Analysis of prevention program effectiveness with clustered data using generalized estimating equations

Journal of Consulting and Clinical Psychology

Volumn 64, Issue 5, 1996, Pages 919-926

  Norton, Edward C ^a,b   Bieler, Gayle S ^a   Ennett, Susan T ^a   Zarkin, Gary A ^a  

^a RTI INTERNATIONAL   (United States)

^b IL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; CLUSTER ANALYSIS; SAMPLING; SOCIAL PSYCHIATRY; STATISTICAL ANALYSIS; STATISTICS; VARIANCE;

ADOLESCENT; ADULT; CHILD; CLUSTER ANALYSIS; CURRICULUM; DATA INTERPRETATION, STATISTICAL; FEMALE; HEALTH EDUCATION; HUMANS; LONGITUDINAL STUDIES; MALE; MENTAL DISORDERS; OUTCOME ASSESSMENT (HEALTH CARE); SUBSTANCE-RELATED DISORDERS; TREATMENT OUTCOME;

EID: 0029848108     PISSN: 0022006X     EISSN: None     Source Type: Journal    
DOI: 10.1037/0022-006X.64.5.919     Document Type: Article

References (42)

1. Bieler, G. S., & Williams, R. L. (1995). Cluster sampling techniques in quantal response teratology and developmental toxieity studies. Biometrics, 51, 764-776.
2. Bryk, A. S., & Raudenbush, S. W. (1987). Application of hierarchical linear models to assessing change. Psychological Bulletin. 101, 147-158.
3. Bureau of Justice Assistance. (1988). Implementing project DARE: Drug abuse resistance education. Washington, DC: Author.
4. Dielman, T. E. ( 1994). School-based research on the prevention of ad- descent use and misuse: Methodological issues and advances. Journal of Research on Adolescence. 4, 271-293.
5. Donner, A. (1982). An empirical study of cluster randomization. American Journal of Epidemiology, 11, 283-286.
6. Donner, A., Birkett, N., & Buck, C. ( 1981 ). Randomization by cluster Sample size requirements and analysis. American Journal of Epidemiology. 114, 283-286.
7. Dwyer, J. H., MacKinnon, D. P., Pentz, M. A., Flay, B. R., Hansen, W. B., Yu, E., Wang, I., & Anderson Johnson, C. (1989). Estimating intervention effects in longitudinal studies. American Journal of Epidemiology, 130, 781-795.
8. Ellickson, P. L., & Bell, R. M. ( 1990). Drug prevention in junior high: A multi-site longitudinal test. Science, 247, 1299-1305.
9. Ellickson, P. L., & Bell, R. M. ( 1992). Challenges to social experiments: A drug prevention example. Journal of Research in Crime and Delinquency. 29(1), 79-101.
10. Ennett, S. T., Rosenbaum, D. P., Flewelling, R. L., Bieler, G. S., Ringwait, C. L., & Bailey, S. L. (1994). Long-term evaluation of drug abuse resistance education. Addictive Behaviors, 19( 1 ), 113-125.
11. Fleiss, J. L. ( 1981 ). Statistical methods for rates and proportions. New York: Wiley.
12. Gibbons, R. D., & Hedeker, D. (1994). Application of random-effects probit regression models. Journal of Consulting and Clinical Psychology. 62.285-296.
13. Goldstein, H. ( 1987 ). Multilevel models in educational and social research. New York: Oxford University Press.
14. Graham, J. W. (1995, June). Testing prevention program effects with a multilevel bootstrap. Paper presented at the Society for Prevention Research Annual Meeting, Scottsdale, AZ.
15. Hedeker, D., & Gibbons, R. D. (1994). A random-effects ordinal regression model for multilevel analysis. Biometrics 50, 933-944.
16. Hedeker, D. R., Gibbons, R. D., & Davis, J. M. ( 1991 ). Random regression models for multicenter clinical trials data. Psychopharmacology Bulletin, 27( 1 ), 73-77.
17. Hedeker, D. R., Gibbons, R. D., & Flay, B. R. ( 1994). Random-effects regression models for clustered data with an example from smoking prevention research. Journal of Consulting and Clinical Psychology, 62, 757-765.
18. Huber, P. J. (1967). The behavior of maximum likelihood estimates under nonstandard conditions. In Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1, 221-233.
19. Karim, M. R., &Zeger, S. L. (1989). GEE: A SAS macro for longitudinal data analysis (Tech. Rep. No. 674). Baltimore: Johns Hopkins University, Department of Biostatistics.
20. Kish, L., & Frankel, M. (1974). Inference from complex samples (with discussion). Journal of the Royal Statistical Society, Series B. 36, 1-37.
21. Laird, N. M., Donnelly, C, & Ware, J. H. ( 1992). Longitudinal studies with continuous responses. Statistical Methods in Medical Research, 1, 225-247.
22. LaPrelle, J., Bauman, K. E., & Koch, G. G. (1992). High intercommunity variation in adolescent cigarette smoking in a 10-community field experiment. Evaluation Review, 16, 115-130.
23. Liang, K. Y, & Zeger, S. L. ( 1986). Longitudinal data analysis using generalized linear models. Biometrika, 73,13-22.
24. Lipsitz, S. R., Fitzmaurice, G. M., Orav, E. J., & Laird, N. M. (1994). Performance of generalized estimating equations in practical situations. Biometrics, 50, 270-278.
25. Moskowitz, J. M., Malvin, J. H., Schaeffer, G. A., & Schaps, E. ( 1984). An experimental evaluation of a drug education course. Journal of Drug Education, 14,9-22.
26. Murray, D. M., & Hannan, P. J. ( 1990). Planning for the appropriate analysis in school-based drug-use prevention studies. Journal of Consulting and Clinical Psychology, 58,458-168.
27. Neuhaus, J. M. (1993). Estimation efficiency and tests of covariate effects with clustered binary data. Biometrics, 49, 989-996.
28. Neuhaus, J. M., Hauck, W. W., & Kalbfleisch, J. D. ( 1991 ). A comparison of cluster-specific and population-averaged approaches for analyzing correlated binary data. International Statistical Review 59, 25-35.
29. Neuhaus, J. M., & Segal, M. R. ( 1993). Design effects for binary regression models fitted to dependent data. Statistics in Medicine, 12, 1259-1268.
30. Park, T. (1993). A comparison of the generalized estimating equation approach with the maximum likelihood approach for repeated measurements. Statistics in Medicine. 12, 1723-1732.
31. Pentz, M. A., Dwyer, J. H., MacKinnon, D. P., Flay, B. R., Hansen, W. B., Wang, E. Y. I., & Johnson, C. A. ( 1989). A multicommunity trial for primary prevention of adolescent drug abuse. Journal of American Medical Association. 261, 3259-3266.
32. Raudenbush, S. W., & Bryk, A. S. (1986). A hierarchical model for studying school effects. Sociology of Education, 59, 1-17.
33. Royall, R. M. (1986). Model robust confidence intervals using maximum likelihood estimators. International Statistical Revieiv, 54,221-226.
34. SAS Institute, Inc. (1989). SAS/STAT user's guide, Volume 2, Version 6 (4th éd.). Cary, NC: Author.
35. Scott, A. J., & Holt, D. ( 1982). The effect of two-stage sampling on ordinary least squares methods. Journal of the American Statistical Association, 77, 848-854.
36. Shah, B. V., Barnwell, B. G., & Bieter, G. S. (1995). SUDAAN user's manual: Version 6.4. Research Triangle Park, NC: Research Triangle Institute.
37. Snedecor, G. W., &Cochran, W. G. ( 1980). Statistical methods. Ames: Iowa State University Press.
38. Stata Corporation. (1993). Stata reference manual: Release 3.1 (6th ed.). College Station, TX: Author.
39. Stiratelli, R., Laird, N. M., & Ware, J. H. (1984). Random-effects models for serial observations with binary response. Biometrics 40, 961-971.
40. White, H. (1980). A heteroskedasticity-consistent covariance matrix and a direct test for heteroskedasticity. Econometrica, 48, 817-838.
41. Zeger, S. L., & Liang, K. Y. ( 1986). Longitudinal data analysis for discrete and continuous outcomes. Biometrics, 42, 121-130.
42. Zeger, S. L., Liang, K. Y, & Albert, P. S. ( 1988 ). Models for longitudinal data: A generalized estimating equation approach. Biometrics 44, 1049-1060.