Using network meta-analysis to predict the percentage of missing participants for a future trial

Background Using evidence synthesis to design a clinical trial has long been advocated as the key against research waste. However, the relevant methodology does not deal with possible missing participants (MP) that may occur in a future trial. We illustrated the synergism of the baseline effects model and network meta-analysis (NMA) to predict the percentage of MP for a future trial. Methods We considered the network of a published systematic review as a case study. We applied the baseline effects model, followed by the relative effects model using Bayesian methods to predict the percentage of MP in each intervention when conducting NMA and a series of pairwise meta-analyses. We illustrated the posterior distribution of the predicted percentage MP under both synthesis methods alongside the MP reported in the corresponding trials for each intervention. Results Selecting different interventions for the baseline effects model yielded different predicted baseline effects and led to different predicted percentages of MP for the remaining interventions, highlighting the need to carefully pre-specifying the intervention for the baseline effects model. Both synthesis methods provided almost identical posterior distributions of predicted percentage MP for estimating similar summary odds ratios. There was great variability in the percentage of MP across the trials for each intervention, manifesting as considerable variability in the percentage difference in MP compared to NMA. Conclusions Incorporating predictions and absolute effects in the context of MP in NMA aids in determining the anticipated percentage of MP in the compared interventions to plan a future trial efficiently.