Methods to address measurement error in both Outcome and Covariates

Biomedical research is increasingly relying on readily available routine data, such as electronic health records.

Routinely collected data, as well as datasets from large cohorts, are often prone to measurement error which, if not addressed in analyses, can bias study results and ultimately mislead clinical decision-making and potentially harm patients.

For this setting, methods that address errors in the outcome and multiple covariates are needed.

In this tutorial, we will review available methods to address for errors in both outcomes and covariates.

We will illustrate methods with use of a running example in order to compare the methods directly.

Both the data and analytic code are provided for the user so that they may easily reproduce results in each example.

We conclude the tutorial with a discussion of the different approaches and highlight areas of future work needed for this setting.