Familial Associations of Lipid Profiles: A Generalised Estimating Equations Approach

Ziegler, Andreas; Kastner, C.; Brunner, D. und Blettner, Maria (1999): Familial Associations of Lipid Profiles: A Generalised Estimating Equations Approach. Sonderforschungsbereich 386, Discussion Paper 168

Abstract

Elevated plasma levels of apolipoproteins A1 (apoA1) and B (apoB) are important protective factors and risk factors, respectively, for atherosclerosis and coronary heart disease. It is well known that both apoA1 and apoB reveal strong familial aggregation. Our goal was to investigate whether exogenous variables influence these associations. We used marginal regression models for the mean and association structure (Generalised Estimating Equations 2; GEE2) to analyse data from 1435 family members within 469 families of different sizes included in the Donolo-Tel Aviv Three-Generation Offspring Study. The usual robust variance matrix was approximated by extensions of jackknife estimators of variance to GEE2 models. Upon use of this approach estimation of standard errors in models with quite complex correlation structures was possible. All analyses were easily carried out using a menu-driven stand-alone software tool for marginal regression modelling. We demonstrate that a variety of hypotheses can be tested using Wald statistics by modelling regression matrices for the association structure. We show that correlation for apoB between parent-offspring pairs increased with decreasing age difference and that pairs with individuals of the same gender had more similar apoA1 levels than individuals of different gender. Associations between different relative pairs did not all agree with those expected from differences in kinship coefficients. The analysis using GEE2 models revealed structures that would not have been detected by other models and should therefore be used in addition to traditional approaches of analysing family data. GEE2 should be considered a standard method for the investigation of familial aggregation. [ Published in: Statistics in Medicine 19, Issue 24, 3345-3357 ]

• Dokumententyp: Paper
• Fakultät: Mathematik, Informatik und Statistik > Statistik > Sonderforschungsbereich 386 ; Sonderforschungsbereiche > Sonderforschungsbereich 386
• Themengebiete: 500 Naturwissenschaften und Mathematik > 510 Mathematik
• Sprache: Englisch