title: "A Bayesian sensitivity analysis to evaluate the impact of unmeasured confounding with external data: a real world comparative effectiveness study in osteoporosis".
abstract: Observational studies are frequently used to assess the effectiveness of medical interventions in routine clinical practice. However, the use of observational data for comparative effectiveness is challenged by selection bias and the potential of unmeasured confounding. This is especially problematic for analyses using a health care administrative database, in which key clinical measures are often not available. This paper provides an approach to conducting a sensitivity analyses to investigate the impact of unmeasured confounding in observational studies.
  
  
  
  In a real world osteoporosis comparative effectiveness study, the bone mineral density (BMD) score, an important predictor of fracture risk and a factor in the selection of osteoporosis treatments, is unavailable in the data base and lack of baseline BMD could potentially lead to significant selection bias. We implemented Bayesian twin-regression models, which simultaneously model both the observed outcome and the unobserved unmeasured confounder, using information from external sources. A sensitivity analysis was also conducted to assess the robustness of our conclusions to changes in such external data.
  
  
  
  The use of Bayesian modeling in this study suggests that the lack of baseline BMD did have a strong impact on the analysis, reversing the direction of the estimated effect (odds ratio of fracture incidence at 24 months: 0.40 vs. 1.36, with/without adjusting for unmeasured baseline BMD).
  
  
  
  The Bayesian twin-regression models provide a flexible sensitivity analysis tool to quantitatively assess the impact of unmeasured confounding in observational studies. Copyright © 2016 John Wiley & Sons, Ltd.
authors.affiliation: Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, United States.
authors.forename: Xiang
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, United States.
authors.forename: Douglas E
authors.initials: DE
authors.lastname: Faries
authors.affiliation: Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, United States.
authors.forename: Natalie
authors.initials: N
authors.lastname: Boytsov
authors.affiliation: Department of Statistical Science, Baylor University, Waco, TX, United States.
authors.forename: James D
authors.initials: JD
authors.lastname: Stamey
authors.affiliation: Department of Statistical Science, Baylor University, Waco, TX, United States.
authors.forename: John W
authors.initials: JW
authors.lastname: Seaman
mesh_terms: D000368:Aged; D001499:Bayes Theorem; D015519:Bone Density / Q000187:drug effects; D050071:Bone Density Conservation Agents / Q000627:therapeutic use*; D057186:Comparative Effectiveness Research / Q000379:methods; D015986:Confounding Factors, Epidemiologic; D005260:Female; D006801:Humans; D008875:Middle Aged; D064887:Observational Studies as Topic / Q000379:methods*; D010024:Osteoporosis / Q000188:drug therapy*; D012044:Regression Analysis; D012107:Research Design
chemical_list: D050071:Bone Density Conservation Agents
keywords: Bayesian; comparative effectiveness; pharmacoepidemiology; sensitivity analyses; unmeasured confounding