Relative risk estimation in cluster randomized trials: A comparison of generalized estimating equation methods

International Journal of Biostatistics

Volumn 7, Issue 1, 2011, Pages

  Yelland, Lisa N ^a   Salter, Amy B ^a   Ryan, Philip ^a  

^a UNIVERSITY OF ADELAIDE   (Australia)

Author keywords

cluster randomized trial; generalized estimating equation; log binomial regression; relative risk; simulation

Indexed keywords

ARTICLE; CLUSTER ANALYSIS; CONTROLLED STUDY; GENERALIZED ESTIMATING EQUATION; HUMAN; INTERMETHOD COMPARISON; LOG BINOMIAL REGRESSION; LOG NORMAL REGRESSION; LOGISTIC REGRESSION ANALYSIS; MEASUREMENT; POISSON DISTRIBUTION; POWER ANALYSIS; RANDOMIZED CONTROLLED TRIAL; RISK FACTOR; SIMULATION;

EID: 79958112552     PISSN: 15574679     EISSN: 15574679     Source Type: Journal    
DOI: 10.2202/1557-4679.1323     Document Type: Article

References (53)

1. Ahmad, F., Hogg-Johnson, S., Stewart, D. E., Skinner, H. A., Glazier, R. H. and Levinson, W. (2009). Computer-assisted screening for intimate partner violence and control: a randomized trial. Annals of Internal Medicine 151(2):93-102.
2. Austin, P. C. (2007). A comparison of the statistical power of different methods for the analysis of cluster randomization trials with binary outcomes. Statistics in Medicine 26(19):3550-3565. (Pubitemid 47180697)
3. Barros, A. J. and Hirakata, V. N. (2003). Alternatives for logistic regression in cross-sectional studies: an empirical comparison of models that directly estimate the prevalence ratio. BMC Medical Research Methodology 3:21.
4. Blizzard, L. and Hosmer, D. W. (2006). Parameter estimation and goodness-of-fit in log binomial regression. Biometrical Journal 48(1):5-22. (Pubitemid 43331719)
5. Boardman, L. A., Steinhoff, M. M., Shackelton, R., Weitzen, S. and Crowthers, L. (2004). A randomized trial of the Fischer cone biopsy excisor and loop electrosurgical excision procedure. Obstetrics and Gynecology 104(4):745-750. (Pubitemid 40458803)
6. Bubner, T. K., Laurence, C., Gialamas, A., Yelland, L. N., Ryan, P., Willson, K. J., Tideman, P., Worley, P. and Beilby, J. J. (2009). Effectiveness of pointof-care testing for therapeutic control of chronic conditions: results from the PoCT in General Practice Trial. Medical Journal of Australia 190(11):624-626.
7. Cannon, M. J., Warner, L., Taddei, J. A. and Kleinbaum, D. G. (2001). What can go wrong when you assume that correlated data are independent: an illustration from the evaluation of a childhood health intervention in Brazil. Statistics in Medicine 20(9-10):1461-1467. (Pubitemid 32433191)
8. Carter, R. E., Lipsitz, S. R. and Tilley, B. C. (2005). Quasi-likelihood estimation for relative risk regression models. Biostatistics 6(1):39-44. (Pubitemid 41240368)
9. Colak, E. and Ozdamar, K. (2007). Comparison of generalized estimating equations' performance in clustered binary observations. Trakya Universitesi Tip Fakultesi Dergisi 24(3):222-227.
10. Cummings, P. (2009). The relative merits of risk ratios and odds ratios. Archives of Pediatrics & Adolescent Medicine 163(5):438-445.
11. Dahmen, G. and Ziegler, A. (2004). Generalized estimating equations in controlled clinical trials: hypotheses testing. Biometrical Journal 46(2):214-232. (Pubitemid 41449121)
12. Deddens, J., Petersen, M. R. and Lei, X. (2003). Estimation of prevalence ratios when PROC GENMOD does not converge. Proceedings of the 28th Annual SAS Users Group International Conference, Paper 270-28. SAS Institute Inc., Cary, NC.
13. Elley, C. R., Kerse, N., Chondros, P. and Robinson, E. (2005). Intraclass correlation coefficients from three cluster randomised controlled trials in primary and residential health care. Australian and New Zealand Journal of Public Health 29(5):461-467. (Pubitemid 41535952)
14. Fitzmaurice, G. M. (1995). A caveat concerning independence estimating equations with multivariate binary data. Biometrics 51(1):309-317.
15. Flanders, W. D. and Rhodes, P. H. (1987). Large sample confidence intervals for regression standardized risks, risk ratios, and risk differences. Journal of Chronic Diseases 40(7):697-704. (Pubitemid 17113476)
16. Green, B. B., Cook, A. J., Ralston, J. D., Fishman, P. A., Catz, S. L., Carlson, J., Carrell, D., Tyll, L., Larson, E. B. and Thompson, R. S. (2008). Effectiveness of home blood pressure monitoring, web communication, and pharmacist care on hypertension control: a randomized controlled trial. Journal of the American Medical Association 299(24):2857-2867. (Pubitemid 351930885)
17. Hammill, B. G. and Preisser, J. S. (2006). A SAS/IML software program for GEE and regression diagnostics. Computational Statistics & Data Analysis 51(2):1197-1212. (Pubitemid 44635894)
18. Hanley, J. A., Negassa, A. and Edwardes, M. D. D. (2000). GEE analysis of negatively correlated binary responses: a caution. Statistics in Medicine 19(5):715-722. (Pubitemid 30143562)
19. Kleinman, L. C. and Norton, E. C. (2009). What's the risk? A simple approach for estimating adjusted risk measures from nonlinear models including logistic regression. Health Services Research 44(1):288-302.
20. Laurence, C., Gialamas, A., Yelland, L., Bubner, T., Ryan, P., Willson, K., Glastonbury, B., Gill, J., Shephard, M. and Beilby, J. (2008). A pragmatic cluster randomised controlled trial to evaluate the safety, clinical effectiveness, cost effectiveness and satisfaction with point of care testing in a general practice setting - Rationale, design and baseline characteristics. Trials 9:50.
21. Lee, J. (1994). Odds ratio or relative risk for cross-sectional data. International Journal of Epidemiology 23(1):201-203. (Pubitemid 24088622)
22. Liang, K. Y. and Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika 73(1):13-22.
23. Localio, A. R., Margolis, D. J. and Berlin, J. A. (2007). Relative risks and confidence intervals were easily computed indirectly from multivariable logistic regression. Journal of Clinical Epidemiology 60(9):874-882. (Pubitemid 47208538)
24. Lu, B., Preisser, J. S., Qaqish, B. F., Suchindran, C., Bangdiwala, S. and Wolfson, M. (2007). A comparison of two bias-corrected covariance estimators for generalized estimating equations. Biometrics 63(3):935-941. (Pubitemid 47352842)
25. Lumley, T., Kronmal, R. and Ma, S. (2006). Relative risk regression in medical research: models, contrasts, estimators, and algorithms. UW Biostatistics Working Paper Series: Working Paper 293.
26. Mancl, L. A. and DeRouen, T. A. (2001). A covariance estimator for GEE with improved small-sample properties. Biometrics 57(1):126-134. (Pubitemid 32200146)
27. Mancl, L. A. and Leroux, B. G. (1996). Efficiency of regression estimates for clustered data. Biometrics 52(2):500-511. (Pubitemid 26187056)
28. Mason, S., Knowles, E., Colwell, B., Dixon, S., Wardrope, J., Gorringe, R., Snooks, H., Perrin, J. and Nicholl, J. (2007). Effectiveness of paramedic practitioners in attending 999 calls from elderly people in the community: cluster randomised controlled trial. British Medical Journal 335:919.
29. McNutt, L. A., Wu, C. T., Xue, X. N. and Hafner, J. P. (2003). Estimating the relative risk in cohort studies and clinical trials of common outcomes. American Journal of Epidemiology 157(10):940-943. (Pubitemid 36555054)
30. Miglioretti, D. L. and Heagerty, P. J. (2007). Marginal modeling of nonnested multilevel data using standard software. American Journal of Epidemiology 165(4):453-463.
31. Neuhaus, J. M. (1992). Statistical methods for longitudinal and clustered designs with binary responses. Statistical Methods in Medical Research 1(3):249-73.
32. Neuhaus, J. M. and Kalbfleisch, J. D. (1998). Between-and within-cluster covariate effects in the analysis of clustered data. Biometrics 54(2):638-645. (Pubitemid 28262485)
33. Neuhaus, J. M., Kalbfleisch, J. D. and Hauck, W. W. (1991). A comparison of cluster-specific and population-averaged approaches for analyzing correlated binary data. International Statistical Review 59(1):25-35.
34. Parker, D. R., Evangelou, E. and Eaton, C. B. (2005). Intraclass correlation coefficients for cluster randomized trials in primary care: the cholesterol education and research trial (CEART). Contemporary Clinical Trials 26(2):260-267. (Pubitemid 40693613)
35. Pendergast, J. F., Gange, S. J., Newton, M. A., Lindstrom, M. J., Palta, M. and Fisher, M. R. (1991). A survey of methods for analyzing clustered binary response data. International Statistical Review 64(1):89-118.
36. Penman, A. D. and Johnson, W. D. (2009). Complementary log-log regression for the estimation of covariate-adjusted prevalence ratios in the analysis of data from cross-sectional studies. Biometrical Journal 51(3):433-442.
37. Petersen, M. R. and Deddens, J. A. (2008). A comparison of two methods for estimating prevalence ratios. BMC Medical Research Methodology 8:9.
38. Ritz, J. and Spiegelman, D. (2004). Equivalence of conditional and marginal regression models for clustered and longitudinal data. Statistical Methods in Medical Research 13(4):309-323. (Pubitemid 39149238)
39. Santos, C. A. S. T., Fiaccone, R. L., Oliveira, N. F., Cunha, S., Barreto, M. L., do Carmo, M. B. B., Moncayo, A. L., Rodrigues, L. C., Cooper, P. J. and Amorim, L. D. (2008). Estimating adjusted prevalence ratio in clustered cross-sectional epidemiological data. BMC Medical Research Methodology 8:80.
40. Schechtman, E. (2002). Odds ratio, relative risk, absolute risk reduction, and the number needed to treat - Which of these should we use? Value in Health 5(5):431-436.
41. Schouten, E. G., Dekker, J. M., Kok, F. J., Lecessie, S., Vanhouwelingen, H. C., Pool, J. and Vandenbroucke, J. P. (1993). Risk ratio and rate ratio estimation in case-cohort designs: hypertension and cardiovascular mortality. Statistics in Medicine 12(18):1733-1745. (Pubitemid 23272638)
42. Sinclair, J. C. and Bracken, M. B. (1994). Clinically useful measures of effect in binary analyses of randomized trials. Journal of Clinical Epidemiology 47(8):881-889. (Pubitemid 24268892)
43. Skov, T., Deddens, J., Petersen, M. R. and Endahl, L. (1998). Prevalence proportion ratios: estimation and hypothesis testing. International Journal of Epidemiology 27(1):91-95. (Pubitemid 28140509)
44. Smeeth, L. and Ng, E. S. W. (2002). Intraclass correlation coefficients for cluster randomized trials in primary care: data from the MRC Trial of the Assessment and Management of Older People in the Community. Controlled Clinical Trials 23(4):409-421. (Pubitemid 34874892)
45. Spiegelman, D. and Hertzmark, E. (2005). Easy SAS calculations for risk or prevalence ratios and differences. American Journal of Epidemiology 162(3):199-200. (Pubitemid 41373523)
46. Wacholder, S. (1986). Binomial regression in GLIM: estimating risk ratios and risk differences. American Journal of Epidemiology 123(1):174-184. (Pubitemid 16189710)
47. Walter, S. D. (2000). Choice of effect measure for epidemiological data. Journal of Clinical Epidemiology 53(9):931-939.
48. Yelland, L. N., Salter, A. B. and Ryan, P. (2011). Relative risk estimation in randomized controlled trials: a comparison of methods for independent observations. The International Journal of Biostatistics 7(1):5.
49. Yelland, L. N., Salter, A. B., Ryan, P. and Laurence, C. O. (2011). Adjusted intraclass correlation coefficients for binary data: methods and estimates from a cluster-randomized trial in primary care. Clinical Trials 8:48-58.
50. Yu, B. B. and Wang, Z. Q. (2008). Estimating relative risks for common outcome using PROC NLP. Computer Methods and Programs in Biomedicine 90(2):179-186.
51. Zeng, L. X., Cheng, Y., Dang, S. N., Yan, H., Dibley, M. J., Chang, S. Y. and Kong, L. Z. (2008). Impact of micronutrient supplementation during pregnancy on birth weight, duration of gestation, and perinatal mortality in rural western China: double blind cluster randomised controlled trial. British Medical Journal 337: a2001.
52. Zhang, J. and Yu, K. F. (1998). What's the relative risk? A method of correcting the odds ratio in cohort studies of common outcomes. Journal of the American Medical Association 280(19):1690-1691. (Pubitemid 28532993)
53. Zou, G. Y. (2004). A modified Poisson regression approach to prospective studies with binary data. American Journal of Epidemiology 159(7):702-706.