Continuously updated network meta-analysis and statistical monitoring for timely decision-making

Abstract

Pairwise and network meta-analysis (NMA) are traditionally used retrospectively to assess existing evidence. However, the current evidence often undergoes several updates as new studies become available. In each update recommendations about the conclusiveness of the evidence and the need of future studies need to be made. In the context of prospective meta-analysis future studies are planned as part of the accumulation of the evidence. In this setting, multiple testing issues need to be taken into account when the meta-analysis results are interpreted. We extend ideas of sequential monitoring of meta-analysis to provide a methodological framework for updating NMAs. Based on the z-score for each network estimate (the ratio of effect size to its standard error) and the respective information gained after each study enters NMA we construct efficacy and futility stopping boundaries. A NMA treatment effect is considered conclusive when it crosses an appended stopping boundary. The methods are illustrated using a recently published NMA where we show that evidence about a particular comparison can become conclusive via indirect evidence even if no further trials address this comparison.