New methodology of influential point detection in regression model building for the prediction of metabolic clearance rate of glucose.

Abstract

Identifying outliers and high-leverage points is a fundamental step in the least-squares regression model building process. The examination of data quality involves the detection of influential points, outliers and high-leverages, which cause many problems in regression analysis. On the basis of a statistical analysis of the residuals (classical, normalized, standardized, jackknife, predicted and recursive) and diagonal elements of a projection matrix, diagnostic plots for influential points indication are formed. The identification of outliers and high leverage points are combined with graphs for the identification of influence type based on the likelihood distance. The powerful procedure for the computation of influential points characteristics written in S-Plus is demonstrated on the model predicting the metabolic clearance rate of glucose (MCRg) that represents the ratio of the amount of glucose supplied to maintain blood glucose levels during the euglycemic clamp and the blood glucose concentration from common laboratory and anthropometric indices. MCRg reflects insulin sensitivity filtering-off the effect of blood glucose. The prediction of clamp parameters should enable us to avoid the demanding clamp examination, which is connected with a higher load and risk for patients.