Multiple imputation to account for measurement error in marginal structural models.
The first journal club article is: Edwards JK, Cole SR, Westreich D, Crane H, Eron J, Mathews WC, Moore R, Stephen BL, Lesko CR, Mugavero MJ. Multiple imputation to account for measurement error in marginal structural models. Epidemiology. 2015 Sep;26(5):645-52. http://www.ncbi.nlm.nih.gov/pubmed/26214338
This journal club is moderated by Dr. Jess Edwards, a research assistant professor in the Department of Epidemiology at the University of North Carolina at Chapel Hill. Her research interests include quantitative methods for causal inference in the context of infectious diseases (particularly HIV) and occupational epidemiology. Dr. Edwards currently focuses on issues related to measurement error, retention in care, and missing data in clinical cohort studies of patients with HIV.
Dr. Edwards has kindly put together a few questions to kickstart the conversation:
Marginal structural models are useful tools for causal inference from observational data. However, when fitting marginal structural models, exposure is almost always assumed to be measured without error. This paper illustrates how multiple imputation can be used to relax this assumption when a “gold standard” measure of exposure is available for some study participants.
- The “gold standard” measure of exposure used in this paper did not come from a formal validation study. What are some of the strengths and limitations of using this type of information on the “gold standard” exposure measure? What are the assumptions necessary to use this information?
- What are some examples of other settings where rich exposure information may be available for a subset of study participants? Could this information be used to account for exposure measurement error? Why or why not?
- In this paper, multiple imputation was used to impute the “true” exposure prior to estimating the inverse probability weights used to account for confounding. Under which situations would you want to estimate the inverse probability weights before imputing the true exposure?