Planning future studies based on the precision of network meta-analysis results.

When there are multiple competing interventions for a healthcare problem, the design of new studies could be based on the entire network of evidence as reflected in a network meta-analysis. There is a practical need to answer how many (if any) studies are needed, of which design (i.e., which treatments to compare), and with what sample size to infer conclusively about the relative treatment effects of a set of target or all competing treatments and their relative ranking. We consider the precision in the results obtained from network meta-analysis: the precision of the joint distribution of the estimated basic parameters of the model and the precision in the treatment ranking. We quantify the precision in the estimated effects by considering their variance-covariance matrix and estimate the precision in ranking by quantifying the dissimilarity of the density functions of summary effect estimates. Then, based on a desirable improvement in precision, we calculate the required sample size for each possible study design and number of study arms, and we present visual tools that can help trialists select the optimal study design. We use a published network of interventions for the treatment of hepatocellular carcinoma to illustrate the suggested methodology. The presented methodology can aid investigators making informed and evidence-based decisions about planning new studies.