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.
journal_name
Cancer Epidemioljournal_title
Cancer epidemiologyauthors
Luta G,Ford MB,Bondy M,Shields PG,Stamey JDdoi
10.1016/j.canep.2012.11.006subject
Has Abstractpub_date
2013-04-01 00:00:00pages
121-6issue
2eissn
1877-7821issn
1877-783Xpii
S1877-7821(12)00158-0journal_volume
37pub_type
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