBV DNA, tenofovir ranked as the best. Entecavir might be the most potent in the normalization of alanine aminotransferase (ALT). The nucleos(t)ide analogues did not have higher serious adverse events rate as compared with placebo.

CONCLUSION

The nucleos(t)ide analogues are all effective for HBeAg seroconversion, HBeAg loss, undetectable HBV DNA, and most are effective for ALT normalization in adults with CHB. RCTs of multi-center, low risk of bias, and long-term follow-up are still needed.

Preventive Treatments for Psychosis: Umbrella Review (Just the Evidence)

Background: Indicated primary prevention in young people at Clinical High Risk for Psychosis (CHR-P) is a promising avenue for improving outcomes of one of the most severe mental disorders but their effectiveness has recently been questioned. Methods: Umbrella review. A multi-step independent literature search of Web of Science until January 11, 2019, identified interventional meta-analyses in CHR-P individuals. The individual randomised controlled trials that were analysed by the meta-analyses were extracted. A review of ongoing trials and a simulation of living meta-analysis complemented the analysis. Results: Seven meta-analyses investigating preventive treatments in CHR-P individuals were included. None of them produced pooled effect sizes across psychological, pharmacological, or other types of interventions. The outcomes analysed encompassed risk of psychosis onset, the acceptability of treatments, the severity of attenuated positive/negative psychotic symptoms, depression, symptom-related distress, social functioning, general functioning, and quality of life. These meta-analyses were based on 20 randomised controlled trials: the vast majority defined the prevention of psychosis onset as their primary outcome of interest and only powered to large effect sizes. There was no evidence to favour any preventive intervention over any other (or control condition) for improving any of these clinical outcomes. Caution is required when making clinical recommendations for the prevention of psychosis in individuals at risk. Discussion: Prevention of psychosis from a CHR-P state has been, and should remain, the primary outcome of interventional research, refined and complemented by other clinically meaningful outcomes. Stagnation of knowledge should promote innovative and collaborative research efforts, in line with the progressive and incremental nature of medical knowledge. Advancements will most likely be associated with the development of new experimental therapeutics that are ongoing along with the ability to deconstruct the high heterogeneity within CHR-P populations. This would require the estimation of treatment-specific effect sizes through living individual participant data meta-analyses, controlling risk enrichment during recruitment, statistical power, and embedding precision medicine within youth mental health services that can accommodate sequential prognosis and advanced trial designs. Conclusions: The evidence-based challenges and proposed solutions addressed by this umbrella review can inform the next generation of research into preventive treatments for psychosis.

Perspective: Network Meta-analysis Reaches Nutrition Research: Current Status, Scientific Concepts, and Future Directions

Traditional pairwise meta-analysis (PMA) is a very useful method that pools evidence from one study design type if appropriate; its widespread use in nutrition research is an important phenomenon. Recently, a promising method for more advanced evidence-synthesis, called network meta-analysis (NMA), was introduced. NMA is an extension of PMA that enables simultaneous comparison of multiple interventions. NMA combines direct evidence (i.e., trials comparing 2 interventions directly) and indirect evidence (i.e., from a connected route via ≥1 comparators, e.g. placebo) in a network of studies. NMAs have the potential to advance knowledge in the field of nutrition as they provide insights that cannot be obtained by individual 2-arm randomized controlled trials or PMA. Thus, in this perspective paper, we aim to summarize the current (methodologic) status of published NMAs in nutrition research and emphasize advances and strengths in comparison with traditional PMA through specific examples, and highlight potential pitfalls and limitations. NMA is an emerging methodology in the field of nutrition research. A PubMed search identified only 23 nutrition research-related NMAs published since the inception of journals up to January 8, 2019 (61% of them published since 2017), compared with >5000 published PMAs. Moreover, we aim to highlight the scientific concepts and standards through the use of the following NMA example: "Which type of oils/solid fats offers the greatest impact on blood lipids?" In this regard, we discuss intervention definitions, transitivity/similarity, statistical methods, description and visualization of results, inconsistency, ranking, dissemination bias, assessing the certainty of evidence by Grading of Recommendations Assessment, Development and Evaluation, and reporting guidelines. We expect that rigorously conducted NMAs based on high-quality systematic reviews will become the new evidence synthesis benchmark in nutrition research. However, caution is warranted because abuse and misinterpretations of PMA and NMA findings could hamper the scientific field and possibly decision-making regarding public policy.

Synthesizing existing evidence to design future trials: survey of methodologists from European institutions

Background

‘Conditional trial design’ is a framework for efficiently planning new clinical trials based on a network of relevant existing trials. The framework considers whether new trials are required and how the existing evidence can be used to answer the research question and plan future research. The potential of this approach has not been fully realized.

Methods

We conducted an online survey among trial statisticians, methodologists, and users of evidence synthesis research using referral sampling to capture opinions about the conditional trial design framework and current practices among clinical researchers. The questions included in the survey were related to the decision of whether a meta-analysis answers the research question, the optimal way to synthesize available evidence, which relates to the acceptability of network meta-analysis, and the use of evidence synthesis in the planning of new studies.

Results

In total, 76 researchers completed the survey. Two out of three survey participants (65%) were willing to possibly or definitely consider using evidence synthesis to design a future clinical trial and around half of the participants would give priority to such a trial design. The median rating of the frequency of using such a trial design was 0.41 on a scale from 0 (never) to 1 (always). Major barriers to adopting conditional trial design include the current regulatory paradigm and the policies of funding agencies and sponsors.

Conclusions

Participants reported moderate interest in using evidence synthesis methods in the design of future trials. They indicated that a major paradigm shift is required before the use of network meta-analysis is regularly employed in the design of trials.

Electronic supplementary material

The online version of this article (10.1186/s13063-019-3449-6) contains supplementary material, which is available to authorized users.

Hypothesis testing in Bayesian network meta-analysis

Background

Network meta-analysis is an extension of the classical pairwise meta-analysis and allows to compare multiple interventions based on both head-to-head comparisons within trials and indirect comparisons across trials. Bayesian or frequentist models are applied to obtain effect estimates with credible or confidence intervals. Furthermore, p-values or similar measures may be helpful for the comparison of the included arms but related methods are not yet addressed in the literature. In this article, we discuss how hypothesis testing can be done in a Bayesian network meta-analysis.

Methods

An index is presented and discussed in a Bayesian modeling framework. Simulation studies were performed to evaluate the characteristics of this index. The approach is illustrated by a real data example.

Results

The simulation studies revealed that the type I error rate is controlled. The approach can be applied in a superiority as well as in a non-inferiority setting.

Conclusions

Test decisions can be based on the proposed index. The index may be a valuable complement to the commonly reported results of network meta-analyses. The method is easy to apply and of no (noticeable) additional computational cost.

Living network meta-analysis compared with pairwise meta-analysis in comparative effectiveness research: empirical study

OBJECTIVE

To examine whether the continuous updating of networks of prospectively planned randomised controlled trials (RCTs) ("living" network meta-analysis) provides strong evidence against the null hypothesis in comparative effectiveness of medical interventions earlier than the updating of conventional, pairwise meta-analysis.

DESIGN

Empirical study of the accumulating evidence about the comparative effectiveness of clinical interventions.

DATA SOURCES

Database of network meta-analyses of RCTs identified through searches of Medline, Embase, and the Cochrane Database of Systematic Reviews until 14 April 2015.

ELIGIBILITY CRITERIA FOR STUDY SELECTION

Network meta-analyses published after January 2012 that compared at least five treatments and included at least 20 RCTs. Clinical experts were asked to identify in each network the treatment comparison of greatest clinical interest. Comparisons were excluded for which direct and indirect evidence disagreed, based on side, or node, splitting test (P<0.10).

OUTCOMES AND ANALYSIS

Cumulative pairwise and network meta-analyses were performed for each selected comparison. Monitoring boundaries of statistical significance were constructed and the evidence against the null hypothesis was considered to be strong when the monitoring boundaries were crossed. A significance level was defined as α=5%, power of 90% (β=10%), and an anticipated treatment effect to detect equal to the final estimate from the network meta-analysis. The frequency and time to strong evidence was compared against the null hypothesis between pairwise and network meta-analyses.

RESULTS

49 comparisons of interest from 44 networks were included; most (n=39, 80%) were between active drugs, mainly from the specialties of cardiology, endocrinology, psychiatry, and rheumatology. 29 comparisons were informed by both direct and indirect evidence (59%), 13 by indirect evidence (27%), and 7 by direct evidence (14%). Both network and pairwise meta-analysis provided strong evidence against the null hypothesis for seven comparisons, but for an additional 10 comparisons only network meta-analysis provided strong evidence against the null hypothesis (P=0.002). The median time to strong evidence against the null hypothesis was 19 years with living network meta-analysis and 23 years with living pairwise meta-analysis (hazard ratio 2.78, 95% confidence interval 1.00 to 7.72, P=0.05). Studies directly comparing the treatments of interest continued to be published for eight comparisons after strong evidence had become evident in network meta-analysis.

CONCLUSIONS

In comparative effectiveness research, prospectively planned living network meta-analyses produced strong evidence against the null hypothesis more often and earlier than conventional, pairwise meta-analyses.