The use of GEE for analyzing longitudinal binomial data: A primer using data from a tobacco intervention

Ji Hyun Lee, Thaddeus A. Herzog, Cathy D. Meade, Monica W Hooper, Thomas H. Brandon

Research output: Contribution to journalArticle

63 Citations (Scopus)

Abstract

Longitudinal study designs in addictive behaviors research are common as researchers have focused increasingly on how various explanatory variables affect responses over time. In particular, such designs are used in intervention studies that have multiple follow-up points. These designs typically involve repeated measurement of participants' responses, and thus correlation within each participant is expected. Correct inferences can only be obtained by taking into account this within-participant correlation between repeated measurements, which can complicate the analysis of longitudinal data. In recent years, generalized estimating equations (GEE) has become a standard method for analyzing non-normal longitudinal data, yet it often is not utilized by addiction researchers. The goal of this article is to provide an overview of the GEE approach for analyzing correlated binary data for behavioral researchers, using data from an intervention study on the prevention of relapse to tobacco smoking.

Original languageEnglish
Pages (from-to)187-193
Number of pages7
JournalAddictive Behaviors
Volume32
Issue number1
DOIs
StatePublished - Jan 1 2007
Externally publishedYes

Fingerprint

Tobacco
Research Personnel
Addictive Behavior
Secondary Prevention
Longitudinal Studies
Smoking
Research

Keywords

  • Binary longitudinal data
  • GEE
  • Relapse-prevention-intervention
  • Statistical analysis
  • Tobacco relapse

ASJC Scopus subject areas

  • Psychiatry and Mental health
  • Clinical Psychology

Cite this

The use of GEE for analyzing longitudinal binomial data : A primer using data from a tobacco intervention. / Lee, Ji Hyun; Herzog, Thaddeus A.; Meade, Cathy D.; Hooper, Monica W; Brandon, Thomas H.

In: Addictive Behaviors, Vol. 32, No. 1, 01.01.2007, p. 187-193.

Research output: Contribution to journalArticle

Lee, Ji Hyun ; Herzog, Thaddeus A. ; Meade, Cathy D. ; Hooper, Monica W ; Brandon, Thomas H. / The use of GEE for analyzing longitudinal binomial data : A primer using data from a tobacco intervention. In: Addictive Behaviors. 2007 ; Vol. 32, No. 1. pp. 187-193.
@article{46e23048c4cd4fe4883c689ddba544b5,
title = "The use of GEE for analyzing longitudinal binomial data: A primer using data from a tobacco intervention",
abstract = "Longitudinal study designs in addictive behaviors research are common as researchers have focused increasingly on how various explanatory variables affect responses over time. In particular, such designs are used in intervention studies that have multiple follow-up points. These designs typically involve repeated measurement of participants' responses, and thus correlation within each participant is expected. Correct inferences can only be obtained by taking into account this within-participant correlation between repeated measurements, which can complicate the analysis of longitudinal data. In recent years, generalized estimating equations (GEE) has become a standard method for analyzing non-normal longitudinal data, yet it often is not utilized by addiction researchers. The goal of this article is to provide an overview of the GEE approach for analyzing correlated binary data for behavioral researchers, using data from an intervention study on the prevention of relapse to tobacco smoking.",
keywords = "Binary longitudinal data, GEE, Relapse-prevention-intervention, Statistical analysis, Tobacco relapse",
author = "Lee, {Ji Hyun} and Herzog, {Thaddeus A.} and Meade, {Cathy D.} and Hooper, {Monica W} and Brandon, {Thomas H.}",
year = "2007",
month = "1",
day = "1",
doi = "10.1016/j.addbeh.2006.03.030",
language = "English",
volume = "32",
pages = "187--193",
journal = "Addictive Behaviors",
issn = "0306-4603",
publisher = "Elsevier Limited",
number = "1",

}

TY - JOUR

T1 - The use of GEE for analyzing longitudinal binomial data

T2 - A primer using data from a tobacco intervention

AU - Lee, Ji Hyun

AU - Herzog, Thaddeus A.

AU - Meade, Cathy D.

AU - Hooper, Monica W

AU - Brandon, Thomas H.

PY - 2007/1/1

Y1 - 2007/1/1

N2 - Longitudinal study designs in addictive behaviors research are common as researchers have focused increasingly on how various explanatory variables affect responses over time. In particular, such designs are used in intervention studies that have multiple follow-up points. These designs typically involve repeated measurement of participants' responses, and thus correlation within each participant is expected. Correct inferences can only be obtained by taking into account this within-participant correlation between repeated measurements, which can complicate the analysis of longitudinal data. In recent years, generalized estimating equations (GEE) has become a standard method for analyzing non-normal longitudinal data, yet it often is not utilized by addiction researchers. The goal of this article is to provide an overview of the GEE approach for analyzing correlated binary data for behavioral researchers, using data from an intervention study on the prevention of relapse to tobacco smoking.

AB - Longitudinal study designs in addictive behaviors research are common as researchers have focused increasingly on how various explanatory variables affect responses over time. In particular, such designs are used in intervention studies that have multiple follow-up points. These designs typically involve repeated measurement of participants' responses, and thus correlation within each participant is expected. Correct inferences can only be obtained by taking into account this within-participant correlation between repeated measurements, which can complicate the analysis of longitudinal data. In recent years, generalized estimating equations (GEE) has become a standard method for analyzing non-normal longitudinal data, yet it often is not utilized by addiction researchers. The goal of this article is to provide an overview of the GEE approach for analyzing correlated binary data for behavioral researchers, using data from an intervention study on the prevention of relapse to tobacco smoking.

KW - Binary longitudinal data

KW - GEE

KW - Relapse-prevention-intervention

KW - Statistical analysis

KW - Tobacco relapse

UR - http://www.scopus.com/inward/record.url?scp=33750733983&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33750733983&partnerID=8YFLogxK

U2 - 10.1016/j.addbeh.2006.03.030

DO - 10.1016/j.addbeh.2006.03.030

M3 - Article

C2 - 16650625

AN - SCOPUS:33750733983

VL - 32

SP - 187

EP - 193

JO - Addictive Behaviors

JF - Addictive Behaviors

SN - 0306-4603

IS - 1

ER -