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Volume 17, Issue 8, Pages 603-607 (August 2007)


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Weighting Condom Use Data to Account for Nonignorable Cluster Size

John M. Williamson, MSc, ScDCorresponding Author Informationemail address, Hae-Young Kim, MSc, Lee Warner, MPH, PhD

Received 29 September 2006; accepted 26 March 2007. published online 29 May 2007.

Purpose

We examined the impact of weighting the generalized estimating equation (GEE) by the inverse of the number of sex acts on the magnitude of association for factors predictive of recent condom use.

Methods

Data were analyzed from a cross-sectional survey on condom use reported during vaginal intercourse during the past year among male students attending two Georgia universities. The usual GEE model was fit to the data predicting the binary act-specific response indicating whether a condom was used. A second cluster-weighted GEE model (i.e., weighting the GEE score equation by the inverse of the number of sex acts) was also fit to predict condom use.

Results

Study participants who engaged in a greater frequency of sex acts were less likely to report condom use, resulting in nonignorable cluster-size data. The GEE analysis weighted by sex act (usual GEE) and the GEE analysis weighted by study subject (cluster-weighted GEE) produced different estimates of the association between the covariates and condom use in last year. For example, the cluster-weighted GEE analysis resulted in a marginally significant relationship between age and condom use (odds ratio of 0.49 with 95% confidence interval (0.23–1.03) for older versus younger participants) versus a nonsignificant relationship with the usual GEE model (odds ratio of 0.67 with a 95% confidence interval of 0.28–1.60).

Conclusions

The two ways of weighting the GEE score equation, by the sex act or by the respondent, may produce different results and a different interpretation of the parameters in the presence of nonignorable cluster size.

From the National Center for Infectious Diseases, Division of Parasitic Diseases, Centers for Disease Control and Prevention (CDC), Atlanta, GA (J.M.W.); Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC (H.-Y.K.); and National Center for Chronic Disease Prevention and Health Promotion, Division of Reproductive Health, Centers for Disease Control and Prevention (CDC), Atlanta, GA (L.W.)

Corresponding Author InformationAddress correspondence to: John M. Williamson, ScD, Centers for Disease Control and Prevention, National Center for Infectio, Atlanta, GA 30341.

PII: S1047-2797(07)00142-1

doi:10.1016/j.annepidem.2007.03.008


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