The Impact of Excluding Trials from Network Meta-Analyses - An Empirical Study

Comparative Effectiveness of Different Exercises for Reducing Pain Intensity in Primary Dysmenorrhea: A Systematic Review and Network Meta-analysis of Randomized Controlled Trials

BACKGROUND

Studies have demonstrated that exercise can mitigate the intensity of menstrual pain in primary dysmenorrhea, but the most effective type of exercise remains unclear. The objective of this systematic review and network meta-analysis was to evaluate the effectiveness of different exercise regimens in reducing pain associated with primary dysmenorrhoea.

METHODS

Randomized controlled trials investigating the relationship between menstrual pain and exercise were selected from major electronic databases until February 2, 2024. The primary outcome was the effect of exercise on pain intensity measured by the mean difference on a 10-cm visual analogue scale at 4 and 8 weeks after intervention. The secondary outcome was the difference in risk of dropout at 8 weeks. The study protocol was registered as INPLASY202330050.

RESULTS

This systematic review and network meta-analysis included 29 randomized controlled trials, which involved 1808 participants with primary dysmenorrhea. Exercise interventions included relaxation exercise, strength training, aerobic activity, yoga, mixed exercise, and the Kegel maneuver. Relaxation exercise was the most effective in reducing menstrual pain in 4 weeks (- 3.56; 95% confidence interval: - 5.03 to - 2.08). All exercise interventions were effective in reducing menstrual pain at 8 weeks, with reductions ranging from - 3.87 (95% CI - 5.51 to - 2.22) for relaxation exercise to - 2.75 (95% CI - 4.00 to - 1.51) for yoga, compared to the control group. Relaxation exercises were found to have a significantly lower dropout risk (- 0.11; 95% CI  - 0.20 to 0.02), while none of the exercise types was associated with a higher dropout risk than the control group.

CONCLUSION

All exercise interventions were effective in reducing menstrual pain in primary dysmenorrhea after 8 weeks of intervention. However, relaxation exercise was found to be the most effective intervention at 4 and 8 weeks and had the lowest risk of dropout.

The Sound of Memory: Investigating Music Therapy's Cognitive Benefits in Patients with Dementia-A Network Meta-Analysis of Randomized Controlled Trials

Numerous previous studies have shown the effectiveness of music therapy in enhancing cognitive functions in patients with dementia. Despite this, robust evidence in this field, especially concerning the comparison of different music therapy types, is lacking. Therefore, randomized controlled trials (RCTs) focusing on music therapy and cognitive functions in dementia patients, termed by "music" AND "dementia" OR "Alzheimer's disease" AND "cognitive", were identified from primary electronic databases to conduct this network meta-analysis (NMA). The primary outcome focused on the impact on cognitive functions, and the secondary outcome was the comparison of dropout rates between the intervention groups and the usual care control groups. Standardized mean difference (SMD) values and the corresponding 95% confidence intervals (CIs) were computed for effect evaluation. This study protocol has been registered in IPLASY (INPLASY202430082). A total of 14 RCTs with 1056 participants were enrolled, examining interventions including Active Music Therapy (AMT), Active Music Therapy with Singing (AMT + Sing), Rhythmic Music Therapy (RMT), Listening to Music (LtM), and Singing (Sing). The results indicated that RMT, AMT + Sing, and AMT all significantly improve cognitive functions in dementia patients, of which the SMD were 0.76 (95% CI = 0.32-1.21), 0.79 (95% CI = 0.03-1.49), and 0.57 (0.18-0.96), respectively. Compared with the control group (usual care), no music therapy type was associated with an increased dropout risk. In conclusion, music therapy can improve cognitive functions in patients with dementia without increasing the risk of dropout, particularly RMT, AMT + Sing, and AMT.

Multifaceted Music Therapy for Depression in Dementia: A Network Meta-Analysis of Randomized Controlled Trials

This study aimed to assess the efficacy of various music therapy interventions in ameliorating depressive symptoms in dementia patients, utilizing a network meta-analysis approach. We rigorously selected randomized controlled trials focused on music therapy for dementia with depressive symptoms from major electronic databases. The primary outcome measured was the impact on depressive symptoms, with the secondary outcome evaluating dropout rates across different intervention groups and standard care control groups. The research protocol has been duly registered with PROSPERO (Registration ID: CRD42023393059). Our network meta-analysis incorporated 14 randomized controlled trials involving a total of 1080 participants and examined a range of interventions, including active music therapy, listening to music, rhythmic music therapy, singing, and tailored music interventions. The analysis revealed that active music therapy combined with singing emerged as the most effective intervention, demonstrating a significant improvement in depressive symptoms in dementia patients (Standardized Mean Difference [SMD] = -0.89, 95% Confidence Interval [CI]: -1.48 to -0.30). In contrast, listening to music alone showed a smaller effect (SMD = -0.26, 95% CI: -0.71 to 0.20). This study was particularly noteworthy for not showing higher dropout rates compared to standard care, indicating its feasibility and acceptability in clinical settings. The findings of our study indicate that active music therapy combined with singing is an effective approach to reducing depressive symptoms in dementia patients, potentially due to enhanced social interaction. These results offer new perspectives for dementia care, suggesting a promising direction for further research and clinical application.

Safety and Effectiveness of Antidysrhythmic Drugs for Pharmacologic Cardioversion of Recent-Onset Atrial Fibrillation: a Systematic Review and Bayesian Network Meta-analysis

PURPOSE

The available evidence to determine which antidysrhythmic drug is superior for pharmacologic cardioversion of recent-onset (onset within 48 h) atrial fibrillation (AF) is uncertain. We aimed to identify the safest and most effective agent for pharmacologic cardioversion of recent-onset AF in the emergency department.

METHODS

We searched MEDLINE, Embase, and Web of Science from inception to February 21, 2023 (PROSPERO: CRD42018083781). Eligible studies were randomized controlled trials that enrolled adult participants with AF ≤ 48 h, compared a guideline-recommended antidysrhythmic drug with another antidysrhythmic drug or a different formulation of the same drug or placebo and reported specific adverse events. The primary outcome was immediate, serious adverse event - cardiac arrest, sustained ventricular tachydysrhythmia, atrial flutter 1:1 atrioventricular conduction, hypotension, and bradycardia. Additional analyses included the outcomes of conversion to sinus rhythm within 4 h and 24 h. We extracted data according to PRISMA-NMA and appraised trials using Cochrane RoB 2. We performed Bayesian network meta-analysis (NMA) using a Markov Chain Monte Carlo method with random-effect model and vague prior distribution to calculate odds ratios with 95% credible intervals. We assessed confidence using CINeMA. We used surface under the cumulative ranking curve (SUCRA) to rank agent(s).

RESULTS

The systematic review initially identified 5545 studies. Twenty-five studies met eligibility criteria, and 22 studies (n = 3082) provided data for NMA, which demonstrated that vernakalant (SUCRA = 70.9%) is most likely to be safest. Additional effectiveness NMA demonstrated that flecainide (SUCRA = 89.0%) is most likely to be superior for conversion within 4 h (27 studies; n = 2681), and ranolazine-amiodarone IV (SUCRA 93.7%) is most likely to be superior for conversion within 24 h (24 studies; n = 3213). Confidence in the NMA estimates is variable and limited mostly by within-study bias and imprecision.

CONCLUSIONS

Among guideline-recommended antidysrhythmic drugs, the combination of digoxin IV and amiodarone IV is definitely among the least safe for cardioversion of recent onset AF; flecainide, vernakalant, ibutilide, propafenone, and amiodarone IV are definitely among the most effective for cardioversion within 4 h; flecainide is definitely among the most effective for cardioversion within 24 h. Further, randomized controlled trials with predetermined and strictly defined, hemodynamic adverse event outcomes are recommended.

Differential Impact of Exercises on Quality-of-Life Improvement in Breast Cancer Survivors: A Network Meta-Analysis of Randomized Controlled Trials

This study aimed to assess the effectiveness of various exercise interventions in enhancing the quality of life for breast cancer survivors. To achieve this, randomized controlled trials were identified from major electronic databases, focusing on the relationship between exercise and quality of life in breast cancer survivors. The primary outcome was the impact of exercise on quality of life 12 weeks after the intervention, with a secondary outcome comparing dropout rates between intervention groups and a regular care control group. The study protocol was registered with INPLASY (INPLASY202340007). A network meta-analysis of nine randomized controlled trials involving 725 participants was conducted, examining aerobic and strength training, aerobic activity, yoga, and strength exercise. Results showed that aerobic and strength training was the most effective intervention, significantly improving the quality of life of breast cancer survivors (1.31; 95% confidence interval: 0.49 to 2.12). Aerobic activity had a borderline effect (0.83; 0.03 to 1.63), while no exercise interventions were associated with an increased dropout risk compared to the control group (regular care). The study concluded that concurrent aerobic and strength training can improve breast cancer survivors' quality of life after 12 weeks of intervention without increasing dropout risk compared to regular care.

Crossroads of methodological choices in research synthesis: insights from two network meta-analyses on preventing relapse in schizophrenia

In recent years, network meta-analyses have been increasingly carried out to inform clinical guidelines and policy. This approach is under constant development, and a broad consensus on how to carry out several of its methodological and statistical steps is still lacking. Therefore, different working groups might often make different methodological choices based on their clinical and research experience, with possible advantages and shortcomings. In this contribution, we will critically assess two network meta-analyses on the topic of pharmacological prevention of relapse in schizophrenia, carried out by two different research groups. We will highlight the implications of different methodological choices on the analysis results and their clinical-epidemiological interpretation. Moreover, we will discuss some of the most relevant technical issues of network meta-analyses for which there is not a broad methodological agreement, including the assessment of transitivity.

Network Meta-Analysis Techniques for Synthesizing Prevention Science Evidence

Network meta-analysis is a popular statistical technique for synthesizing evidence from studies comparing multiple interventions. Benefits of network meta-analysis, over more traditional pairwise meta-analysis approaches, include evaluating efficacy/safety of interventions within a single framework, increased precision, comparing pairs of interventions that have never been directly compared in a trial, and providing a hierarchy of interventions in terms of their effectiveness. Network meta-analysis is relatively underutilized in prevention science. This paper therefore presents a primer of network meta-analysis for prevention scientists who wish to apply this method or to critically appraise evidence from publications using the method. We introduce the key concepts and assumptions of network meta-analysis, namely, transitivity and consistency, and demonstrate their applicability to the field of prevention science. We then illustrate the method using a network meta-analysis examining the comparative effectiveness of brief alcohol interventions for preventing hazardous drinking among college students. We provide data and code for all examples. Finally, we discuss considerations that are particularly relevant in network meta-analyses in the field of prevention, such as including non-randomized evidence.

A Bayesian approach to discrete multiple outcome network meta-analysis

In this paper we suggest a new Bayesian approach to network meta-analysis for the case of discrete multiple outcomes. The joint distribution of the discrete outcomes is modeled through a Gaussian copula with binomial marginals. The remaining elements of the hierarchial random effects model are specified in a standard way, with the logit of the success probabilities given by the sum of a baseline log-odds and random effects comparing the log-odds of each treatment against the reference and having a Gaussian distribution centered at the vector of pooled effects. An adaptive Markov Chain Monte Carlo algorithm is devised for running posterior inference. The model is applied to two datasets from Cochrane reviews, already analysed in two papers so to assess and compare its performance. We implemented the model in a freely available R package called netcopula.

Bayesian Network Meta-analysis of Multiple Outcomes in Dental Research

OBJECTIVES

Dental research typically targets multiple outcomes. Interdental cleaning devices such as interdental brushes (IB) and water jet devices (WJ) share a sizable portion of the medical device market. However, recommendations for device selection are limited by the conflicting evidence from multiple outcomes in available studies and the lack of an appropriate synthesis approach to summarize evidences taken from multiple outcomes. In particular, both pairwise meta-analyses and single-outcome network meta-analyses can give discordant results. The purpose of this multioutcome, Bayesian network meta-analysis is to introduce this innovative method to the dental research community using data from interdental cleaning device studies for illustrative purposes.

METHODS

We reanalyzed a network meta-analysis of interproximal oral hygiene methods in the reduction of clinical indices of inflammation, which included 22 trials assessing 10 interproximal oral hygiene aids. We focused on the primary outcome of gingival inflammation, which was measured by 2 correlated outcome variables, the Gingival Index (GI) and bleeding on probing (BOP).

RESULTS

In our previous single-outcome analysis, we concluded that IB and WJ rank high for reducing gingival inflammation while toothpick and flossing rank last. In this multioutcome Bayesian network meta-analysis with equal weight on GI and BOP, the surface under the cumulative ranking curve was 0.87 for WJ and 0.85 for IB. WJ and IB remained ranked as the 2 best devices across different sets of weightings for the GI and BOP.

CONCLUSION

In conclusion, multioutcome Bayesian network meta-analysis naturally takes the correlations among multiple outcomes into account, which in turn can provide more comprehensive evidence.

Empirical evaluation of SUCRA-based treatment ranks in network meta-analysis: quantifying robustness using Cohen's kappa

OBJECTIVE

To provide a framework for quantifying the robustness of treatment ranks based on Surface Under the Cumulative RAnking curve (SUCRA) in network meta-analysis (NMA) and investigating potential factors associated with lack of robustness.

METHODS

We propose the use of Cohen's kappa to quantify the agreement between SUCRA-based treatment ranks estimated through NMA of a complete data set and a subset of it. We illustrate our approach using five published NMA data sets, where robustness was assessed by removing studies one at a time.

RESULTS

Overall, SUCRA-based treatment ranks were robust to individual studies in the five data sets we considered. We observed more incidences of disagreement between ranks in the networks with larger numbers of treatments. Most treatments moved only one or two ranks up or down. The lowest quadratic weighted kappa estimate observed across all networks was in the network with the smallest number of treatments (4), where weighted kappa=40%. In the network with the largest number of treatments (12), the lowest observed quadratic weighted kappa=89%, reflecting a small shift in this network's treatment ranks overall. Preliminary observations suggest that a study's size, the number of studies making a treatment comparison, and the agreement of a study's estimated treatment effect(s) with those estimated by other studies making the same comparison(s) may explain the overall robustness of treatment ranks to studies.

CONCLUSIONS

Investigating robustness or sensitivity in an NMA may reveal outlying rank changes that are clinically or policy-relevant. Cohen's kappa is a useful measure that permits investigation into study characteristics that may explain varying sensitivity to individual studies. However, this study presents a framework as a proof of concept and further investigation is required to identify potential factors associated with the robustness of treatment ranks using more extensive empirical evaluations.

Quantifying and presenting overall evidence in network meta-analysis

Network meta-analysis (NMA) has become an increasingly used tool to compare multiple treatments simultaneously by synthesizing direct and indirect evidence in clinical research. However, many existing studies did not properly report the evidence of treatment comparisons and show the comparison structure to audience. In addition, nearly all treatment networks presented only direct evidence, not overall evidence that can reflect the benefit of performing NMAs. This article classifies treatment networks into three types under different assumptions; they include networks with each treatment comparison's edge width proportional to the corresponding number of studies, sample size, and precision. In addition, three new measures (ie, the effective number of studies, the effective sample size, and the effective precision) are proposed to preliminarily quantify overall evidence gained in NMAs. They permit audience to intuitively evaluate the benefit of performing NMAs, compared with pairwise meta-analyses based on only direct evidence. We use four case studies, including one illustrative example, to demonstrate their derivations and interpretations. Treatment networks may look fairly differently when different measures are used to present the evidence. The proposed measures provide clear information about overall evidence of all treatment comparisons, and they also imply the additional number of studies, sample size, and precision obtained from indirect evidence. Some comparisons may benefit little from NMAs. Researchers are encouraged to present overall evidence of all treatment comparisons, so that audience can preliminarily evaluate the quality of NMAs.

Bayesian hierarchical methods for meta-analysis combining randomized-controlled and single-arm studies

Meta-analysis of interventions usually relies on randomized controlled trials. However, when the dominant source of information comes from single-arm studies, or when the results from randomized controlled trials lack generalization due to strict inclusion and exclusion criteria, it is vital to synthesize both sources of evidence. One challenge of synthesizing both sources is that single-arm studies are usually less reliable than randomized controlled trials due to selection bias and confounding factors. In this paper, we propose a Bayesian hierarchical framework for the purpose of bias reduction and efficiency gain. Under this framework, three methods are proposed: bivariate generalized linear mixed effects models, hierarchical power prior model and hierarchical commensurate prior model. Design difference and potential biases are considered in all models, within which the hierarchical power prior and hierarchical commensurate prior models further offer to downweight single-arm studies flexibly. The hierarchical commensurate prior model is recommended as the primary method for evidence synthesis because of its accuracy and robustness. We illustrate our methods by applying all models to two motivating datasets and evaluate their performance through simulation studies. We finish with a discussion of the advantages and limitations of our methods, as well as directions for future research in this area.