Meta‐analysis combining parallel and cross‐over trials with random effects

Meta-analysis can necessitate the combination of parallel and cross-over trial designs. Because of the differences in the trial designs and potential biases notably associated with the crossover trials, one often combines trials of the same designs only, which decreases the power of the meta-analysis. To combine results of clinical trials from parallel and cross-over designs, we extend the method proposed in an accompanying study to account for random effects. We propose here a hierarchical mixed model allowing the combination of the 2 types of trial designs and accounting for additional covariates where random effects can be introduced to account for heterogeneity in trial, treatment effect, and interactions. We introduce a multilevel model and a Bayesian hierarchical model for combined trial design meta-analysis. The analysis of the models by restricted iterative generalised least square and Monte Carlo Markov Chain is presented. Methods are compared in a combined design meta-analysis model on salt reduction. Both models and their respective advantages in the perspective of meta-analysis are discussed. However, the access to the trial data, in particular sequence and period data in cross-over trials, remains a major limitation to the meta-analytic combination of trial designs.