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

Addictive Behaviors

Volumn 32, Issue 1, 2007, Pages 187-193

  Lee, Ji Hyun ^a   Herzog, Thaddeus A ^a   Meade, Cathy D ^a   Webb, Monica S ^a   Brandon, Thomas H ^a  

^a H LEE MOFFITT CANCER CENTER AND RESEARCH INSTITUTE   (United States)

Author keywords

Binary longitudinal data; GEE; Relapse prevention intervention; Statistical analysis; Tobacco relapse

Indexed keywords

ARTICLE; BEHAVIORAL SCIENCE; CORRELATION ANALYSIS; DRUG DEPENDENCE; FOLLOW UP; HUMAN; MEDICAL RESEARCH; RELAPSE; SMOKING; STATISTICAL ANALYSIS; TOBACCO DEPENDENCE;

ADULT; DATA INTERPRETATION, STATISTICAL; FEMALE; HUMANS; LOGISTIC MODELS; LONGITUDINAL STUDIES; MALE; MIDDLE AGED; RANDOMIZED CONTROLLED TRIALS; SMOKING CESSATION; SOFTWARE;

EID: 33750733983     PISSN: 03064603     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.addbeh.2006.03.030     Document Type: Article

References (4)

1. Brandon T.H., Meade C.D., Herzog T.A., Chrisikos T.N., Webb M.S., and Cantor A.B. Efficacy and cost-effectiveness of a minimal intervention to prevent smoking relapse: Dismantling the effects of amount of content versus contact. Journal of Consulting and Clinical Psychology 75 (2004) 797-808
2. Fitzmaurice G.M. A caveat concerning independence estimating equations with multivariate binary data. Biometrics 51 (1995) 309-317
3. Liang K.Y., and Zeger S.L. Longitudinal data analysis using generalized linear models. Biometrika 73 (1986) 13-22
4. McCullagh P., and Nelder J.A. Generalized linear models. 2nd Ed. (1989), Chapman and Hall, New York