Statistical methods in medical diagnosis.
A review of statistical methods in medical diagnosis is presented. Research has focused on three distinct tasks: classification of subjects into probable diagnostic categories on the basis of presenting clinical indicators (discriminant analysis), assessment of diagnostic test characteristics, and relation of diagnostic testing to subsequent patient management. Although many sophisticated models have been developed for discriminant analysis, recent empirical comparisons indicate that standard methods such as linear discrimination and logistic regression work very well. More research is needed to overcome practical difficulties that are not accommodated in the conventional assumptions. Research on the assessment of diagnostic tests has been oriented more toward selection biases and practical problems. There is a need to develop generalized models for the problem of differential diagnosis. The relation of testing to subsequent management of the patient is a topic that has only recently been explored. It represents an important task in the cost-effective management of health resources.
Begg
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CB
Department of Biostatistics, Harvard University, Boston, Massachusetts.
D003661:Decision Support Techniques; D003663:Decision Trees; D003933:Diagnosis; D003951:Diagnostic Errors; D006801:Humans; D008722:Methods; D011237:Predictive Value of Tests; D013223:Statistics as Topic