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

SUBMITTER: Luta G 

PROVIDER: S-EPMC3834354 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature