LAUSR

ICU clinicians rely on bedside physiologic measurements to inform many of our routine clinical decisions. Because deranged physiology is usually associated with poor clinical outcome, it is tempting to hypothesize that manipulating and intervening on physiological parameters might improve outcomes for patients. However, testing these hypotheses through mathematical models of the relationship between physiology and outcomes presents a number of important methodological challenges. These models reflect the theories of the researcher and can therefore be heavily influenced by one's assumptions and background beliefs. Model building must therefore be approached with great care and forethought, as failure to consider relevant sources of measurement error, confounding, coupling, and time-dependency, or to assess the direction of causality for associations of interest before modelling, may give rise to spurious results. This paper outlines the main challenges in analyzing and interpreting these models and offers potential solutions to address these challenges.