Estimates of Intraclass Correlation for Variables Related to Behavioral HIV/STD Prevention in a Predominantly African American and Hispanic Sample of Young Women
Restricted accessResearch articleFirst published online February, 2009
Estimates of Intraclass Correlation for Variables Related to Behavioral HIV/STD Prevention in a Predominantly African American and Hispanic Sample of Young Women
Studies designed to evaluate HIV and STD prevention interventions often involve random assignment of groups such as neighborhoods or communities to study conditions (e.g., to intervention or control). Investigators who design group-randomized trials (GRTs) must take the expected intraclass correlation coefficient (ICC) into account in sample size estimation to have adequate power; however, few published ICC estimates exist for outcome variables related to HIV and STD prevention. The Prevention Options for Women Equal Rights (POWER) study was a GRT designed to evaluate a campaign to increase awareness and use of condoms among young African American and Hispanic women. The authors used precampaign and postcampaign data from the POWER study to estimate ICCs (unadjusted and adjusted for covariates) for a variety of sexual behavior and other variables. To illustrate the impact of ICCs on power, the authors present sample-size calculations and demonstrate how ICCs of differing magnitude will affect estimates of required sample size.
Bull, S.S., Posner, S.F., Ortiz, C., Beaty, B., Benton, K., Lin, L., et al. (in press). A social marketing campaign promoting female and male condoms. Journal of Adolescent Health, 43(1), 71-78.
2.
Donner, A., Birkett, N., & Buck, C. (1981). Randomization by cluster: Sample size requirements and analysis. American Journal of Epidemiology, 114(6), 906-914.
3.
Donner, A., & Klar, N. (2000). Design and analysis of cluster randomization trials in health research. London: Arnold .
4.
Donner, A., & Klar, N. (2004). Pitfalls of and controversies in cluster randomization trials. American Journal of Public Health, 94(3), 416-422.
5.
Feldblum, P.J., Chen-Mok, M., Bwayo, J.J., Omari, M., Kuyoh, M., & Ryan, K.A. (1999). Intracluster correlation of STD prevalence in a community intervention trial in Kenya. The Lancet, 354(9187), 1356-1357.
6.
Feng, Z., Diehr, P., Peterson, A., & McLerran, D. (2001). Selected statistical issues in group randomized trials. Annual Review of Public Health, 22, 167-187.
7.
Feng, Z., & Grizzle, J.E. (1992). Correlated binomial variates: Properties of estimator intraclass correlation and its effect on sample size calculation. Statistics in Medicine, 11(12), 1607-1614.
8.
Giraudeau, B. (2006). Model mis-specification and overestimation of the intraclass correlation coefficient in cluster randomized trials. Statistics in Medicine, 25(6), 957-964.
9.
Hughes, J.P. (2005). Using baseline data to design a group randomized trial. Statistics in Medicine, 24(13), 1983-1994.
10.
Kish, L. (1965). Survey sampling. New York: John Wiley.
11.
Klar, N., & Donner, A. (2001). Current and future challenges in the design and analysis of cluster randomization trials. Statistics in Medicine , 20(24), 3729-3740.
12.
Muhib, F.B., Lin, L.S., Steuve, A., Miller, R.L., Ford, W.L., Johnson, W.D., et al. (2001). A venue-based method for sampling hard-to-reach populations. Public Health Reports, 116(Suppl. 1), 216-222.
13.
Murray, D.M. (1998). Design and analysis of group-randomized trials . New York: Oxford University Press .
14.
Murray, D.M., & Blitstein, J.L. (2003). Methods to reduce the impact of intraclass correlation in group-randomized trials. Evaluation Review, 27(1), 79-103.
15.
Murray, D.M., Varnell, S.P., & Blitstein, J.L. (2004). Design and analysis of group-randomized trials: A review of recent methodological developments. American Journal of Public Health, 94(3), 423-432.
16.
Parker, D.R., Evangelou, E., & Eaton, C.B. (2005). Intraclass correlation coefficients for cluster randomized trials in primary care: The Cholesterol Education and Research Trial (CEART). Controlled Clinical Trials, 26(2), 260-267.
17.
Pedlow, C.T., & Carey, M.P. (2003). HIV sexual risk-reduction interventions for youth: A review and methodological critique of randomized controlled trials. Behavior Modification, 27(2), 135-190.
18.
Robin, L., Dittus, P., Whitaker, D., Crosby, R., Ethier, K., Mezoff, J., et al. (2004). Behavioral interventions to reduce incidence of HIV, STD, and pregnancy among adolescents: A decade in review. Journal of Adolescent Health, 34(1), 3-26.
19.
SAS Institute. (2003). SAS/STAT Software for PC (Version 9.1). Cary, NC: Author.
20.
Searle, S.R. (1971). Linear models. New York: Wiley.
21.
Semaan, S., Kay, L., Strouse, D., Sogolow, E., Mullen, P.D., Neumann, M.S., et al. (2002). A profile of U.S.-based trials of behavioral and social interventions for HIV risk reduction. Journal of Acquired Immune Deficiency Syndromes, 30(Suppl. 1), S30-S50.
22.
Simpson, J.M., Klar, N., & Donner, A. (1995). Accounting for cluster randomization: A review of primary prevention trials, 1990 through 1993. American Journal of Public Health, 85(10), 1378-1383.
23.
Varnell, S.P., Murray, D.M., & Baker, W.L. (2001). An evaluation of analysis options for the one-group-per-condition design: Can any of the alternatives overcome the problems inherent in this design? Evaluation Review, 25(4), 440-453.
24.
Varnell, S.P., Murray, D.M., Janega, J.B., & Blitstein, J.L. (2004). Design and analysis of group-randomized trials: A review of recent practices. American Journal of Public Health, 94(3), 393-399.
25.
Wolfinger, R., & O'Connell, M. (1993). Generalized linear mixed models: A pseudo-likelihood approach. Journal of Statistical Computation and Simulation , 48, 233-243.
26.
Zucker, D.M. (1990). An analysis of variance pitfall: The fixed effects analysis in a nested design. Educational and Psychological Measurement, 50, 731-738.
