International Journal of Statistics in Medical Research

An Empirical Method of Detecting Time-Dependent Confounding: An Observational Study of Next Day Delirium in a Medical ICU
Pages 41-47
T.E. Murphy, P.H. Van Ness, K.L.B. Araujo and M.A. Pisani
Published: 08 January 2016

Abstract: Longitudinal research on older persons in the medical intensive care unit (MICU) is often complicated by the time-dependent confounding of concurrently administered interventions such as medications and intubation. Such temporal confounding can bias the respective longitudinal associations between concurrently administered treatments and a longitudinal outcome such as delirium. Although marginal structural models address time-dependent confounding, their application is non-trivial and preferably justified by empirical evidence. Using data from a longitudinal study of older persons in the MICU, we constructed a plausibility score from 0 – 10 where higher values indicate higher plausibility of time-dependent confounding of the association between a time-varying explanatory variable and an outcome. Based on longitudinal plots, measures of correlation, and longitudinal regression, the plausibility scores were compared to the differences in estimates obtained with non-weighted and marginal structural models of next day delirium. The plausibility scores of the three possible pairings of daily doses of fentanyl, haloperidol, and intubation indicated the following: low plausibility for haloperidol and intubation, moderate plausibility for fentanyl and haloperidol, and high plausibility for fentanyl and intubation. Comparing multivariable models of next day delirium with and without adjustment for time-dependent confounding, only intubation’s association changed substantively. In our observational study of older persons in the MICU, the plausibility scores were generally reflective of the observed differences between coefficients estimated from non-weighted and marginal structural models.

Keywords: Time dependent confounding, cross-correlation, longitudinal, marginal structural model, ICU.
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