Marginal analysis of a population-based genetic association study of quantitative traits with incomplete longitudinal data

Baojiang Chen, Zhijian Chen, Longyang Wu, Lihua Wang, Grace Yi Yi

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)109-123
Number of pages15
JournalJournal of the Iranian Statistical Society
Volume10
Issue number2
StatePublished - Dec 1 2011

Fingerprint

Gene-environment Interaction
Genetic Association
Incomplete Data
Longitudinal Data
Weighted Estimating Equations
Missing Data Mechanism
Consistent Estimates
Missing at Random
Generalized Estimating Equations
Interaction Effects
Model Specification
Statistical Inference

Keywords

  • Generalized estimating equations
  • Genetic association
  • Longitudinal data
  • Missing at random

ASJC Scopus subject areas

  • Statistics and Probability

Cite this

Marginal analysis of a population-based genetic association study of quantitative traits with incomplete longitudinal data. / Chen, Baojiang; Chen, Zhijian; Wu, Longyang; Wang, Lihua; Yi, Grace Yi.

In: Journal of the Iranian Statistical Society, Vol. 10, No. 2, 01.12.2011, p. 109-123.

Research output: Contribution to journalArticle

Chen, Baojiang ; Chen, Zhijian ; Wu, Longyang ; Wang, Lihua ; Yi, Grace Yi. / Marginal analysis of a population-based genetic association study of quantitative traits with incomplete longitudinal data. In: Journal of the Iranian Statistical Society. 2011 ; Vol. 10, No. 2. pp. 109-123.
@article{35867f1670fc4f438eb7bcaef09630e1,
title = "Marginal analysis of a population-based genetic association study of quantitative traits with incomplete longitudinal data",
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",
author = "Baojiang Chen and Zhijian Chen and Longyang Wu and Lihua Wang and Yi, {Grace Yi}",
year = "2011",
month = "12",
day = "1",
language = "English (US)",
volume = "10",
pages = "109--123",
journal = "Journal of the Iranian Statistical Society",
issn = "1726-4057",
publisher = "Iranian Statistical Society",
number = "2",

}

TY - JOUR

T1 - Marginal analysis of a population-based genetic association study of quantitative traits with incomplete longitudinal data

AU - Chen, Baojiang

AU - Chen, Zhijian

AU - Wu, Longyang

AU - Wang, Lihua

AU - Yi, Grace Yi

PY - 2011/12/1

Y1 - 2011/12/1

N2 - 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.

AB - 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.

KW - Generalized estimating equations

KW - Genetic association

KW - Longitudinal data

KW - Missing at random

UR - http://www.scopus.com/inward/record.url?scp=84859327444&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84859327444&partnerID=8YFLogxK

M3 - Article

VL - 10

SP - 109

EP - 123

JO - Journal of the Iranian Statistical Society

JF - Journal of the Iranian Statistical Society

SN - 1726-4057

IS - 2

ER -