title: Bayesian methodology for the design and interpretation of clinical trials in critical care medicine: a primer for clinicians.
abstract: OBJECTIVE
  To review Bayesian methodology and its utility to clinical decision making and research in the critical care field.
  
  
  METHODS
  Clinical, epidemiological, and biostatistical studies on Bayesian methods in PubMed and Embase from their inception to December 2013.
  
  
  RESULTS
  Bayesian methods have been extensively used by a wide range of scientific fields, including astronomy, engineering, chemistry, genetics, physics, geology, paleontology, climatology, cryptography, linguistics, ecology, and computational sciences. The application of medical knowledge in clinical research is analogous to the application of medical knowledge in clinical practice. Bedside physicians have to make most diagnostic and treatment decisions on critically ill patients every day without clear-cut evidence-based medicine (more subjective than objective evidence). Similarly, clinical researchers have to make most decisions about trial design with limited available data. Bayesian methodology allows both subjective and objective aspects of knowledge to be formally measured and transparently incorporated into the design, execution, and interpretation of clinical trials. In addition, various degrees of knowledge and several hypotheses can be tested at the same time in a single clinical trial without the risk of multiplicity. Notably, the Bayesian technology is naturally suited for the interpretation of clinical trial findings for the individualized care of critically ill patients and for the optimization of public health policies.
  
  
  CONCLUSIONS
  We propose that the application of the versatile Bayesian methodology in conjunction with the conventional statistical methods is not only ripe for actual use in critical care clinical research but it is also a necessary step to maximize the performance of clinical trials and its translation to the practice of critical care medicine.
authors.affiliation: 1Division of Infectious Diseases, Department of Internal Medicine, University of Nebraska Medical Center, Omaha, NE. 2Department of Critical Care, National Institutes of Health, Bethesda, MD.
authors.forename: Andre C
authors.initials: AC
authors.lastname: Kalil
authors.forename: Junfeng
authors.initials: J
authors.lastname: Sun
mesh_terms: D001499:Bayes Theorem; D002986:Clinical Trials as Topic / Q000379:methods* / Q000592:standards; D017326:Clinical Trials, Phase III as Topic / Q000379:methods / Q000592:standards; D057186:Comparative Effectiveness Research / Q000379:methods / Q000592:standards; D003422:Critical Care / Q000379:methods* / Q000592:standards; D003657:Decision Making; D006801:Humans; D015201:Meta-Analysis as Topic; D017410:Practice Guidelines as Topic / Q000592:standards; D015203:Reproducibility of Results; D013223:Statistics as Topic / Q000379:methods / Q000592:standards; D016896:Treatment Outcome