Marginal Analysis of A Population-Based Genetic Association Study of Quantitative Traits with Incomplete Longitudinal Data

Authors
Baojiang Chen
Zhijian Chen
Longyang Wu
Lihua Wang
Grace Y. Yi
Abstract
A common study to investigate gene-environment interaction
is designed to be longitudinal and population-based. Data arising
from longitudinal association studies often contain missing responses.
Naive analysis without taking missingness into account may produce
invalid inference, especially when the missing data mechanism depends
on the response process. To address this issue in the analysis concerning
gene-environment interaction effects, in this paper, we adopt an inverse
probability weighted generalized estimating equations (IPWGEE)
approach to conduct statistical inference. This approach is attractive
because it does not require full model specification yet it can provide
consistent estimates under the missing at random (MAR) mechanism.
We utilize this method to analyze data arising from a cardiovascular
disease study.
Keywords
Generalized estimating equations
Genetic Association
longitudinal data
missing at random
Journal of the Iranian Statistical Society
Volume 10, Issue 2
November 2011
Pages 109-123

  • Receive Date 23 July 2022