Literature DB >> 18503036

Marginal structural models for partial exposure regimes.

Stijn Vansteelandt1, Karl Mertens, Carl Suetens, Els Goetghebeur.   

Abstract

Intensive care unit (ICU) patients are highly susceptible to hospital-acquired infections due to their poor health and many invasive therapeutic treatments. The effect on mortality of acquiring such infections is, however, poorly understood. Our goal is to quantify this using data from the National Surveillance Study of Nosocomial Infections in ICUs (Belgium). This is challenging because of the presence of time-dependent confounders, such as mechanical ventilation, which lie on the causal path from infection to mortality. Standard statistical analyses may be severely misleading in such settings and have shown contradictory results. Inverse probability weighting for marginal structural models may instead be used but is not directly applicable because these models parameterize the effect of acquiring infection on a given day in ICU, versus "never" acquiring infection in ICU, and this is ill-defined when ICU discharge precedes that day. Additional complications arise from the informative censoring of the survival time by hospital discharge and the instability of the inverse weighting estimation procedure. We accommodate this by introducing a new class of marginal structural models for so-called partial exposure regimes. These describe the effect on the hazard of death of acquiring infection on a given day s, versus not acquiring infection "up to that day," had patients stayed in the ICU for at least s days.

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Year:  2008        PMID: 18503036     DOI: 10.1093/biostatistics/kxn012

Source DB:  PubMed          Journal:  Biostatistics        ISSN: 1465-4644            Impact factor:   5.899


  4 in total

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Journal:  Intensive Care Med       Date:  2010-03       Impact factor: 17.440

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Authors:  Stijn Vansteelandt; Tyler J Vanderweele
Journal:  Biometrics       Date:  2012-09-18       Impact factor: 2.571

3.  Adjusting for time-varying confounding in the subdistribution analysis of a competing risk.

Authors:  Maarten Bekaert; Stijn Vansteelandt; Karl Mertens
Journal:  Lifetime Data Anal       Date:  2009-10-10       Impact factor: 1.588

4.  Estimating the Comparative Effectiveness of Feeding Interventions in the Pediatric Intensive Care Unit: A Demonstration of Longitudinal Targeted Maximum Likelihood Estimation.

Authors:  Noémi Kreif; Linh Tran; Richard Grieve; Bianca De Stavola; Robert C Tasker; Maya Petersen
Journal:  Am J Epidemiol       Date:  2017-12-15       Impact factor: 5.363

  4 in total

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