SAS and R code for probabilistic quantitative bias analysis for misclassified binary variables and binary unmeasured confounders

Systematic error from selection bias, uncontrolled confounding, and misclassification is ubiquitous in epidemiologic research but is rarely quantified using quantitative bias analysis (QBA). This gap may in part be due to the lack of readily modifiable software to implement these methods. Our objective is to provide computing code that can be tailored to an analyst's dataset. We briefly describe the methods for implementing QBA for misclassification and uncontrolled confounding and present the reader with example code for how such bias analyses, using both summary-level data and individual record-level data, can be implemented in both SAS and R. Our examples show how adjustment for uncontrolled confounding and misclassification can be implemented. Resulting bias-adjusted point estimates can then be compared to conventional results to see the impact of this bias in terms of its direction and magnitude. Further, we show how 95% simulation intervals can be generated that can be compared to conventional 95% confidence intervals to see the impact of the bias on uncertainty. Having easy to implement code that users can apply to their own datasets will hopefully help spur more frequent use of these methods and prevent poor inferences drawn from studies that do not quantify the impact of systematic error on their results.

Automated summary

Systematic error, including selection bias, uncontrolled confounding, and misclassification, is common in epidemiological research but rarely quantified. We provide computing code that can be customized for an analyst's dataset to implement quantitative bias analysis (QBA) for misclassification and uncontrolled confounding. We demonstrate the implementation of bias analyses using SAS and R, including adjustment for confounding and misclassification. The resulting bias-adjusted estimates can be compared to conventional results, and simulation intervals can be generated to evaluate the impact of bias on uncertainty.