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The choice of multivariable model depends primarily on the type of outcome variable. Use multiple linear regression and analysis of variance for interval outcomes, multiple logistic regression and log-binomial regression with dichotomous outcomes, proportion odds regression with ordinal outcomes, multinomial logistic regression for nominal outcomes, proportional hazards analysis for time to outcome, Poisson regression and negative binomial regression for counts and for incidence rates. Each model has a different set of underlying assumptions. All of the models assume that there is only one observation of outcome for each subject.
This chapter identifies and explains the fundamental role and responsibilities of the perioperative practitioner essential to the surgical scrub role; this includes surgical counts, sharps safety, specimen managements, and waste disposal. The scrub practitioner is a recognised member of the perioperative team, performing a crucial role in preparing the operating theatre environment for surgical procedures. They must ensure it is clean, ready, and safe to receive the surgical patient. The scrub practitioner should possess the requisite technical and non-technical skills, and theoretical underpinning knowledge of anatomy and physiology to optimally perform their role.
Applies the GLM framework to modeling event count data. Discusses the common problem of overdispersion and the methods for extending the model to account for it.
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