Statistical methods in diagnosis.
Motivations are presented for exploring formal statistical methods for use in medical diagnosis and the advantages and disadvantages are discussed. A brief review is presented of classical linear discriminant analysis, quadratic discriminant analysis, logistic regression, nearest neighbour and kernel methods, recursive partitioning methods, the independence model, regularized discriminant analysis, structured conditional probability distributions, methods for categorical data, and other methods. Criteria on which a choice might be made are presented and methods for assessing diagnostic performance are outlined. Particular applications of screening and chromosome analysis are used as illustrations and available software is described.
Hand
D J
DJ
Department of Statistics, Faculty of Mathematics, Open University, Milton Keynes, UK.
D001499:Bayes Theorem; D002869:Chromosome Aberrations / Q000175:diagnosis; D025063:Chromosome Disorders; D003936:Diagnosis, Computer-Assisted; D016002:Discriminant Analysis; D006801:Humans; D008403:Mass Screening / Q000379:methods; D008432:Mathematical Computing; D011336:Probability; D012044:Regression Analysis; D012984:Software