Statistical Methods for Modeling Time-Updated Exposures in Cohort Studies of Chronic Kidney Disease.

ABSTRACT: When estimating the effect of an exposure on a time-to-event type of outcome, one can focus on the baseline exposure or the time-updated exposures. Cox regression models can be used in both situations. When time-dependent confounding exists, the Cox model with time-updated covariates may produce biased effect estimates. Marginal structural models, estimated through inverse-probability weighting, were developed to appropriately adjust for time-dependent confounding. We review the concept of time-dependent confounding and illustrate the process of inverse-probability weighting. We fit a marginal structural model to estimate the effect of time-updated systolic BP on the time to renal events such as ESRD in the Chronic Renal Insufficiency Cohort. We compare the Cox regression model and the mar

SUBMITTER: Xie D 

PROVIDER: S-EPMC5672960 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature