New GRADE guidance on assessing imprecision in a network meta-analysis

Imprecision is one of the major domains of the GRADE framework and is used to assess whether to rate down the certainty of evidence related to an outcome of interest. In a traditional ("pairwise") meta-analysis which compares two intervention groups, exposures, or tests against one another, two considerations are made: the confidence interval around the absolute estimate of effect, and the optimal information size (OIS). If the bounds of the confidence interval cross a threshold for a meaningful effect, and/or if optimal information size given the sample size in the meta-analysis is not met, then one should consider rating down for imprecision.

In the context of small sample sizes, confidence intervals around an effect may be fragile - meaning they could be changed substantially with additional information. Therefore, the consideration of OIS along with the bounds of the confidence interval helps address this concern when rating the certainty of evidence to develop a clinical recommendation. This is typically done by assessing whether the sample size of the meta-analysis meets that determined by a traditional power analysis for a given effect size.

However, in a network meta-analysis, both direct and indirect comparisons are made across various interventions or tests. Thus, especially if the inclusion of indirect comparisons changes the overall estimate of effect, considering only the sample size involved in the direct comparisons would be misleading. 

A new GRADE guidance paper lays out how to assess imprecision in the context of a network meta-analysis:

• If the 95% confidence interval crosses a decision-making threshold, rate down for imprecision. Thresholds should be ideally set a priori. It may be considered to rate down by two or even three levels depending on the degree of imprecision and the resulting communication of the certainty of evidence. For example, if imprecision is the only concern for an outcome, rating down by two instead of one level would be the difference between saying that a certain intervention or test "likely" or "probably" increases or decreases a given outcome, versus whether it simply "may" have this effect.
• If the 95% confidence interval does not cross a decision-making threshold, consider whether the effect size may be inflated. If a point estimate is far away enough from a threshold, even a relatively wide CI may not cross it. Further, relatively large effect sizes from smaller pools of evidence can be reduced with future research. 
• In the case of a large effect size, consider whether OIS is met. If the number of patients contributing to a NMA does not meet this number, consider rating down by one, two, or three levels depending on the severity of the width of the CI. 
• If the upper-limit of a confidence interval using relative risk is 3 or more times higher than the lower-limit, OIS has likely not been met. Similarly, upper-to-lower-limit comparisons of odds ratios exceeding 2.5 have likely not met OIS.
• Alternatively, when the effect size is both modest, plausible, and does not cross a threshold, one likely does not need to rate down for imprecision. 
• Avoid "double dinging" for imprecision if this limitation has already been addressed by rating down elsewhere.

Brignardello-Peterson R, Guyatt GH, Mustafa RA, et al. (2021). GRADE guidelines 33. Addressing imprecision in a network meta-analysis. J Clin Epidemiol (in-press). 

Manuscript available at the publisher's website here.

Help for Choosing Among Multiple Interventions Using GRADE

It is not uncommon for a health guideline to compare two or more interventions against one another. However, while sophisticated statistical approaches such as network meta-analyses allow us to compare these interventions head-to-head in terms of specified health outcomes, they do not take other important aspects of clinical decision-making into account, such as patient values and preferences, resource use, and equity considerations. A new paper from Piggott and colleagues aims to provide initial suggestions for using the GRADE evidence to decision (EtD) framework when choosing which of multiple interventions to recommend.

The authors identified a need for more direction when undertaking a multiple intervention comparison (MC) approach while working on recently released guidelines for the European Commission Initiative on Breast Cancer in which multiple screening intervals were compared against one another. Based on this experience, the group drafted a flexible yet transparency-minded framework to help guide similar efforts in the future, which was then added as a module in GRADE's official guideline development software, GRADEpro. 

The new module was pilot-tested for feasibility with several additional guidelines. The module allows the user to select and then compare multiple pairwise comparisons against one another (for instance, with one column for "Intervention 1 vs. Comparator 1" and "Intervention 2 vs. Comparator 2"). A five-star system is used to judge various components of the EtD, such as cost effectiveness, for each individual intervention and comparator, whereas a column on the right-hand side allows the user to input the relative importance of these components in decision-making.

Finally, the user can review all judgments across interventions and summatively recommend the most favorable intervention(s) overall.

Piggott T, Brozek J, Nowak A, et al. (2021). Using GRADE evidence to decision frameworks to choose from multiple interventions. J Clin Epidemiol 130:117-124.

Manuscript available from the publisher's website here.

Research Shorts: Borrowing of strength from indirect evidence

Contributed by M. Hassan Murad, M.D.

Network meta-analysis (NMA) is supposed to increase precision (because it includes more studies in the analysis); however, this is not always the case. This empirical study evaluated 915 possible treatment comparisons. The study used the recently proposed borrowing of strength statistic, which quantifies the percentage reduction in the uncertainty of the effect estimate when adding indirect evidence to an NMA

When only one study contributed direct evidence, NMAs resulted in reduced precision and no appreciable improvements in precision in 57.5% and 12.7% comparisons, respectively. When at least two studies contributed direct evidence, NMAs provided increased precision in 66.4% comparisons. The bottom-line is that, in sparse networks (i.e., networks that mostly consist of indirect evidence), NMA may not improve precision as much as stakeholders want and expect.

Reference: Lin L, Xing A, Kofler MJ, Murad MH. Borrowing of strength from indirect evidence in 40 network meta-analyses. Journal of clinical epidemiology. 2018 Oct 17.