Clustering and Residual Confounding in the Application of Marginal Structural Models: Dialysis Modality, Vascular Access, and Mortality

American Journal of Epidemiology
Jessica KaszaRory Wolfe

Abstract

In the application of marginal structural models to compare time-varying treatments, it is rare that the hierarchical structure of a data set is accounted for or that the impact of unmeasured confounding on estimates is assessed. These issues often arise when analyzing data sets drawn from clinical registries, where patients may be clustered within health-care providers, and the amount of data collected from each patient may be limited by design (e.g., to reduce costs or encourage provider participation). We compared the survival of patients undergoing treatment with various dialysis types, where some patients switched dialysis modality during the course of their treatment, by estimating a marginal structural model using data from the Australia and New Zealand Dialysis and Transplant Registry, 2003-2011. The number of variables recorded by the registry is limited, and patients are clustered within the dialysis centers responsible for their treatment, so we assessed the impact of accounting for unmeasured confounding or clustering on estimated treatment effects. Accounting for clustering had limited impact, and only unreasonable levels of unmeasured confounding would have changed conclusions about treatment comparisons. Our anal...Continue Reading

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Citations

Jan 24, 2016·American Journal of Kidney Diseases : the Official Journal of the National Kidney Foundation·Kevan R Polkinghorne
Nov 13, 2015·American Journal of Kidney Diseases : the Official Journal of the National Kidney Foundation·Mark R MarshallStephen P McDonald
May 11, 2015·American Journal of Kidney Diseases : the Official Journal of the National Kidney Foundation·Mark R MarshallStephen P McDonald
Mar 25, 2017·International Journal of Epidemiology·Jessica KaszaTibor Schuster
May 17, 2021·American Journal of Kidney Diseases : the Official Journal of the National Kidney Foundation·Mark R MarshallStephen P McDonald

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