Hierarchical Bayesian methods for multiple outcomes in mixed treatment comparisons

Abstract

Biomedical decision makers confronted with questions about the comparative effectiveness and safety of interventions often wish to combine all sources of data. Such multiple treatment comparisons (MTCs) may or may not include head-to-head randomized controlled trials of the treatments of primary interest, instead relying largely on indirect comparisons (say, trials that separately compare each treatment to placebo). In such settings, hierarchical Bayes-MCMC meta-analytic methods are becoming more popular due to their flexibility and interpretability. Compared to frequentist approaches, Bayesian methods cope better with complex data structures and models, and produce estimates and measures of uncertainty that are generally better at capturing all sources of uncertainty in the data. In addition, the Bayesian approach helps to make sound decisions based on probabilities that each treatment is best overall, through a weighted scoring rule that trades off efficacy and safety. Many randomized clinical trials report multiple outcomes with possible inherent correlations. Moreover, MTC data are typically sparse (though richer than standard meta-analysis data, comparing only two treatments) and researchers often choose study arms based on previous trials. In this dissertation, we summarize existing hierarchical Bayesian methods for MTCs with a single outcome, and introduce novel Bayesian approaches for analyzing multiple outcomes simultaneously, rather than in separate MTC analyses. We incorporate missing data and the correlation structure between outcomes through contrast- and arm-based parameterizations that consider any unobserved treatment arms as missing data to be imputed. We also extend the model to apply to all types of generalized linear model outcomes, such as count or continuous responses, and mixed-type outcomes, such as paired continuous efficacy and binary safety responses. Finally, availability of individual patient-level data (IPD) broadens the scope of MTCs, and enables us to incorporate patient-level clinical characteristics. We close the dissertation by developing arm-based IPD MTC models which offer more straightforward interpretation and application compared to the existing contrast-based IPD MTC models.