#### Abstract

Intensive care unit (ICU) patients are ell known to be highly susceptible for nosocomial (i.e. hospital-acquired) infections due to their poor health and many invasive therapeutic treatments. The effects of acquiring such infections in ICU on mortality are however ill understood. Our goal is to quantify these effects using data from the National Surveillance Study of Nosocomial Infections in Intensive Care

Units (Belgium). This is a challenging problem because of the presence of time-dependent confounders (such as exposure to 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 contradicting results.

While inverse probability weighting for marginal structural models can be used to accommodate time-dependent confounders, inference for the effect of

?ICU acquired infections on mortality under such models is further complicated (a) by the fact that marginal structural models infer the effect of acquiring infection on a given, fixed day ?in ICU?, which is not well defined when ICU discharge comes prior to that day; (b) by informative censoring of the survival time due to hospital discharge; and (c) by the instability of the inverse weighting estimation procedure. We accommodate these problems by developing inference under a new class of marginal structural models which describe the hazard of death for patients if, possibly contrary to fact, they stayed in the ICU for at least a given number of days s and acquired infection or not on that day. Using these models we estimate that, if patients stayed in the ICU for at least s days, the effect of acquiring infection on day s would be to multiply the subsequent hazard of death

by 2.74 (95 per cent conservative CI 1.48; 5.09).

#### Disciplines

Epidemiology | Statistical Methodology | Statistical Theory

#### Suggested Citation

Vansteelandt, Stijn; Mertens, Karl; Suetens, Carl; and Goetghebeur, Els, "Marginal Structural Models for Partial Exposure Regimes" (February 2008). *Harvard University Biostatistics Working Paper Series.* Working Paper 78.

http://biostats.bepress.com/harvardbiostat/paper78