This talk will discuss two guidance papers for biostatisticians on the topic of measurement error, written by Topic Group 4 (Measurement Error and Misclassification) of the STRATOS Initiative. I will provide some background to this work and give an overview of the material covered in the two papers. The topics in the first paper range from an introduction to error of different types and discussion of their impact, to study design considerations, to the simpler methods of measurement error correction (regression calibration and simulation extrapolation). The second paper covers more advanced topics including more advanced and flexible methods for error correction, such as Bayesian methods and multiple imputation, the design and analysis of studies when the outcome is measured with error, and the use of sensitivity analyses. I will also highlight some of the available software for implementing the methods discussed. The rest of the talk will focus on two particular topics from the guidance paper which involve more recent findings: use of multiple imputation for correcting for the impacts of measurement error in covariates, and special issues arising when there is measurement error in an outcome variable. Examples will be given from a study in nutritional epidemiology with error-prone covariates, and from a trial with an error-prone outcome. In the following presentation, Laurence Freedman will discuss another particularly interesting and challenging topic covered in the guidance papers, that of Berkson error.