[An application of a linear statistical model in clinical anesthesia practice].
One of the important topics in clinical practice of anesthesia, is "prediction". We have to predict what will happen next after obtaining some imperfect data about the patient. The standard and most popularized method is the one utilizing a linear statistical model. This linear model in prediction is based, in part, on the information theory, which says that the volume of information is the linear combination of its components. One of the characteristic models is linear regression analysis. In this paper, basic structure of linear statistical model and its application are discussed. In particular, problems and pitfalls in its application are demonstrated, focusing on variable selection, multi-colinearity and model checking, which will, I believe, lead us to have an access to this clinically important subject "prediction".
Morita
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Department of Anesthesia, Teikyo University, School of Medicine, Ichihara Hospital, Ichihara.
D000758:Anesthesia; D005544:Forecasting; D006801:Humans; D016014:Linear Models; D015233:Models, Statistical