In light of increasing attention towards the use of Real-World Evidence (RWE) in decision making in recent years, this commentary aims to reflect on the experiences gained in accessing and using RWE for Comparative Effectiveness Research (CER) as part of the Innovative Medicines Initiative GetReal Consortium (IMI-GetReal) and discuss their implications for RWE use in decision-making. For the purposes of this commentary, we define RWE as evidence generated based on health data collected outside the context of RCTs. Meanwhile, we define Comparative Effectiveness Research (CER) as the conduct and/or synthesis of research comparing different benefits and harms of alternative interventions and strategies to prevent, diagnose, treat, and monitor health conditions in routine clinical practice (i.e. the real-world setting). The equivalent term for CER as used in the European context of Health Technology Assessment (HTA) and decision making is Relative Effectiveness Assessment (REA).

Background: The objective of this systematic review is to investigate the use of Bayesian data analysis in epidemiology in the past decade, and particularly to evaluate the quality of research papers reporting the results of these analyses.

Methods: Complete volumes of five major epidemiological journals in the period 2005-2015 were searched via Pubmed. In addition we performed an extensive within-manuscript search using a specialized Java application. Details of reporting on Bayesian statistics were examined in original research papers with primary Bayesian data analyses.

Results: The number of studies in which Bayesian techniques were used for primary data analysis remain constant over the years. Though many authors presented thorough descriptions of the analyses they performed and the results they obtained, several reports presented incomplete method sections, and even some incomplete results sections. Especially, information on the process of prior elicitation, specification and evaluation was often lacking.

Conclusions: Though available guidance papers concerned with reporting of Bayesian analyses emphasize the importance of transparent prior specification, the results obtained in this systematic review show that these guidance papers are often not used. Additional efforts should be made to increase the awareness of the existence and importance of these checklists in order to overcome the controversy with respect to the use of Bayesian techniques. The reporting quality in epidemiological literature could be improved by updating existing guidelines on the reporting of frequentist analyses to address issues that are important for Bayesian data analyses.