Journal
NEPHROLOGY DIALYSIS TRANSPLANTATION
Volume 32, Issue -, Pages 84-90Publisher
OXFORD UNIV PRESS
DOI: 10.1093/ndt/gfw341
Keywords
bias; confounding; inverse probability of treatment weight; longitudinal study design; marginal methods
Categories
Ask authors/readers for more resources
Marginal structural models are a multi-step estimation procedure designed to control for the effect of confounding variables that change over time, and are affected by previous treatment. When a time-varying confounder is affected by prior treatment standard methods for confounding control are inappropriate, because over time the covariate plays both the role of confounder and mediator of the effect of treatment on outcome. Marginal structural models first calculate a weight to assign to each observation. These weights reflect the extent to which observations with certain characteristics (covariate values) are under-represented or over-represented in the sample with the respect to a target population in which these characteristics are balanced across treatment groups. Then, marginal structural models estimate the outcome of interest taking into account these weights. Marginal structural models are a powerful method for confounding control in longitudinal study designs that collect time-varying information on exposure, outcome and other covariates.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available