title: [An application of Bayesian estimation in daily clinical practice of anesthesia].
abstract: The data obtained from the clinical practice are not unanimous, because we cannot assume the patient condition constant. It is, however, important to estimate real time patient condition individually to make more appropriate decision on treatment. In the classical statistical approach to the estimation problem, the patient condition is, so to speak, viewed as an unknown constant, which does not change with time. This is a wrong assumption in the clinical world. In Bayesian statistics, an available datum or information prior to the event (prior probability) is used in the estimation process to obtain the posterior probability. Furthermore, datum or information gained in the clinical setting is considered as a fact instead of a random variable and is utilized to join itself with prior probability. In other word, in Bayesian statistics, estimation is changed to prediction. We utilize our subjective thinking in making decision in our daily practice of anesthesia. In fact, subjectivity is based on the beliefs or opinions, depending on the experience and information possessed by the anesthesiologist who is making the assessment. In Bayesian estimation, subjectivity, a measure of belief is incorporated as a prior probability which, I believe, is more flexible in dealing with the real world problems than any other statistical estimation.
authors.affiliation: Department of Anesthesia, Teikyo University School of Medicine, Ichihara Hospital.
authors.forename: S
authors.initials: S
authors.lastname: Morita
mesh_terms: D000776:Anesthesiology; D001499:Bayes Theorem; D006801:Humans; D010818:Practice Patterns, Physicians'