To optimize patient outcomes, healthcare decisions should be based on the most up-to-date high-quality evidence. Randomized controlled trials (RCTs) are vital for demonstrating the efficacy of interventions; however, information on how an intervention compares to already available treatments and/or fits into treatment algorithms is sometimes limited. Although different therapeutic classes are available for the treatment of chronic obstructive pulmonary disease (COPD), assessing the relative efficacy of these treatments is challenging. Synthesizing evidence from multiple RCTs via meta-analysis can help provide a comprehensive assessment of all available evidence and a "global summary" of findings. Pairwise meta-analysis is a well-established method that can be used if two treatments have previously been examined in head-to-head clinical trials. However, for some comparisons, no head-to-head studies are available, for example the efficacy of single-inhaler triple therapies for the treatment of COPD. In such cases, network meta-analysis (NMA) can be used, to indirectly compare treatments by assessing their effects relative to a common comparator using data from multiple studies. However, incorrect choice or application of methods can hinder interpretation of findings or lead to invalid summary estimates. As such, the use of the GRADE reporting framework is an essential step to assess the certainty of the evidence. With an increasing reliance on NMAs to inform clinical decisions, it is now particularly important that healthcare professionals understand the appropriate usage of different methods of NMA and critically appraise published evidence when informing their clinical decisions. This review provides an overview of NMA as a method for evidence synthesis within the field of COPD pharmacotherapy. We discuss key considerations when conducting an NMA and interpreting NMA outputs, and provide guidance on the most appropriate methodology for the data available and potential implications of the incorrect application of methods. We conclude with a simple illustrative example of NMA methodologies using simulated data, demonstrating that when applied correctly, the outcome of the analysis should be similar regardless of the methodology chosen.
Keywords: Bayesian; Bucher ITC; Chronic obstructive pulmonary disease; Frequentist; GRADE; Head-to-head comparison; Indirect treatment comparison; Network meta-analysis; Randomized controlled trials; Single-inhaler triple therapy.
There are several different treatments available for chronic obstructive pulmonary disease (COPD). Finding out which of these treatments is the most effective is difficult, especially if conflicting results from clinical trials have been reported, or if treatments have never been directly compared to each other. Meta-analysis allows the results from multiple studies to be combined together to give a single summary of findings. This can be useful in cases where previous trials have shown contradictory findings. However, this method can only be used if there is more than one study looking at the same two treatments (e.g., several studies that compared treatment A to treatment B). For treatments that have never been compared in clinical trials, network meta-analysis (NMA) can be used. This method allows several treatments to be compared at the same time using the results from trials comparing different treatments. This method creates ‘indirect evidence’. Indirect evidence refers to cases where two treatments have never been directly compared to each other in a clinical study, but both have been separately compared to a common treatment (e.g., treatment A and treatment C have never been directly compared to each other, but both have been separately compared to treatment B in a clinical study). NMA can be carried out using different methods. However, if the correct method is not chosen, this can lead to inaccurate results. It is becoming more common for NMA findings to be used to help make clinical decisions. Therefore, it is important that healthcare professionals are able to assess the results of published NMAs, including the methods used, to find the most appropriate results to support their clinical decisions. This tutorial provides an overview of different NMA methods, with a focus on the use of these methods within the context of COPD treatments. We also present an example where we use various NMA methods on the same data set to show that different methods should lead to similar results if the methods are used correctly.
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