Charlotte Rietbergen1, Thomas P A Debray2, Irene Klugkist3, Kristel J M Janssen4, Karel G M Moons4. 1. Department of Methodology and Statistics, Utrecht University, Padualaan 14, Utrecht 3584 CH, The Netherlands. Electronic address: c.rietbergen@uu.nl. 2. Department of Epidemiology, Julius Center for Health Sciences and Primary Care, UMC Utrecht, Huispost Str. 6.131, PO Box 85500, Utrecht 3508 GA, The Netherlands; Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, UMC Utrecht, Huispost Str. 6.131, PO Box 85500, Utrecht 3508 GA, The Netherlands. 3. Department of Methodology and Statistics, Utrecht University, Padualaan 14, Utrecht 3584 CH, The Netherlands; Section of Research Methodology, Measurement and Data Analysis, Department of Behavioural, Management and Social Sciences, Twente University, P.O. Box 217, Enschede 7500 AE, The Netherlands. 4. Department of Epidemiology, Julius Center for Health Sciences and Primary Care, UMC Utrecht, Huispost Str. 6.131, PO Box 85500, Utrecht 3508 GA, The Netherlands.
Abstract
OBJECTIVES: 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. STUDY DESIGN AND SETTING: 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 the original research papers with primary Bayesian data analyses. RESULTS: The number of studies in which Bayesian techniques were used for primary data analysis remains 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 result sections. Especially, information on the process of prior elicitation, specification, and evaluation was often lacking. CONCLUSION: 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 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.
OBJECTIVES: 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. STUDY DESIGN AND SETTING: 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 the original research papers with primary Bayesian data analyses. RESULTS: The number of studies in which Bayesian techniques were used for primary data analysis remains 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 result sections. Especially, information on the process of prior elicitation, specification, and evaluation was often lacking. CONCLUSION: 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 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.