An evaluation of constrained randomization for the design and analysis of group-randomized trials with binary outcomes

Statistics in Medicine

Volumn 36, Issue 24, 2017, Pages 3791-3806

  Li, Fan ^a,b   Turner, Elizabeth L ^a   Heagerty, Patrick J ^c   Murray, David M ^d   Vollmer, William M ^e   DeLong, Elizabeth R ^a,b  

^a DUKE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b DUKE CLINICAL RESEARCH INSTITUTE   (United States)

^c UNIVERSITY OF WASHINGTON   (United States)

^d NATIONAL INSTITUTES OF HEALTH   (United States)

^e CENTER FOR HEALTH RESEARCH   (United States)

Author keywords

constrained randomization; generalized estimating equations; generalized linear mixed model; group randomized trial; permutation test

Indexed keywords

HUMAN; MONTE CARLO METHOD; OUTCOME ASSESSMENT; RANDOMIZATION; RANDOMIZED CONTROLLED TRIAL (TOPIC); STUDENT T TEST; COLORADO; COMPUTER SIMULATION; FEMALE; IMMUNIZATION; INFANT; INTERSECTORAL COLLABORATION; MALE; METHODOLOGY; PRESCHOOL CHILD; PROCEDURES; SAMPLE SIZE; STATISTICAL MODEL;

CHILD, PRESCHOOL; COLORADO; COMPUTER SIMULATION; FEMALE; HUMANS; IMMUNIZATION; INFANT; INTERSECTORAL COLLABORATION; LIKELIHOOD FUNCTIONS; MALE; MONTE CARLO METHOD; RANDOM ALLOCATION; RANDOMIZED CONTROLLED TRIALS AS TOPIC; RESEARCH DESIGN; SAMPLE SIZE;

EID: 85030182655     PISSN: 02776715     EISSN: 10970258     Source Type: Journal    
DOI: 10.1002/sim.7410     Document Type: Article

References (50)

1. Murray DM. Design and Analysis of Group-Randomized Trials. New York, NY: Oxford University Press; 1998.
2. Donner A, Klar N. Design and Analysis of Group-Randomized Trials in Health Research. London: Arnold; 2000.
3. Eldridge S, Kerry S. A Practical Guide to Cluster Randomised Trials in Health Services Research. Chichester, West Sussex: John Wiley & Sons; 2012.
4. Campbell MJ, Walters SJ. How to Design, Analyse and Report Cluster Randomised Trials in Medicine and Health Related Research. Chichester, West Sussex: John Wiley & Sons; 2014.
5. Hayes RJ, Moulton LH. Cluster Randomised Trials. Boca Raton, FL: Taylor & Francis Group, LLC; 2009.
6. Eldridge SM, Ukoumunne OC, Carlin JB. The intra-cluster correlation coefficient in cluster randomized trials: a review of definitions. Int Stat Rev. 2009;77(3):378-394.
7. Murray DM, Varnell SP, Blitstein JL. Design and analysis of group-randomized trials: a review of recent methodological developments. Am J Public Health. 2004;94(3):423-432.
8. Turner EL, Li F, Gallis JA, Prague M, Murray DM. Review of recent methodological developments in group-randomized trials: Part 1–design. Am J Public Health. 2017;107(6):907-915.
9. Ivers NM, Halperin IJ, Barnsley J, et al. Allocation techniques for balance at baseline in cluster randomized trials: a methodological review. Trials. 2012;13(1):120-128.
10. Kernan WN, Viscoli CM, Makuch RW, Brass LM, Horwitz RI. Stratified randomization for clinical trials. J Clin Epidemiol. 1999;52(1):19-26.
11. Donner A, Klar N. Pitfalls of and controversies in cluster randomization trials. Am J Public Health. 2004;94(3):416-422.
12. Moulton LH. Covariate-based constrained randomization of group-randomized trials. Clin Trials. 2004;1(3):297-305.
13. Raab GM, Butcher I. Balance in cluster randomized trials. Stat Med. 2001;20(3):351-365.
14. Hernandez AV, Steyerberg EW, Habbema JDF. Covariate adjustment in randomized controlled trials with dichotomous outcomes increases statistical power and reduces sample size requirements. J Clin Epidemiol. 2004;57(5):454-460.
15. Raab GM, Day S, Sales J. How to select covariates to include in the analysis of a clinical trial. Controlled Clin Trials. 2000;21(4):330-342.
16. Wright N, Ivers N, Eldridge S, Taljaard M, Bremner S. A review of the use of covariates in cluster randomized trials uncovers marked discrepancies between guidance and practice. J Clin Epidemiol. 2015;68(6):603-609.
17. Austin PC. A comparison of the statistical power of different methods for the analysis of cluster randomization trials with binary outcomes. Stat Med. 2007;26(19):3550-3565.
18. Li F, Lokhnygina Y, Murray DM, Heagerty PJ, Delong ER. An evaluation of constrained randomization for the design and analysis of group-randomized trials. Stat Med. 2015;35(10):1565-1579.
19. De Hoop E, Teerenstra S, Van Gaal BGI, Moerbeek M, Borm GF. The "best balance" allocation led to optimal balance in cluster-controlled trials. J Clin Epidemiol. 2012;65(2):132-137.
20. Dickinson LM, Beaty B, Fox C, Pace W, Dickinson WP, Emsermann C, Kempe A. Pragmatic cluster randomized trials using covariate constrained randomization: a method for practice-based research networks (PBRNs). J Am Board Fam Med. 2015;28(5):663-672.
21. Coronado GD, Vollmer WM, Petrik A, et al. Strategies and opportunities to STOP colon cancer in priority populations: design of a cluster-randomized pragmatic trial. Contemporary Clin Trials. 2014;38(2):344-349.
22. Wolfinger R, O'Connell M. Generalized linear mixed models: a pseudo-likelihood approach. J Stat Comput Simul. 1993;48(3):233-243.
23. Pinheiro JC, Bates DM. Mixed-Effects Models in S and S-PLUS. New York, NY: Springer; 2009.
24. Pinheiro JC, Bates DM. Approximations to the log-likelihood function in the nonlinear mixed-effects model. J Comput Graph Stat. 1995;4(1):12-35.
25. Li P, Redden DT. Comparing denominator degrees of freedom approximations for the generalized linear mixed model in analyzing binary outcome in small sample cluster-randomized trials. BMC Med Res Methodol. 2015;15(1):1-12.
26. Schluchter M, Elashoff JT. Small-sample adjustments to tests with unbalanced repeated measures assuming several covariance structures. J Stat Comput Simul. 1990;19(37):69-87.
27. Liang KY, Zeger SL. Longitudinal data analysis using generalized linear models. Biometrika. 1986;73(1):13-22.
28. McCulloch CE, Searle SR, Neuhaus JM. Generalized, Linear, and Mixed Models. Hoboken, New Jersey: John Wiley & Sons, Inc.; 2008.
29. Wedderburn RWM. Quasi-likelihood functions, generalized linear models, and the Gauss-Newton method. Biometrika. 1974;61(3):439-447.
30. Mancl LA, DeRouen TA. A covariance estimator for GEE with improved small-sample properties. Biometrics. 2001;57(1):126-134.
31. Kauermann G, Carroll RJ. A note on the efficiency of sandwich covariance matrix estimation. J Am Stat Assoc. 2001;96(456):1387-1396.
32. Fay MP, Graubard BI. Small-sample adjustments for Wald-type tests using sandwich estimators. Biometrics. 2001;57(4):1198-1206.
33. Pan W, Wall MM. Small-sample adjustments in using the sandwich variance estimator in generalized estimating equations. Stat Med. 2002;21(10):1429-1441.
34. Morel JG, Bokossa MC, Neerchal NK. Small sample correction for the variance of GEE estimators. Biom J. 2003;45(4):395-409.
35. Li Peng, Redden DT. Small sample performance of bias-corrected sandwich estimators for cluster-randomized trials with binary outcomes. Stat Med. 2015;34(2):281-296.
36. Lu B, Preisser JS, Qaqish BF, Suchindran C, Bangdiwala SI, Wolfson M. A comparison of two bias-corrected covariance estimators for generalized estimating equations. Biometrics. 2007;63(3):935-941.
37. Westgate PM. On small-sample inference in group randomized trials with binary outcomes and cluster-level covariates. Biom J. 2013;55(5):789-806.
38. Fisher RA. The Design of Experiments. Edinburgh: Oliver and Boyd; 1935.
39. Good PI. Permutation Tests. A Pratical Guide to Resampling Methods for Testing Hypotheses. New York: Springer-Verlag; 1994.
40. Edgington ES. Randomization Tests. New York: Marcel-Decker; 1987.
41. Gail MH, Mark SD, Carroll RJ, Green SB, Pee D. On design considerations and randomization-based inference for community intervention trials. Stat Med. 1996;15(11):1069-1092.
42. Braun TM, Feng Z. Optimal permutation tests for the analysis of group randomized trials. J Am Stat Assoc. 2001;96(456):1424-1432.
43. Murray DM, Hannan PJ, Pals SP, McCowen RG, Baker WL, Blitstein JL. A comparison of permutation and mixed-model regression methods for the analysis of simulated data in the context of a group-randomized trial. Stat Med. 2006;25(3):375-88.
44. Murray DM, Blitstein JL. Methods to reduce the impact of intraclass correlation in group-randomized trials. Eval Rev. 2003;27(1):79-103.
45. Liu Q, Pierce DA. A note on Gauss-Hermite quadrature. Biometrika. 1994;81(3):624-629.
46. Ford WP, Westgate PM. Improved standard error estimator for maintaining the validity of inference in cluster randomized trials with a small number of clusters. Biom J. 2017;59(3):478-495.
47. Turner EL, Prague M, Gallis JA, Li F, Murray DM. Review of recent methodological developments in group-randomized trials: Part 2–Analysis. Am J Public Health. 2017;107(7):1078-1086.
48. Diehr P, Martin DC, Koepsell T, Cheadle A. Breaking the matches in a paired t-test for community interventions when the number of pairs is small. Stat Med. 1995;14(13):1491-1504.
49. Proschan MA. On the distribution of the unpaired t-statistic with paired data. Stat Med. 1996;15(10):1059-1063.
50. Omar RZ, Thompson SG. Analysis of a cluster randomized trial with binary outcome data using a multi-level model. Stat Med. 2000;19(19):2675-2688.