title: Bayesian sensitivity analysis methods to evaluate bias due to misclassification and missing data using informative priors and external validation data.
abstract: BACKGROUND
  Recent research suggests that the Bayesian paradigm may be useful for modeling biases in epidemiological studies, such as those due to misclassification and missing data. We used Bayesian methods to perform sensitivity analyses for assessing the robustness of study findings to the potential effect of these two important sources of bias.
  
  
  METHODS
  We used data from a study of the joint associations of radiotherapy and smoking with primary lung cancer among breast cancer survivors. We used Bayesian methods to provide an operational way to combine both validation data and expert opinion to account for misclassification of the two risk factors and missing data. For comparative purposes we considered a "full model" that allowed for both misclassification and missing data, along with alternative models that considered only misclassification or missing data, and the naïve model that ignored both sources of bias.
  
  
  RESULTS
  We identified noticeable differences between the four models with respect to the posterior distributions of the odds ratios that described the joint associations of radiotherapy and smoking with primary lung cancer. Despite those differences we found that the general conclusions regarding the pattern of associations were the same regardless of the model used. Overall our results indicate a nonsignificantly decreased lung cancer risk due to radiotherapy among nonsmokers, and a mildly increased risk among smokers.
  
  
  CONCLUSIONS
  We described easy to implement Bayesian methods to perform sensitivity analyses for assessing the robustness of study findings to misclassification and missing data.
authors.affiliation: Department of Biostatistics, Bioinformatics, and Biomathematics, Lombardi Comprehensive Cancer Center, Georgetown University, Building D, Suite 180, 4000 Reservoir Road, NW, Washington, DC 20057-1484, USA. gl77@georgetown.edu.
authors.forename: George
authors.initials: G
authors.lastname: Luta
authors.forename: Melissa B
authors.initials: MB
authors.lastname: Ford
authors.forename: Melissa
authors.initials: M
authors.lastname: Bondy
authors.forename: Peter G
authors.initials: PG
authors.lastname: Shields
authors.forename: James D
authors.initials: JD
authors.lastname: Stamey
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D015982:Bias; D001943:Breast Neoplasms / Q000145:classification / Q000453:epidemiology*; D016022:Case-Control Studies; D015986:Confounding Factors, Epidemiologic; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000145:classification / Q000453:epidemiology*; D008875:Middle Aged; D008962:Models, Theoretical; D012307:Risk Factors; D017741:Survivors; D054928:Validation Studies as Topic