Random-effects meta-analysis of combined outcomes based on reconstructions of individual patient data.

Meta-analyses of clinical trials typically focus on one outcome at a time. However, treatment decision-making depends on an overall assessment of outcomes balancing benefit in various domains and potential risks. This calls for meta-analysis methods for combined outcomes that encompass information from different domains. When individual patient data (IPD) are available from all studies, combined outcomes can be calculated for each individual and standard meta-analysis methods would apply. However, IPD are usually difficult to obtain. We propose a method to estimate the overall treatment effect for combined outcomes based on first reconstructing pseudo IPD from available summary statistics and then pooling estimates from multiple reconstructed datasets. We focus on combined outcomes constructed from two continuous original outcomes. The reconstruction step requires the specification of the joint distribution of these two original outcomes, including the correlation which is often unknown. For outcomes that are combined in a linear fashion, misspecifications of this correlation affect efficiency, but not consistency, of the resulting treatment effect estimator. For other combined outcomes, an accurate estimate of the correlation is necessary to ensure the consistency of treatment effect estimates. To this end, we propose several ways to estimate this correlation under different data availability scenarios. We evaluate the performance of the proposed methods through simulation studies and apply these to two examples: (a) a meta-analysis of dipeptidyl peptidase-4 inhibitors vs control on treating type 2 diabetes; and (b) a meta-analysis of positive airway pressure therapy vs control on lowering blood pressure among patients with obstructive sleep apnea.