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Multivariate analysis of cholesterol distribution for monitoring the risk of coronary heart disease
Author(s) -
Percy David F.,
Hine Trevor J.
Publication year - 1993
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4780121008
Subject(s) - multivariate statistics , coronary heart disease , multivariate analysis , consistency (knowledge bases) , statistics , joint probability distribution , variance (accounting) , multivariate analysis of variance , medicine , computer science , mathematics , artificial intelligence , accounting , business
The screening of people for potential coronary heart disease and the monitoring of subjects considered at risk have been performed for some time by measuring total serum cholesterol and its constituent lipoproteins. However, these measurements vary substantially within subjects, making such assessments imprecise. It has been suggested that greater consistency can be achieved by analysing the joint distribution of the individual lipoproteins or of transformed variables derived from them. In this paper we present the results of a laboratory experiment to investigate these ideas with a view to improving current methods of monitoring patients at risk. Nested, random‐effects, multivariate analysis of variance and the log‐ratio analysis of compositions are used to include information on all three lipoproteins simultaneously, and ratios of generalized variances are used to assess and compare the different response variables. The multivariate approach is seen to be far superior to the usual methods. Recommendations are made for routine monitoring and the practical implications are discussed.