We need a comprehensive, up-to-date synthesis of evidence for all treatments available for a given disease

For many conditions, multiple competing treatments are available, many of which have been assessed in randomized trials. Clinicians and patients who are making medical decisions need to know which treatments work best among all treatments available for the condition of interest. They increasingly use meta-analyses that synthesize the results of randomized trials to inform the relative efficacy and safety of the different treatments.

But conventional meta-analyses do not provide an exhaustive up-to-date synthesis of all available treatments, and thus prevent from answering easily to the real questions of interest.

We propose to switch:

  • from a series of conventional meta-analyses focusing on specific treatments (many treatments being not considered), performed at a given time and frequently out-of-date
  • to a single systematic review and evidence synthesis (with meta-analyses and network meta-analyses) covering all treatments and systematically updated when new trial results become available

We call this approach “live cumulative network meta-analysis”.

network of NMA

Research is wasted when meta-analyses fail to provide a complete and up-to-date evidence synthesis

Conventional meta-analyses assess comparisons between only two treatments and thus focus on specific parts of the existing evidence. In a specific condition, second-line treatments of advanced non-small-cell lung cancer, we have shown that, when considered collectively, the 29 meta-analyses published from 2001 to 2015 did not encompass the whole available randomized evidence. In fact, more than 40% of treatments, of treatment comparisons and of trials were missing1.

An exhaustive synthesis could be provided by a network meta-analysis. This technique allows for comparing all treatments to each other, even if randomized controlled trials are not available for some treatment comparisons.

Another potential concern is that few meta-analyses are updated while, according to the clinical area, a meta-analysis may become quickly out-of-date. In a sample of 100 meta-analyses, about a quarter was out of date within two years of publication 2. In second-line treatments of advanced NSCLC, clinically important randomized evidence appears much more rapidly: on average, one new treatment is assessed in randomized trials every 3 months.

Methodological steps of live cumulative network meta-analysis

The principle is to switch from a series of disparate meta-analyses, which are frequently out-of-date and redundant, to a single systematic review and evidence synthesis (including meta-anlayses and network meta-analyses) of allavailable treatments, continuously updated for a specific condition. The end-product of this live cumulative network meta-analysis would not be an black or white answer to the initial question Which treatments work best? For example, the objective is not to provide only a ranking of the different treatments but rather to give access to an exhaustive up-to-date and transparent evidence base with meta-analyses and network meta-analyses for efficacy and safety outcomes.

The figure below shows the methodological steps for a live cumulative network meta-analysis. It is initiated with a network meta-analysis. Six methodological steps are then repeated at regular intervals to keep the network meta-analysis updated over time: adaptive search for treatments and trials, screening of reports and selection of trials, data extraction, assessment of risk of bias, update of the network of trials and evidence synthesis and lastly dissemination.

graph of live NMA

The iterations and the update frequency are designed to maintain the high-quality standards of the systematic review and to ensure that they are feasible. Iterations are performed with manual processes as currently recommended to ensure high quality.

An important aspect is the creation of a community of experts and its involvement at different steps of the process. Such a community will increase the clinical relevance, methodological validity and practical feasibility of the live cumulative network meta-analysis.

For a given condition, our proposal regarding this research community would be to involve different open groups of different skills. For instance, anyone interested in the condition targeted by the live cumulative network meta-analysis, including patients, would be able to spontaneously report new relevant treatments and trials. Experts in the given condition (clinicians, trialists or members of cooperative groups) from different countries would validate the methodological choices (e.g., selection criteria for populations, treatments and outcomes or subgroup analyses) and would be involved in the screening and selection of trials. Experts trained in systematic review methodology and experts in statistical methods would execute the core review steps.

Anyone willing to contribute to the development of live cumulative network meta-analysis can contact through "Contribute"

A proof of concept study is ongoing

We have started a live cumulative network meta-analysis of second-line treatments in advanced non-small cell lung cancer with wild-type or unknown status for epidermal growth factor receptor.3

We chose this topic because:

  • Non-small-cell lung cancer represents 85% of lung cancer and remains the leading cause of cancer-related death worldwide.
  • Four second-line treatments (docetaxel, pemetrexed, erlotinib and gefitinib) are recommended in patients with an advanced NSCLC. But more than forty second-line treatments have been evaluated over the past decade and the number is continuously increasing. A new treatment is assessed in randomized trials every 3 months.

This live cumulative network meta-analysis is performed in collaboration with the Cochrane Lung Cancer group

Anyone willing to contribute can contact us through "Contribute"


  1. Créquit P, Trinquart L, Yavchitz A, Ravaud P. Wasted research when systematic reviews fail to provide a complete and up-to-date evidence synthesis: the example of lung cancer. BMC Med. 2016 Jan 20;14(1):8. doi: 10.1186/s12916-016-0555-0. PubMed PMID: 26792360; PubMed Central PMCID: PMC4719540.
  2. Shojania KG, Sampson M, Ansari MT, Ji J, Doucette S, Moher D. How quickly do systematic reviews go out of date? A survival analysis. Ann Intern Med. 2007 Aug 21;147(4):224-33. Epub 2007 Jul 16. PubMed PMID: 17638714.
  3. Crequit P, Trinquart L, Ravaud P. Live cumulative network meta-analysis: protocol for second-line treatments in advanced non-small-cell lung cancer with wild-type or unknown status for epidermal growth factor receptor. BMJ Open 2016;6(8):e011841.