Estimating haplotype effects for survival data.
Genetic association studies often investigate the effect of haplotypes on an outcome of interest. Haplotypes are not observed directly, and this complicates the inclusion of such effects in survival models. We describe a new estimating equations approach for Cox's regression model to assess haplotype effects for survival data. These estimating equations are simple to implement and avoid the use of the EM algorithm, which may be slow in the context of the semiparametric Cox model with incomplete covariate information. These estimating equations also lead to easily computable, direct estimators of standard errors, and thus overcome some of the difficulty in obtaining variance estimators based on the EM algorithm in this setting. We also develop an easily implemented goodness-of-fit procedure for Cox's regression model including haplotype effects. Finally, we apply the procedures presented in this article to investigate possible haplotype effects of the PAF-receptor on cardiovascular events in patients with coronary artery disease, and compare our results to those based on the EM algorithm.
Scheike
Thomas H
TH
Department of Biostatistics, University of Copenhagen, Copenhagen K, Denmark. ts@biostat.ku.dk
Martinussen
Torben
T
Silver
Jeremy D
JD
D001699:Biometry / Q000379:methods; D002318:Cardiovascular Diseases / Q000235:genetics; D003324:Coronary Artery Disease / Q000235:genetics; D056726:Genetic Association Studies; D006239:Haplotypes; D006801:Humans; D010980:Platelet Membrane Glycoproteins / Q000235:genetics; D016016:Proportional Hazards Models; D043562:Receptors, G-Protein-Coupled / Q000235:genetics; D016019:Survival Analysis
D010980:Platelet Membrane Glycoproteins; D043562:Receptors, G-Protein-Coupled; C039351:platelet activating factor receptor