Introduction

Patient management comprises three key elements: arriving at a diagnosis, assessing prognosis, and making a treatment choice. Many decisions have to be made along the way. Almost every clinical decision carries an element of uncertainty: uncertainty about the occurrence of events and about the value of these events. Especially in the clinical neurosciences, decision making becomes more complicated every day, because of rapidly emerging new treatments and diagnostic tests, and because of increasing demands from patients to be informed and to participate in decision making. By its explicit nature and strictly logical approach, decision analysis can be a helpful tool in everyday patient management. It can form the necessary link between the results of a randomized clinical trial or diagnostic study, and their application in clinical practice. Since the mid-1970s, more than 60 decision analyses of neurological management problems have been published, and their number is now growing fast.

Decision analysis is a theory of decision making under conditions of uncertainty, used for normative, prescriptive purposes. In clinical decision analysis an intriguing mixture of medical (Bayesian) statistics, clinical epidemiology and clinical science is added (4–6). A clinical decision analysis explicitly addresses the inevitable uncertainties in a clinical problem and combines these with preferences for health outcomes in a consistent framework that obeys the laws of probability calculus and the theory of subjective expected utility. This should lead to management advice concerning an individual patient, or a group of similar patients. First the basic steps and assumptions in a decision analysis will be outlined. Four stages can be conveniently identified.