Two new approaches for the visualisation of models for network meta-analysis

Physical activity improves the visual-spatial working memory of individuals with mild cognitive impairment or Alzheimer's disease: a systematic review and network meta-analysis

Objective

Our network meta-analysis aimed to ascertain the effect of physical activity on the visual-spatial working memory of individuals with mild cognitive impairment and Alzheimer's disease as well as to propose tailored exercise interventions for each group.

Methods

Employing a frequentist approach, we performed a network meta-analysis to compare the effectiveness of different exercise interventions in improving the visual-spatial working memory of individuals with mild cognitive impairment and Alzheimer's disease. Subsequently, we explored the moderating variables influencing the effectiveness of the exercise interventions through a subgroup analysis.

Results

We included 34 articles involving 3,074 participants in the meta-analysis, comprised of 1,537 participants from studies on mild cognitive impairment and 1,537 participants from studies on Alzheimer's disease. The articles included exhibited an average quality score of 6.6 (score studies) and 6.75 (reaction time [RT] studies), all passing the inconsistency test (p > 0.05). In the mild cognitive impairment literature, mind-body exercise emerged as the most effective exercise intervention (SMD = 0.61, 95% CI: 0.07-1.14). In Alzheimer's disease research, aerobic exercise was identified as the optimal exercise intervention (SMD = 0.39, 95% CI: 0.06-0.71).

Conclusion

The results of the subgroup analysis suggest that the most effective approach to enhancing the visual-spatial working memory of individuals with mild cognitive impairment entails exercising at a frequency of three or more times per week for over 60 min each time and at a moderate intensity for more than 3 months. Suitable exercise options include mind-body exercise, multicomponent exercise, resistance exercise, and aerobic exercise. For individuals with Alzheimer's disease, we recommend moderately intense exercise twice per week for over 90 min per session and for a duration of 3 months or longer, with exercise options encompassing aerobic exercise and resistance exercise.

Development and design validation of a novel network meta-analysis presentation tool for multiple outcomes: a qualitative descriptive study

OBJECTIVE

The Grades of Recommendations, Assessment, Development and Evaluation working group recently developed an innovative approach to interpreting results from network meta-analyses (NMA) through minimally and partially contextualised methods; however, the optimal method for presenting results for multiple outcomes using this approach remains uncertain. We; therefore, developed and iteratively modified a presentation method that effectively summarises NMA results of multiple outcomes for clinicians using this new interpretation approach.

DESIGN

Qualitative descriptive study.

SETTING

A steering group of seven individuals with experience in NMA and design validation studies developed two colour-coded presentation formats for evaluation. Through an iterative process, we assessed the validity of both formats to maximise their clarity and ease of interpretation.

PARTICIPANTS

26 participants including 20 clinicians who routinely provide patient care, 3 research staff/research methodologists and 3 residents.

MAIN OUTCOME MEASURES

Two team members used qualitative content analysis to independently analyse transcripts of all interviews. The steering group reviewed the analyses and responded with serial modifications of the presentation format.

RESULTS

To ensure that readers could easily discern the benefits and safety of each included treatment across all assessed outcomes, participants primarily focused on simple information presentations, with intuitive organisational decisions and colour coding. Feedback ultimately resulted in two presentation versions, each preferred by a substantial group of participants, and development of a legend to facilitate interpretation.

CONCLUSION

Iterative design validation facilitated the development of two novel formats for presenting minimally or partially contextualised NMA results for multiple outcomes. These presentation approaches appeal to audiences that include clinicians with limited familiarity with NMAs.

Interpreting and assessing confidence in network meta-analysis results: an introduction for clinicians

Purpose

We aimed to provide clinicians with introductory guidance for interpreting and assessing confidence in on Network meta-analysis (NMA) results.

Methods

We reviewed current literature on NMA and summarized key points.

Results

Network meta-analysis (NMA) is a statistical method for comparing the efficacy of three or more interventions simultaneously in a single analysis by synthesizing both direct and indirect evidence across a network of randomized clinical trials. It has become increasingly popular in healthcare, since direct evidence (head-to-head randomized clinical trials) are not always available. NMA methods are categorized as either Bayesian or frequentist, and while the two mostly provide similar results, the two approaches are theoretically different and require different interpretations of the results.

Conclusions

We recommend a careful approach to interpreting NMA results and the validity of an NMA depends on its underlying statistical assumptions and the quality of the evidence used in the NMA.

The Kilim plot: A tool for visualizing network meta-analysis results for multiple outcomes

Network meta‐analysis (NMA) can be used to compare multiple competing treatments for the same disease. In practice, usually a range of outcomes is of interest. As the number of outcomes increases, summarizing results from multiple NMAs becomes a nontrivial task, especially for larger networks. Moreover, NMAs provide results in terms of relative effect measures that can be difficult to interpret and apply in every‐day clinical practice, such as the odds ratios. In this article, we aim to facilitate the clinical decision‐making process by proposing a new graphical tool, the Kilim plot, for presenting results from NMA on multiple outcomes. Our plot compactly summarizes results on all treatments and all outcomes; it provides information regarding the strength of the statistical evidence of treatment effects, while it illustrates absolute, rather than relative, effects of interventions. Moreover, it can be easily modified to include considerations regarding clinically important effects. To showcase our method, we use data from a network of studies in antidepressants. All analyses are performed in R and we provide the source code needed to produce the Kilim plot, as well as an interactive web application.

Utilizing radar graphs in the visualization of simulation and estimation results in network meta-analysis

Traditional visualization in meta-analysis uses forest plots to illustrate the combined treatment effect, along with the respective results from primary trials. While the purpose of visualization is clear in the pairwise setting, additional treatments broaden the focus and extend the results to be illustrated in network meta-analysis. The complexity increases further in situations where all potential contrasts in the network are compared to a predefined fixed value of interest, such as the 95% coverage evaluated against the nominal value of 95% in simulation studies. We propose utilizing radar graphs to illustrate results from network meta-analysis in cases where the interest lies in the comparison of estimated results (after fitting a network meta-analysis in a specific data set) or a performance measure (simulation study) to a pre-defined fixed reference value. Accounting for the complex high-dimensional data structure, the general picture of the full network is captured at once without increasing the space needed for visualization. Especially in large simulation studies, where multiple scenarios need to be visually combined to gain an overview on different scenarios, this type of illustration facilitates the discussion of results. Further properties, such as the expected variation due to the Monte-Carlo error or the differentiation between directly and indirectly estimated treatment contrasts in simulation studies, as well as the indication of well-connected and sparsely connected treatments in an applied network meta-analysis, can additionally be included in the visualization. While we used the radar-graph mainly for a simulation study, other applications are suitable whenever relative contributions of treatment (contrasts) are of interest.