Two decades of network meta-analysis: Roadmap to their applications and challenges.

Recently, Ades and colleagues discussed the controversies and advancements in network meta-analysis (NMA) over the past two decades, discussing its reliability, assumptions, novel approaches, and provided some useful recommendations for the conduction of NMAs. The present discussion paper builds on the insights by Ades and colleagues, providing a roadmap for NMA applications, advancements in software and tools, and approaches designed to facilitate the assessment and interpretation of NMA findings. It also discusses the impact of NMA across disciplines, particularly for policymakers and guideline developers. Despite 20 years of NMA history, challenges remain in understanding and assessing assumptions, communicating and interpreting findings, and applying common approaches like network meta-regression and NMA involving non-randomized studies in readily available software. NMA has proven particularly valuable in clinical decision-making, which highlights the need for additional training and interdisciplinary collaboration of knowledge users, including patient engagement, to enhance its adoption and address real-world problems.

Methodological review of NMA bias concepts provides groundwork for the development of a list of concepts for potential inclusion in a new risk of bias tool for network meta-analysis (RoB NMA Tool).

INTRODUCTION: Network meta-analyses (NMAs) have gained popularity and grown in number due to their ability to provide estimates of the comparative effectiveness of multiple treatments for the same condition. The aim of this study is to conduct a methodological review to compile a preliminary list of concepts related to bias in NMAs. METHODS AND ANALYSIS: We included papers that present items related to bias, reporting or methodological quality, papers assessing the quality of NMAs, or method papers. We searched MEDLINE, the Cochrane Library and unpublished literature (up to July 2020). We extracted items related to bias in NMAs. An item was excluded if it related to general systematic review quality or bias and was included in currently available tools such as ROBIS or AMSTAR 2. We reworded items, typically structured as questions, into concepts (i.e. general notions). RESULTS: One hundred eighty-one articles were assessed in full text and 58 were included. Of these articles, 12 were tools, checklists or journal standards; 13 were guidance documents for NMAs; 27 were studies related to bias or NMA methods; and 6 were papers assessing the quality of NMAs. These studies yielded 99 items of which the majority related to general systematic review quality and biases and were therefore excluded. The 22 items we included were reworded into concepts specific to bias in NMAs. CONCLUSIONS: A list of 22 concepts was included. This list is not intended to be used to assess biases in NMAs, but to inform the development of items to be included in our tool.

HIGHLIGHTS: • Our research aimed to develop a preliminary list of concepts related to bias with the goal of developing the first tool for assessing the risk of bias in the results and conclusions of a network meta-analysis (NMA).• We followed the methodology proposed by Whiting (2017) and Sanderson (2007) for creating systematically developed lists of quality items, as a first step in the development of a risk of bias tool for network meta-analysis (RoB NMA Tool).• We included items related to biases in NMAs and excluded items that are equally applicable to all systematic reviews as they are covered by other tools (e.g. ROBIS, AMSTAR 2).• Fifty-seven studies were included generating 99 items, which when screened, yielded 22 included items. These items were then reworded into concepts in preparation for a Delphi process for further vetting by external experts.• A limitation of our study is the challenge in retrieving methods studies as methods collections are not regularly updated.

Comparison of two distinct arrival and treatment programs for bovine respiratory disease in high-risk feeder cattle entering a feedlot.

Antimicrobial metaphylaxis of high-risk cattle entering the feedlot is a common management strategy implemented against bovine respiratory disease (BRD). Typically, following a prescribed postmetaphylactic interval (PMI), animals displaying clinical signs of BRD are pulled from the feedlot pen and treated with antimicrobials when treatment criteria are met. The objective of this study was to compare 2 distinct sequential BRD treatment protocols each consisting of a metaphylactic antimicrobial plus 2 potential subsequent as-needed treatment antimicrobials. Heifers at high-risk for BRD (n = 1000; initial BW = 229 kg ± 1.6) purchased from sale barns in the southeastern U.S. were transported to a contract research feedlot in Nebraska and randomly assigned to 1 of 2 experimental groups (10 blocks of 100 animals each; 50 per treatment group). Experimental groups consisted of: (1) tulathromycin metaphylaxis (2.5 mg/kg) followed by ceftiofur crystalline free acid (6.6 mg/kg) and danofloxacin (8 mg/kg) for subsequent first and second as-needed BRD treatment, respectively (TCD) or (2) tildipirosin metaphylaxis (4 mg/kg) followed by florfenicol-flunixin meglumine (40 mg/kg florfenicol; 2.2 mg/kg flunixin meglumine) and enrofloxacin (12.5 mg/kg) for subsequent first and second as-needed BRD treatment, respectively (TFFE). Following expiration of the 7-d PMI, calves that showed signs of clinical BRD were pulled and examined to determine if treatment was necessary based on a clinical attitude score (CAS). Heifers with a CAS of 1 accompanied by ≥40°C rectal temperature, and all heifers with a CAS ≥ 2 regardless of rectal temperature, received the appropriate first-treatment antimicrobial. Upon relapse, following expiration of the post-treatment interval (PTI), heifers received the appropriate second-treatment antimicrobial. In the first 90 d, calves in the TFFE experimental group received more first-treatments than calves in the TCD experimental group (P = 0.054) and resulted in 50% greater mortality (P < 0.043) relative to the TCD heifers. From d 0 to closeout, first-treatment morbidity as well as mortality were greater in TFFE relative to TCD (P ≤ 0.032). Growth performance did not differ between treatments in the first 90 d; however, ADG was greater (P = 0.033) and G:F improved (P = 0.014) at closeout in TCD versus TFFE on a deads-in basis. Closeout economics revealed a $50.78/animal greater profit in the TCD experimental group relative to TFFE.

Analysis of adaptive platform trials using a network approach.

BACKGROUND: Adaptive platform trials allow randomized controlled comparisons of multiple treatments using a common infrastructure and the flexibility to adapt key design features during the study. Nonetheless, they have been criticized due to the potential for time trends in the underlying risk level of the population. Such time trends lead to confounding between design features and risk level, which may introduce bias favoring one or more treatments. This is particularly true when experimental treatments are not all randomized during the same time period as the control, leading to the potential for bias from non-concurrent controls. METHODS: Two analysis methods addressing this bias are stratification and adjustment. Stratification uses only comparisons between treatment cohorts randomized during identical time periods and does not use non-concurrent randomizations. Adjustment uses a modeled analysis including time period adjustment, allowing all data to be used, even from periods without concurrent randomization. We show that these competing approaches may be embedded in a common framework using network meta-analysis principles. We interpret the stages between adaptations in a platform trial as separate fixed design trials. This allows platform trials to be viewed as networks of direct randomized comparisons and indirect non-randomized comparisons. Network meta-analysis methodology can be re-purposed to aggregate the total information from a platform trial and to transparently decompose this total information into direct randomized evidence and indirect non-randomized evidence. This allows sensitivity to indirect information to be assessed and the two analysis methods to be clearly compared. RESULTS: Simulations of platform trials were analyzed using a network approach implemented in the netmeta package in R. The results demonstrated bias of unadjusted methods in the presence of time trends in risk level. Adjustment and stratification were both unbiased when direct evidence and indirect evidence were consistent. Network tests of inconsistency may be used to diagnose inconsistency when it exists. In an illustrative network analysis of one of the treatment comparisons from the STAMPEDE platform trial in metastatic prostate cancer, indirect comparisons using non-concurrent controls were inconsistent with the information from direct randomized comparisons. This supports the primary analysis approach of STAMPEDE, which used only direct randomized comparisons. CONCLUSION: Network meta-analysis provides a natural methodology for analyzing the network of direct and indirect treatment comparisons from a platform trial. Such analyses provide transparent separation of direct and indirect evidence, allowing assessment of the impact of non-concurrent controls. We recommend time-stratified analysis of concurrently controlled comparisons for primary analyses, with time-adjusted analyses incorporating non-concurrent controls reserved for secondary analyses. However, regardless of which methodology is used, a network analysis provides a useful supplement to the primary analysis.

Improving Glycemic Control in Type 2 Diabetes Using Mobile Applications and e-Coaching: A Mixed Treatment Comparison Network Meta-Analysis.

OBJECTIVES: This study compared the effectiveness of glycemic control among usual care, care management using a mobile application (app), and management using an app with additional e-coaching for patients with type 2 diabetes mellitus (T2DM) using a mixed treatment comparison (MTC) network meta-analysis (NMA). METHODS: A systematic search for published randomized controlled trials (RCTs) was conducted, which included Pubmed, Web of Science, Cochrane Central Register of Controlled Trials, CINAL, Koreamed, KMbase, and ScienceOn, until October 2020. Among the 10,391 studies identified after removing duplicates, 14 RCTs were finally included in the MTC NMA. Data extraction and methodological quality assessment using version 2 of the Cochrane tool for assessing the risk-of-bias in randomized trials (RoB 2) was performed. The comparative efficacy was analyzed using the random-effects NMA based on a frequentist model by the intervention group and main outcome variables. RESULTS: At the 3-month follow-up after each intervention, a comparison of the P-scores revealed the app plus e-coaching intervention to be the most effective method for reducing the HbA1c level in a homogeneous gender ratio group (P-score 0.92). At the 6-month follow-up period, app intervention was the best in reducing the HbA1c level in the homogeneous gender ratio and under 60 years of age group (P-score 1.00). CONCLUSIONS: Based on MTC analysis using the data from published RCTs, mobile apps or apps with e-coaching interventions for T2DM patients were more effective in improving the HbA1c values, FBS, and hypoglycemia frequency than usual care. Nevertheless, further research will be needed to clarify the effects of adding e-coaching to the app. STUDY REGISTRATION: Research Registry UIN (reviewregistry780).

Efficacy and Safety of Anti-Vascular Endothelial Growth Factor Monotherapies for Neovascular Age-Related Macular Degeneration: A Mixed Treatment Comparison.

Background: We aimed to evaluate the comparative efficacy and safety of anti-vascular endothelial growth factor (anti-VEGF) monotherapy to identify its utilization and prioritization in patients with neovascular age-related macular degeneration (nAMD). Methods: Eligible studies included randomized controlled trials comparing the recommended anti-VEGF agents (ranibizumab, bevacizumab, aflibercept, brolucizumab, and conbercept) under various therapeutic regimens. Outcomes of interest included the mean change in best-corrected visual acuity (BCVA), serious adverse events, the proportion of patients who gained ≥15 letters or lost <15 letters in BCVA, the mean change in central retinal thickness, and the number of injections within 12 months. Results: Twenty-seven trials including 10,484 participants and eighteen treatments were identified in the network meta-analysis. The aflibercept 2 mg bimonthly, ranibizumab 0.5 mg T&E, and brolucizumab 6 mg q12w/q8w regimens had better visual efficacy. Brolucizumab had absolute superiority in anatomical outcomes and a relative advantage of safety, as well as good performance of aflibercept 2 mg T&E. The proactive regimens had slightly better efficacy but a slightly increased number of injections versus the reactive regimen. Bevacizumab had a statistically non-significant trend toward a lower degree of efficacy and safety. Conclusion: The visual efficacy of four individual anti-VEGF drugs is comparable. Several statistically significant differences were observed considering special anti-VEGF regimens, suggesting that brolucizumab 6 mg q12w/q8w, aflibercept 2 mg bimonthly or T&E, and ranibizumab 0.5 mg T&E are the ideal anti-VEGF regimens for nAMD patients. In the current landscape, based on the premise of equivalent efficacy and safety, the optimal choice of anti-VEGF monotherapies seems mandatory to obtain maximal benefit.

Bayesian splines versus fractional polynomials in network meta-analysis.

BACKGROUND: Network meta-analysis (NMA) provides a powerful tool for the simultaneous evaluation of multiple treatments by combining evidence from different studies, allowing for direct and indirect comparisons between treatments. In recent years, NMA is becoming increasingly popular in the medical literature and underlying statistical methodologies are evolving both in the frequentist and Bayesian framework. Traditional NMA models are often based on the comparison of two treatment arms per study. These individual studies may measure outcomes at multiple time points that are not necessarily homogeneous across studies. METHODS: In this article we present a Bayesian model based on B-splines for the simultaneous analysis of outcomes across time points, that allows for indirect comparison of treatments across different longitudinal studies. RESULTS: We illustrate the proposed approach in simulations as well as on real data examples available in the literature and compare it with a model based on P-splines and one based on fractional polynomials, showing that our approach is flexible and overcomes the limitations of the latter. CONCLUSIONS: The proposed approach is computationally efficient and able to accommodate a large class of temporal treatment effect patterns, allowing for direct and indirect comparisons of widely varying shapes of longitudinal profiles.

Overview of Differences and Similarities of Published Mixed Treatment Comparisons on Pharmaceutical Interventions for Multiple Sclerosis.

INTRODUCTION: Mixed treatment comparisons (MTCs) are increasingly important in the assessment of the benefit-risk profile of pharmaceutical treatments for relapsing-remitting multiple sclerosis (RRMS). Interpretation of MTCs requires a clear understanding of the methods of analysis and population studied. The objectives of this work were to compare MTCs of pharmaceutical treatments for RRMS, including a detailed description of differences in populations, treatments assessed, methods used and findings; and to discuss key considerations when conducting an MTC. METHODS: Fourteen databases were searched until July 2019 to identify MTCs (published during or after 2010) in adults (at least 18 years of age) with RRMS or rapidly evolving severe RRMS treated with any form of pharmaceutical treatment. No language restriction was imposed. RESULTS: Twenty-seven MTCs assessing 21 treatments were identified. Comparison highlighted many differences in conduct and reporting between MTCs relating to the patient populations or treatments included, duration of follow-up and outcomes of interest measured. The lack of similarity between the MTCs leads to questions about variability in the robustness of analyses and makes comparisons between studies challenging. CONCLUSION: Given the importance of MTCs for healthcare decision-making, it is imperative that reporting of methods, results and assumptions is clear and transparent to allow accurate interpretation of findings. For MTCs to be relevant, the choice of outcome measures should reflect clinical practice. Combination of treatments or of outcomes measured at different points of time should be avoided, as should imputation without justification. Furthermore, all approved treatment options should be included and updates of MTCs should be conducted when data for new treatments are published.

A Systematic Review and Mixed Treatment Comparison of Pharmaceutical Interventions for Multiple Sclerosis.

BACKGROUND: Since 2010, 27 mixed-treatment comparisons (MTCs) of disease-modifying therapies (DMTs) for multiple sclerosis have been published. However, there has been continued evolution in the field of MTCs. Additionally, limitations in methodological approach and reporting transparency, even in the most recent publications, makes interpretation and comparison of existing studies difficult. OBJECTIVES: The objectives of this study are twofold: (1) to estimate the efficacy and safety of DMTs at European Commission-approved doses compared with placebo in adults with relapsing-remitting multiple sclerosis (RRMS) using MTC, and (2) to identify and address methodological challenges when performing MTC in RRMS, thereby creating a baseline for comparisons with future treatments. METHODS: Searches were completed in 14 databases, including MEDLINE, Embase, CENTRAL, CDSR and DARE, from inception to June 2018 to identify published or unpublished prospective, randomised controlled trials of all European Union-approved DMTs or DMTs expected to be approved in the near future in RRMS or rapidly-evolving severe RRMS. No language or date restrictions were applied. Studies were included in the MTC if they were judged to have sufficiently similar characteristics, based on the following: patient age; proportion of male participants; Expanded Disability Status Scale (EDSS) score; duration of disease; number of relapses prior to enrolment and proportion of previously treated patients. Background information from the included studies, as well as effect size and confidence intervals (where relevant) of defined outcomes were extracted. Reporting of the MTC was consistent with the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) network meta-analysis guidelines. RESULTS: In total, 33 studies were included in the MTC. Annualised relapse rate (ARR 28 trials) was significantly reduced in all treatments compared with placebo. Alemtuzumab had the highest probability (63%) of being the most effective treatment in terms of ARR compared with placebo (rate ratio [RR] 0.28, 95% credible interval [CrI] 0.21-0.38), followed by natalizumab (30% probability; RR 0.32, 95% CrI 0.23-0.43). The risk of 3- and 6-month confirmed disability progression (CDP3M, 13 trials; CDP6M, 14 trials) were similar; CDP6M was significantly reduced for alemtuzumab (hazard ratio [HR] 0.365; 95% CrI 0.165-0.725), ocrelizumab (HR 0.405, 95% CrI 0.188-0.853) and natalizumab (HR 0.459, 95% CrI 0.252-0.840) relative to placebo. There were no significant differences in the odds of serious adverse events (SAEs, 6 trials) between any treatment and placebo. The results of the MTC were limited by the lack of studies reporting direct comparisons between the included treatments and by heterogeneous reporting of key outcome data. CONCLUSIONS: Meta-analyses confirmed the benefit of all DMTs in terms of relapse rate compared with placebo with a comparable rate of SAEs for the DMTs that could be included in the network. The rigor and transparency of reporting in this study provide a benchmark for comparisons with future new agents.

A simulation study to compare different estimation approaches for network meta-analysis and corresponding methods to evaluate the consistency assumption.

BACKGROUND: Network meta-analysis (NMA) is becoming increasingly popular in systematic reviews and health technology assessments. However, there is still ambiguity concerning the properties of the estimation approaches as well as for the methods to evaluate the consistency assumption. METHODS: We conducted a simulation study for networks with up to 5 interventions. We investigated the properties of different methods and give recommendations for practical application. We evaluated the performance of 3 different models for complex networks as well as corresponding global methods to evaluate the consistency assumption. The models are the frequentist graph-theoretical approach netmeta, the Bayesian mixed treatment comparisons (MTC) consistency model, and the MTC consistency model with stepwise removal of studies contributing to inconsistency identified in a leverage plot. RESULTS: We found that with a high degree of inconsistency none of the evaluated effect estimators produced reliable results, whereas with moderate or no inconsistency the estimator from the MTC consistency model and the netmeta estimator showed acceptable properties. We also saw a dependency on the amount of heterogeneity. Concerning the evaluated methods to evaluate the consistency assumption, none was shown to be suitable. CONCLUSIONS: Based on our results we recommend a pragmatic approach for practical application in NMA. The estimator from the netmeta approach or the estimator from the Bayesian MTC consistency model should be preferred. Since none of the methods to evaluate the consistency assumption showed satisfactory results, users should have a strong focus on the similarity as well as the homogeneity assumption.

The dark side of the force: Multiplicity issues in network meta-analysis and how to address them.

Standard models for network meta-analysis simultaneously estimate multiple relative treatment effects. In practice, after estimation, these multiple estimates usually pass through a formal or informal selection procedure, eg, when researchers draw conclusions about the effects of the best performing treatment in the network. In this paper, we present theoretical arguments as well as results from simulations to illustrate how such practices might lead to exaggerated and overconfident statements regarding relative treatment effects. We discuss how the issue can be addressed via multilevel Bayesian modelling, where treatment effects are modelled exchangeably, and hence estimates are shrunk away from large values. We present a set of alternative models for network meta-analysis, and we show in simulations that in several scenarios, such models perform better than the usual network meta-analysis model.

Characteristics and methods of incorporating randomized and nonrandomized evidence in network meta-analyses: a scoping review.

OBJECTIVES: The objective of the study was to conduct a scoping review of the published literature on methods used to combine randomized and nonrandomized evidence (NRE) in network meta-analyses (NMAs) and their respective characteristics. STUDY DESIGN AND SETTING: We conducted a scoping review using a list of NMAs which incorporated NRE that were identified from a previous review. All NMAs that included NRE in the analysis of main outcomes or sensitivity analyses were eligible for inclusion. Two reviewers independently screened studies for inclusion and performed data abstraction. Data analysis involved quantitative (frequencies and percentages) and qualitative (narrative synthesis) methods. RESULTS: A total of 23 NMAs met the predefined inclusion criteria, of which 74% (n = 17) used naïve pooling, 0% used NRE as informative priors, 9% (n = 2) used the 3-level Bayesian hierarchical model, 9% (n = 2) used all methods, and 9% (n = 2) used other methods. Most NMAs were supplemented with additional analyses to investigate the effect estimates when only randomized evidence was included. CONCLUSION: Although most studies provided justification for the inclusion of NRE, transparent reporting of the method used to combine randomized evidence and NRE was unclear in most published networks. Most NMAs used naïve pooling for combining randomized evidence and NRE.

A comparative review of network meta-analysis models in longitudinal randomized controlled trial.

Network meta-analysis (NMA) technique extends the standard meta-analysis methods, allowing pairwise comparison of all treatments in a network in the absence of head-to-head comparisons. Traditional NMA models consider a single endpoint for each trial. However, in many cases, trials in the network have different durations and/or report data at multiple time points. Moreover, these time points are often not the same for all trials. In this work, we review the most relevant methods that incorporate multiple time points and allow indirect comparisons of treatment effects across different longitudinal studies. In particular, we focus on the mixed treatment comparison developed by Dakin et al,[10] on the Bayesian evidence synthesis techniques-integrated two-component prediction developed by Ding et al,[11] and on the more recent method based on fractional polynomials by Jansen et al.[12] We highlight the main features of each model and illustrate them in simulations and in a real data application. Our study shows that methods based on fractional polynomials offer a flexible modeling strategy in most applications.

Network meta-analysis: application and practice using R software.

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.

Network meta-analysis of rare events using the Mantel-Haenszel method.

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.

Different light-activation systems associated with dental bleaching: a systematic review and a network meta-analysis.

OBJECTIVES: A systematic review and a network meta-analysis were performed to answer the following research question: "Is there any light-activation protocol capable of improving color change efficacy when associated with an in-office bleaching gel in adults?" MATERIAL AND METHODS: A search was performed in PubMed, Scopus, Web of Science, LILACS, BBO, Cochrane Library, and SIGLE without date and/or language restrictions in April 23, 2017 (updated on March 30, 2018). IADR abstracts (1990-2018), unpublished and ongoing trial registries, dissertations, and theses were also searched. Only randomized clinical trials conducted in adults that included at least one group treated with in-office dental bleaching with light activation were included. The risk of bias (RoB) was evaluated using the Cochrane Collaboration tool. A random-effects Bayesian-mixed treatment comparison (MTC) model was used to combine light-activated versus light-free in-office bleaching with direct light-free comparison trials. A meta-analysis with independent analysis (high- and low-concentrate hydrogen peroxide [HP]) was conducted for color change (∆E*, ∆SGU). RESULTS: After the removal of duplicates, title, and abstract screening, 28 studies remained. Nine were considered to be at a low RoB, five were at a high RoB, and the remaining were at an unclear RoB. The MTC analysis showed no significant difference in color change (ΔE* and ΔSGU) between light-activation protocols and light-free in-office bleaching, regardless of the HP concentration in the efficacy of the bleaching. CONCLUSION: No type of light-activated in-office bleaching was superior to light-free in-office bleaching for both high- and low-concentrate in-office bleaching gels (PROSPERO-CRD42017078743). CLINICAL RELEVANCE: Although many times dental professionals use "laser whitening" as a form of marketing, this study confirmed that no type of light-activation for in-office bleaching can improve the bleaching efficacy.

Comparative efficacy of antimicrobial treatments in dairy cows at dry-off to prevent new intramammary infections during the dry period or clinical mastitis during early lactation: a systematic review and network meta-analysis.

A systematic review and network meta-analysis were conducted to assess the relative efficacy of antimicrobial therapy given to dairy cows at dry-off. Eligible studies were controlled trials assessing the use of antimicrobials compared to no treatment or an alternative treatment, and assessed one or more of the following outcomes: incidence of intramammary infection (IMI) at calving, incidence of IMI during the first 30 days in milk (DIM), or incidence of clinical mastitis during the first 30 DIM. Databases and conference proceedings were searched for relevant articles. The potential for bias was assessed using the Cochrane Risk of Bias 2.0 algorithm. From 3480 initially identified records, 45 trials had data extracted for one or more outcomes. Network meta-analysis was conducted for IMI at calving. The use of cephalosporins, cloxacillin, or penicillin with aminoglycoside significantly reduced the risk of new IMI at calving compared to non-treated controls (cephalosporins, RR = 0.37, 95% CI 0.23-0.65; cloxacillin, RR = 0.55, 95% CI 0.38-0.79; penicillin with aminoglycoside, RR = 0.42, 95% CI 0.26-0.72). Synthesis revealed challenges with a comparability of outcomes, replication of interventions, definitions of outcomes, and quality of reporting. The use of reporting guidelines, replication among interventions, and standardization of outcome definitions would increase the utility of primary research in this area.

Comparative efficacy of antimicrobials for treatment of clinical mastitis in lactating dairy cattle: a systematic review and network meta-analysis.

A systematic review and network meta-analysis were conducted to assess the relative efficacy of antimicrobial therapy for clinical mastitis in lactating dairy cattle. Controlled trials in lactating dairy cattle with natural disease exposure were eligible if they compared an antimicrobial treatment to a non-treated control, placebo, or a different antimicrobial, for the treatment of clinical mastitis, and assessed clinical or bacteriologic cure. Potential for bias was assessed using a modified Cochrane Risk of Bias 2.0 tool. From 14775 initially identified records, 54 trials were assessed as eligible. Networks were established for bacteriologic cure by bacterial species group, and clinical cure. Disparate networks among bacteriologic cures precluded meta-analysis. Network meta-analysis was conducted for trials assessing clinical cure, but lack of precision of point estimates resulted in wide credibility intervals for all treatments, with no definitive conclusions regarding relative efficacy. Consideration of network geometry can inform future research to increase the utility of current and previous work. Replication of intervention arms and consideration of connection to existing networks would improve the future ability to determine relative efficacy. Challenges in the evaluation of bias in primary research stemmed from a lack of reporting. Consideration of reporting guidelines would also improve the utility of future research.

Comparative efficacy of teat sealants given prepartum for prevention of intramammary infections and clinical mastitis: a systematic review and network meta-analysis.

A systematic review and network meta-analysis were conducted to assess the relative efficacy of internal or external teat sealants given at dry-off in dairy cattle. Controlled trials were eligible if they assessed the use of internal or external teat sealants, with or without concurrent antimicrobial therapy, compared to no treatment or an alternative treatment, and measured one or more of the following outcomes: incidence of intramammary infection (IMI) at calving, IMI during the first 30 days in milk (DIM), or clinical mastitis during the first 30 DIM. Risk of bias was based on the Cochrane Risk of Bias 2.0 tool with modified signaling questions. From 2280 initially identified records, 32 trials had data extracted for one or more outcomes. Network meta-analysis was conducted for IMI at calving. Use of an internal teat sealant (bismuth subnitrate) significantly reduced the risk of new IMI at calving compared to non-treated controls (RR = 0.36, 95% CI 0.25-0.72). For comparisons between antimicrobial and teat sealant groups, concerns regarding precision were seen. Synthesis of the primary research identified important challenges related to the comparability of outcomes, replication and connection of interventions, and quality of reporting of study conduct.

Ranking crop species using mixed treatment comparisons.

The mixed treatment comparison (MTC) method has been proposed to combine results across trials comparing several treatments. MTC allows coherent judgments on which of the treatments is the most effective. It produces estimates of the relative effects of each treatment compared with every other treatment by pooling direct and indirect evidence. In this article, we explore how this methodological framework can be used to rank a large number of agricultural crop species from yield data collected in field experiments. Our approach is illustrated in a meta-analysis of yield data obtained in 67 field studies for 36 different bioenergy crop species. The considered dataset defines a network of comparisons of crop species. We introduce several Bayesian MTC models based on baseline treatment contrasts and evaluate the practical advantages of these models to produce yield ratio estimates. We explore the consistency of some estimates by node-splitting and compare our results to those obtained with a classical two-way linear mixed model. Results reveal that the model showing the lowest deviance information criterion (DIC) includes both study random effects and study-specific residual variances. But all the tested models including study random effects lead to similar yield ratio estimates. The proposed Bayesian framework allows an in-depth analysis of the uncertainty in the species ranking.