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Familial associations of lipid profiles: a generalized estimating equations approach
Author(s) -
Ziegler Andreas,
Kastner Christian,
Brunner Daniel,
Blettner Maria
Publication year - 2000
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 - 10.1002/1097-0258(20001230)19:24<3345::aid-sim829>3.0.co;2-5
Subject(s) - wald test , estimator , generalized estimating equation , statistics , family aggregation , standard error , correlation , mathematics , regression , variance (accounting) , econometrics , apolipoprotein b , regression analysis , statistical hypothesis testing , medicine , disease , geometry , accounting , cholesterol , business
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 (generalized 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 jack‐knife estimators of variance to GEE2 models. Estimation of standard errors in models with quite complex correlation structures was possible using this approach. 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. Copyright © 2000 John Wiley & Sons, Ltd.