1932

Abstract

This article reviews the essential ingredients and innovations in the design and analysis of group-randomized trials. The methods literature for these trials has grown steadily since they were introduced to the biomedical research community in the late 1970s, and we summarize those developments. We review, in addition to the group-randomized trial, methods for two closely related designs, the individually randomized group treatment trial and the stepped-wedge group-randomized trial. After describing the essential ingredients for these designs, we review the most important developments in the evolution of their methods using a new bibliometric tool developed at the National Institutes of Health. We then discuss the questions to be considered when selecting from among these designs or selecting the traditional randomized controlled trial. We close with a review of current methods for the analysis of data from these designs, a case study to illustrate each design, and a brief summary.

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2020-04-01
2024-04-20
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Literature Cited

  1. 1. 
    Adams G, Gulliford MC, Ukoumunne OC, Eldridge S, Chinn S, Campbell MJ 2004. Patterns of intra-cluster correlation from primary care research to inform study design and analysis. J. Clin. Epidemiol. 57:785–94
    [Google Scholar]
  2. 2. 
    Agbla SC, DiazOrdaz K. 2018. Reporting non-adherence in cluster randomised trials: a systematic review. Clin. Trials 15:294–304
    [Google Scholar]
  3. 3. 
    Andridge RR, Shoben AB, Muller KE, Murray DM 2014. Analytic methods for individually randomized group treatment trials and group-randomized trials when subjects belong to multiple groups. Stat. Med. 33:2178–90
    [Google Scholar]
  4. 4. 
    Baio G, Copas A, Ambler G, Hargreaves J, Beard E, Omar RZ 2015. Sample size calculation for a stepped wedge trial. Trials 16:354
    [Google Scholar]
  5. 5. 
    Baldwin SA, Bauer DJ, Stice E, Rohde P 2011. Evaluating models for partially clustered designs. Psychol. Methods 16:149–65
    [Google Scholar]
  6. 6. 
    Baldwin SA, Murray DM, Shadish WR 2005. Empirically supported treatments or type I errors? Problems with the analysis of data from group-administered treatments. J. Consult. Clin. Psychol. 73:924–35
    [Google Scholar]
  7. 7. 
    Barker D, McElduff P, D'Este C, Campbell MJ 2016. Stepped wedge cluster randomised trials: a review of the statistical methodology used and available. BMC Med. Res. Methodol. 16:69
    [Google Scholar]
  8. 8. 
    Bauer DJ, Gottfredson NC, Dean D, Zucker RA 2013. Analyzing repeated measures data on individuals nested within groups: accounting for dynamic group effects. Psychol. Methods 18:1–14
    [Google Scholar]
  9. 9. 
    Bauer DJ, Sterba SK, Hallfors DD 2008. Evaluating group-based interventions when control participants are ungrouped. Multivariate Behav. Res. 43:210–36
    [Google Scholar]
  10. 10. 
    Beard E, Lewis JJ, Copas A, Davey C, Osrin D et al. 2015. Stepped wedge randomised controlled trials: systematic review of studies published between 2010 and 2014. Trials 16:353
    [Google Scholar]
  11. 11. 
    Bland JM, Kerry SM. 1997. Statistics notes. Trials randomised in clusters. BMJ 315:600
    [Google Scholar]
  12. 12. 
    Boutron I, Moher D, Altman DG, Schulz KF, Ravaud P 2008. Extending the CONSORT statement to randomized trials of nonpharmacologic treatment: explanation and elaboration. Ann. Intern. Med. 148:295–309
    [Google Scholar]
  13. 13. 
    Bowater RJ, Abdelmalik SM, Lilford RJ 2009. The methodological quality of cluster randomised controlled trials for managing tropical parasitic disease: a review of trials published from 1998 to 2007. Trans. R. Soc. Trop. Med. Hyg. 103:429–36
    [Google Scholar]
  14. 14. 
    Brown CA, Lilford RJ. 2006. The stepped wedge trial design: a systematic review. BMC Med. Res. Methodol. 6:54
    [Google Scholar]
  15. 15. 
    Bryk AS, Raudenbush SW. 1992. Hierarchical Linear Models: Applications and Data Analysis Methods Newbury Park, CA: Sage
  16. 16. 
    Campbell MJ, Walters SJ. 2014. How to Design, Analyse and Report Cluster Randomised Trials in Medicine and Health Related Research Chichester, UK: Wiley
  17. 17. 
    Campbell MK, Elbourne DR, Altman DG 2004. CONSORT statement: extension to cluster randomised trials. BMJ 328:702–8
    [Google Scholar]
  18. 18. 
    Campbell MK, Piaggio G, Elbourne DR, Altman DG 2012. Consort 2010 statement: extension to cluster randomised trials. BMJ 345:e5661
    [Google Scholar]
  19. 19. 
    Copas AJ, Lewis JJ, Thompson JA, Davey C, Baio G, Hargreaves JR 2015. Designing a stepped wedge trial: three main designs, carry-over effects and randomisation approaches. Trials 16:352
    [Google Scholar]
  20. 20. 
    Cornfield J. 1978. Randomization by group: a formal analysis. Am. J. Epidemiol. 108:100–2
    [Google Scholar]
  21. 21. 
    Crits-Christoph P, Mintz J. 1991. Implications of therapist effects for the design and analysis of comparative studies of psychotherapies. J. Consult. Clin. Psychol. 59:20–26
    [Google Scholar]
  22. 22. 
    Davey C, Hargreaves J, Thompson JA, Copas AJ, Beard E et al. 2015. Analysis and reporting of stepped wedge randomised controlled trials: synthesis and critical appraisal of published studies, 2010 to 2014. Trials 16:358
    [Google Scholar]
  23. 23. 
    de Hoop E, Woertman W, Teerenstra S 2013. The stepped wedge cluster randomized trial always requires fewer clusters but not always fewer measurements, that is, participants than a parallel cluster randomized trial in a cross-sectional design. In reply. J. Clin. Epidemiol. 66:1428
    [Google Scholar]
  24. 24. 
    Desai M, Bryson SW, Robinson T 2013. On the use of robust estimators for standard errors in the presence of clustering when clustering membership is misspecified. Contemp. Clin. Trials 34:248–56
    [Google Scholar]
  25. 25. 
    Diaz-Ordaz K, Froud R, Sheehan B, Eldridge S 2013. A systematic review of cluster randomised trials in residential facilities for older people suggests how to improve quality. BMC Med. Res. Methodol. 13:127
    [Google Scholar]
  26. 26. 
    Diaz-Ordaz K, Kenward MG, Cohen A, Coleman CL, Eldridge S 2014. Are missing data adequately handled in cluster randomised trials? A systematic review and guidelines. Clin. Trials 11:590–600
    [Google Scholar]
  27. 27. 
    Donner A, Birkett N, Buck C 1981. Randomization by cluster: sample size requirements and analysis. Am. J. Epidemiol. 114:906–14
    [Google Scholar]
  28. 28. 
    Donner A, Brown KS, Brasher P 1990. A methodological review of non-therapeutic intervention trials employing cluster randomization, 1979–1989. Int. J. Epidemiol. 19:795–800
    [Google Scholar]
  29. 29. 
    Donner A, Klar N. 2000. Design and Analysis of Cluster Randomization Trials in Health Research London: Arnold
  30. 30. 
    Donner A, Klar N. 2002. Issues in the meta-analysis of cluster randomized trials. Stat. Med. 21:2971–80
    [Google Scholar]
  31. 31. 
    Eccles M, Grimshaw J, Campbell M, Ramsay C 2003. Research designs for studies evaluating the effectiveness of change and improvement strategies. Qual. Saf. Health Care 12:47–52
    [Google Scholar]
  32. 32. 
    Eldridge S, Ashby D, Bennett C, Wakelin M, Feder G 2008. Internal and external validity of cluster randomised trials: systematic review of recent trials. BMJ 336:876–80
    [Google Scholar]
  33. 33. 
    Eldridge S, Kerry S. 2012. A Practical Guide to Cluster Randomized Trials in Health Research London: Arnold
  34. 34. 
    Eldridge SM, Ashby D, Feder GS, Rudnicka AR, Ukoumunne OC 2004. Lessons for cluster randomized trials in the twenty-first century: a systematic review of trials in primary care. Clin. Trials 1:80–90
    [Google Scholar]
  35. 35. 
    Eldridge SM, Ukoumunne OC, Carlin JB 2009. The intra-cluster correlation coefficient in cluster randomized trials: a review of definitions. Int. Stat. Rev. 77:378–94
    [Google Scholar]
  36. 36. 
    Emsley R, Dunn G, White IR 2010. Mediation and moderation of treatment effects in randomised controlled trials of complex interventions. Stat. Methods Med. Res. 19:237–70
    [Google Scholar]
  37. 37. 
    Fay M, Graubard P. 2001. Small-sample adjustments for Wald-type tests using sandwich estimators. Biometrics 57:1198–206
    [Google Scholar]
  38. 38. 
    Feldman HA. 1988. Families of lines: random effects in linear regression analysis. J. Appl. Physiol. 64:1721–32
    [Google Scholar]
  39. 39. 
    Feng Z, McLerran D, Grizzle J 1996. A comparison of statistical methods for clustered data analysis with Gaussian error. Stat. Med. 15:1793–806
    [Google Scholar]
  40. 40. 
    Fiero MH, Huang S, Oren E, Bell ML 2016. Statistical analysis and handling of missing data in cluster randomized trials: a systematic review. Trials 17:72
    [Google Scholar]
  41. 41. 
    Fok CC, Henry D, Allen J 2015. Research designs for intervention research with small samples II: stepped wedge and interrupted time-series designs. Prev. Sci. 16:967–77
    [Google Scholar]
  42. 42. 
    Ford WP, Westgate PM. 2017. Improved standard error estimator for maintaining the validity of inference in cluster randomized trials with a small number of clusters. Biom. J. 59:478–95
    [Google Scholar]
  43. 43. 
    Froud R, Eldridge S, Diaz Ordaz K, Marinho VC, Donner A 2012. Quality of cluster randomized controlled trials in oral health: a systematic review of reports published between 2005 and 2009. Community Dent. Oral. Epidemiol. 40:Suppl. 13–14
    [Google Scholar]
  44. 44. 
    Gail MH, Mark SD, Carroll RJ, Green SB, Pee D 1996. On design considerations and randomization-based inference for community intervention trials. Stat. Med. 15:1069–92
    [Google Scholar]
  45. 45. 
    Girling AJ, Hemming K. 2016. Statistical efficiency and optimal design for stepped cluster studies under linear mixed effects models. Stat. Med. 35:2149–66
    [Google Scholar]
  46. 46. 
    Goldstein H. 1987. Multilevel Models in Educational and Social Research New York: Oxford Univ. Press
  47. 47. 
    Goldstein H. 1995. Multilevel Statistical Models London: Edward Arnold
  48. 48. 
    Goldstein H. 2003. Multilevel Statistical Models London: Edward Arnold, 3rd ed..
  49. 49. 
    Goldstein H. 2011. Multilevel Statistical Modeling Chichester, UK: Wiley, 4th ed..
  50. 50. 
    Grant A, Treweek S, Dreischulte T, Foy R, Guthrie B 2013. Process evaluations for cluster-randomised trials of complex interventions: a proposed framework for design and reporting. Trials 14:15
    [Google Scholar]
  51. 51. 
    Grantham KL, Kasza J, Heritier S, Hemming K, Forbes AB 2019. Accounting for a decaying correlation structure in cluster randomized trials with continuous recruitment. Stat. Med. 38:1918–34
    [Google Scholar]
  52. 52. 
    Grayling MJ, Wason JMS, Mander AP 2017. Stepped wedge cluster randomized controlled trial designs: a review of reporting quality and design features. Trials 18:33
    [Google Scholar]
  53. 53. 
    Gulliford MC, Ukoumunne OC, Chinn S 1999. Components of variance and intraclass correlations for the design of community-based surveys and intervention studies: data from the Health Survey for England 1994. Am. J. Epidemiol. 149:876–83
    [Google Scholar]
  54. 54. 
    Hayes RJ, Bennett S. 1999. Simple sample size calculation for cluster-randomized trials. Int. J. Epidemiol. 28:319–26
    [Google Scholar]
  55. 55. 
    Hayes RJ, Moulton LH. 2009. Cluster Randomised Trials Boca Raton, FL: CRC Press
  56. 56. 
    Hayes RJ, Moulton LH. 2017. Cluster Randomised Trials Boca Raton, FL: CRC Press, 2nd ed..
  57. 57. 
    Hedeker D, Gibbons RD. 1994. A random-effects ordinal regression model for multilevel analysis. Biometrics 50:933–44
    [Google Scholar]
  58. 58. 
    Hedeker D, Gibbons RD. 1996. MIXOR: a computer program for mixed-effects ordinal regression analysis. Comput. Methods Programs Biomed. 49:157–76
    [Google Scholar]
  59. 59. 
    Hedges LV, Hedberg EC. 2007. Intraclass correlation values for planning group-randomized trials in education. Educ. Eval. Policy Anal. 29:60–87
    [Google Scholar]
  60. 60. 
    Hemming K, Girling A. 2013. The efficiency of stepped wedge versus cluster randomized trials: stepped wedge studies do not always require a smaller sample size. J. Clin. Epidemiol. 66:1427–28
    [Google Scholar]
  61. 61. 
    Hemming K, Haines TP, Chilton PJ, Girling AJ, Lilford RJ 2015. The stepped wedge cluster randomised trial: rationale, design, analysis, and reporting. BMJ 350:h391
    [Google Scholar]
  62. 62. 
    Hemming K, Kasza J, Hooper R, Forbes A, Taljaard M 2020. A tutorial on sample size calculation for cluster randomised multiple-period parallel, cross-over and stepped-wedge trials using the Shiny CRT Calculator. Int. J. Epidemiol. In press
    [Google Scholar]
  63. 63. 
    Hemming K, Lilford R, Girling AJ 2015. Stepped-wedge cluster randomised controlled trials: a generic framework including parallel and multiple-level designs. Stat. Med. 34:181–96
    [Google Scholar]
  64. 64. 
    Hemming K, Taljaard M. 2016. Sample size calculations for stepped wedge and cluster randomised trials: a unified approach. J. Clin. Epidemiol. 69:137–46
    [Google Scholar]
  65. 65. 
    Hemming K, Taljaard M, Forbes A 2017. Analysis of cluster randomised stepped wedge trials with repeated cross-sectional samples. Trials 18:101
    [Google Scholar]
  66. 66. 
    Hemming K, Taljaard M, Forbes A 2018. Modeling clustering and treatment effect heterogeneity in parallel and stepped-wedge cluster randomized trials. Stat. Med. 37:883–98
    [Google Scholar]
  67. 67. 
    Hemming K, Taljaard M, McKenzie JE, Hooper R, Copas A et al. 2018. Reporting of stepped wedge cluster randomised trials: extension of the CONSORT 2010 statement with explanation and elaboration. BMJ363–k1614
    [Google Scholar]
  68. 68. 
    Heo M, Litwin AH, Blackstock O, Kim N, Arnsten JH 2017. Sample size determinations for group-based randomized clinical trials with different levels of data hierarchy between experimental and control arms. Stat. Methods Med. Res. 26:399–413
    [Google Scholar]
  69. 69. 
    Heo M, Nair SR, Wylie-Rosett J, Faith MS, Pietrobelli A et al. 2018. Trial characteristics and appropriateness of statistical methods applied for design and analysis of randomized school-based studies addressing weight-related issues: a literature review. J. Obes. 2018:8767315
    [Google Scholar]
  70. 70. 
    Hooper R, Forbes A, Hemming K, Takeda A, Beresford L 2018. Analysis of cluster randomised trials with an assessment of outcome at baseline. BMJ 360:k1121
    [Google Scholar]
  71. 71. 
    Huang S, Fiero MH, Bell ML 2016. Generalized estimating equations in cluster randomized trials with a small number of clusters: review of practice and simulation study. Clin. Trials 13:445–49
    [Google Scholar]
  72. 72. 
    Hughes JP, Granston TS, Heagerty PJ 2015. Current issues in the design and analysis of stepped wedge trials. Contemp. Clin. Trials 45:55–60
    [Google Scholar]
  73. 73. 
    Hussey MA, Hughes JP. 2007. Design and analysis of stepped wedge cluster randomized trials. Contemp. Clin. Trials 28:182–91
    [Google Scholar]
  74. 74. 
    Hutchins BI, Yuan X, Anderson JM, Santangelo GM 2016. Relative citation ratio (RCR): a new metric that uses citation rates to measure influence at the article level. PLOS Biol 14:e1002541
    [Google Scholar]
  75. 75. 
    Ivers NM, Halperin IJ, Barnsley J, Grimshaw JM, Shah BR et al. 2012. Allocation techniques for balance at baseline in cluster randomized trials: a methodological review. Trials 13:120
    [Google Scholar]
  76. 76. 
    Ivers NM, Taljaard M, Dixon S, Bennett C, McRae A et al. 2011. Impact of CONSORT extension for cluster randomised trials on quality of reporting and study methodology: review of random sample of 300 trials, 2000–8. BMJ 343:d5886
    [Google Scholar]
  77. 77. 
    Ji XY, Fink G, Robyn PJ, Small DS 2017. Randomization inference for stepped-wedge cluster-randomized trials: an application to community-based health insurance. Ann. Appl. Stat. 11:1–20
    [Google Scholar]
  78. 78. 
    Kahan BC, Morris TP. 2013. Assessing potential sources of clustering in individually randomised trials. BMC Med. Res. Methodol. 13:58
    [Google Scholar]
  79. 79. 
    Kasza J, Forbes AB. 2019. Inference for the treatment effect in multiple-period cluster randomised trials when random effect correlation structure is misspecified. Stat. Methods Med. Res. 28:3112–22
    [Google Scholar]
  80. 80. 
    Kasza J, Hemming K, Hooper R, Matthews J, Forbes AB et al. 2019. Impact of non-uniform correlation structure on sample size and power in multiple-period cluster randomised trials. Stat. Methods Med. Res. 28:703–16
    [Google Scholar]
  81. 81. 
    Kauermann G, Carroll RJ. 2001. A note on the efficiency of sandwich covariance matrix estimation. J. Am. Stat. Assoc. 96:1387–96
    [Google Scholar]
  82. 82. 
    Kenward MG, Roger JH. 1997. Small sample inference for fixed effects from restricted maximum likelihood. Biometrics 53:983–97
    [Google Scholar]
  83. 83. 
    Kish L. 1965. Survey Sampling New York: Wiley
  84. 84. 
    Klar N, Darlington G. 2004. Methods for modelling change in cluster randomization trials. Stat. Med. 23:2341–57
    [Google Scholar]
  85. 85. 
    Kreidler SM, Muller KE, Grunwald GK, Ringham BM, Coker-Dukowitz ZT et al. 2013. GLIMMPSE: online power computation for linear models with and without a baseline covariate. J. Stat. Softw. 54:i10
    [Google Scholar]
  86. 86. 
    Kristunas C, Morris T, Gray L 2017. Unequal cluster sizes in stepped-wedge cluster randomised trials: a systematic review. BMJ Open 7:e017151
    [Google Scholar]
  87. 87. 
    Krull JL, MacKinnon DP. 1999. Multilevel mediation modeling in group-based intervention studies. Eval. Rev. 23:418–44
    [Google Scholar]
  88. 88. 
    Krull JL, MacKinnon DP. 2001. Multilevel modeling of individual and group level mediated effects. Multivariate Behav. Res. 36:249–77
    [Google Scholar]
  89. 89. 
    Lee KJ, Thompson SG. 2005. Clustering by health professional in individually randomised trials. BMJ 330:142–44
    [Google Scholar]
  90. 90. 
    Lee KJ, Thompson SG. 2005. The use of random effects models to allow for clustering in individually randomized trials. Clin. Trials 2:163–73
    [Google Scholar]
  91. 91. 
    Li F, Turner EL, Preisser JS 2018. Sample size determination for GEE analyses of stepped wedge cluster randomized trials. Biometrics 74:1450–58
    [Google Scholar]
  92. 92. 
    Li P, Redden DT. 2015. 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. 15:38
    [Google Scholar]
  93. 93. 
    Li P, Redden DT. 2015. Small sample performance of bias-corrected sandwich estimators for cluster-randomized trials with binary outcomes. Stat. Med. 34:281–96
    [Google Scholar]
  94. 94. 
    Lindquist EF. 1953. Design and Analysis of Experiments in Psychology and Education Boston: Houghton Mifflin
  95. 95. 
    Lohr S, Schochet PZ, Sanders E 2014. Partially nested randomized controlled trials in education research: a guide to design and analysis NCER 2014-2000, Natl. Cent. Educ. Res., Inst. Educ. Sci., US Dep. Educ Washington, DC: https://ies.ed.gov/ncer/pubs/20142000/pdf/20142000.pdf
  96. 96. 
    Luo W, Cappaert KJ, Ning L 2015. Modelling partially cross-classified multilevel data. Br. J. Math. Stat. Psychol. 68:342–62
    [Google Scholar]
  97. 97. 
    Mancl LA, DeRouen TA. 2001. A covariance estimator for GEE with improved small-sample properties. Biometrics 57:126–34
    [Google Scholar]
  98. 98. 
    Martin J, Taljaard M, Girling A, Hemming K 2016. Systematic review finds major deficiencies in sample size methodology and reporting for stepped-wedge cluster randomised trials. BMJ Open 6:e010166
    [Google Scholar]
  99. 99. 
    Martindale C. 1978. The therapist-as-fixed-effect fallacy in psychotherapy research. J. Consult. Clin. Psychol. 46:1526–30
    [Google Scholar]
  100. 100. 
    Mdege ND, Brabyn S, Hewitt C, Richardson R, Torgerson DJ 2014. The 2 × 2 cluster randomized controlled factorial trial design is mainly used for efficiency and to explore intervention interactions: a systematic review. J. Clin. Epidemiol. 67:1083–92
    [Google Scholar]
  101. 101. 
    Mdege ND, Man MS, Taylor Nee Brown CA, Torgerson DJ 2011. Systematic review of stepped wedge cluster randomized trials shows that design is particularly used to evaluate interventions during routine implementation. J. Clin. Epidemiol. 64:936–48
    [Google Scholar]
  102. 102. 
    Mhurchu CN, Gorton D, Turley M, Jiang Y, Michie J et al. 2013. Effects of a free school breakfast programme on children's attendance, academic achievement and short-term hunger: results from a stepped-wedge, cluster randomised controlled trial. J. Epidemiol. Community Health 67:257–64
    [Google Scholar]
  103. 103. 
    Moerbeek M, Teerenstra S. 2016. Power Analysis of Trials with Multilevel Data Boca Raton, FL: CRC
  104. 104. 
    Morel JG, Bokossa MC, Neerchal NK 2003. Small sample correction for the variance of GEE estimators. Biom. J. 45:395–409
    [Google Scholar]
  105. 105. 
    Murray DM. 1998. Design and Analysis of Group-Randomized Trials New York: Oxford Univ. Press
  106. 106. 
    Murray DM, Hannan PJ. 1990. Planning for the appropriate analysis in school-based drug-use prevention studies. J. Consult. Clin. Psychol. 58:458–68
    [Google Scholar]
  107. 107. 
    Murray DM, Hannan PJ, Baker WL 1996. A Monte Carlo study of alternative responses to intraclass correlation in community trials. Is it ever possible to avoid Cornfield's penalties?. Eval. Rev. 20:313–37
    [Google Scholar]
  108. 108. 
    Murray DM, Hannan PJ, Pals SP, McCowen RG, Baker WL, Blitstein JL 2006. 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. 25:375–88
    [Google Scholar]
  109. 109. 
    Murray DM, Hannan PJ, Wolfinger RD, Baker WL, Dwyer JH 1998. Analysis of data from group-randomized trials with repeat observations on the same groups. Stat. Med. 17:1581–600
    [Google Scholar]
  110. 110. 
    Murray DM, Pals SL, Blitstein JL, Alfano CM, Lehman J 2008. Design and analysis of group-randomized trials in cancer: a review of current practices. J. Natl. Cancer Inst. 100:483–91
    [Google Scholar]
  111. 111. 
    Murray DM, Pals SL, George SM, Kuzmichev A, Lai GY et al. 2018. Design and analysis of group-randomized trials in cancer: a review of current practices. Prev. Med. 111:241–47
    [Google Scholar]
  112. 112. 
    Murray DM, Pennell M, Rhoda D, Hade EM, Paskett ED 2010. Designing studies that would address the multilayered nature of health care. J. Natl. Cancer Inst. Monogr. 2010:90–96
    [Google Scholar]
  113. 113. 
    Murray DM, Varnell SP, Blitstein JL 2004. Design and analysis of group-randomized trials: a review of recent methodological developments. Am. J. Public Health 94:423–32
    [Google Scholar]
  114. 114. 
    Nickless A, Voysey M, Geddes J, Yu LM, Fanshawe TR 2018. Mixed effects approach to the analysis of the stepped wedge cluster randomised trial—investigating the confounding effect of time through simulation. PLOS ONE 13:e0208876
    [Google Scholar]
  115. 115. 
    Nye B, Konstantopoulos S, Hedges L 2004. How large are teacher effects. Educ. Eval. Policy Anal. 26:237–57
    [Google Scholar]
  116. 116. 
    Pals SL, Murray DM, Alfano CM, Shadish WR, Hannan PJ, Baker WL 2008. Individually randomized group treatment trials: a critical appraisal of frequently used design and analytic approaches. Am. J. Public Health 98:1418–24
    [Google Scholar]
  117. 117. 
    Pals SL, Wiegand RE, Murray DM 2011. Ignoring the group in group-level HIV/AIDS intervention trials: a review of reported design and analytic methods. AIDS 25:989–96
    [Google Scholar]
  118. 118. 
    Puffer S, Torgerson D, Watson J 2003. Evidence for risk of bias in cluster randomised trials: review of recent trials published in three general medical journals. BMJ 327:785–89
    [Google Scholar]
  119. 119. 
    Raab GM, Butcher I. 2005. Randomization inference for balanced cluster-randomized trials. Clin. Trials 2:130–40
    [Google Scholar]
  120. 120. 
    Rao JN, Scott AJ. 1992. A simple method for the analysis of clustered binary data. Biometrics 48:577–85
    [Google Scholar]
  121. 121. 
    Raudenbush SW. 1997. Statistical analysis and optimal design for cluster randomized trials. Psychol. Methods 2:173–85
    [Google Scholar]
  122. 122. 
    Raudenbush SW, Bryk AS. 2002. Hierarchical Linear Models: Applications and Data Analysis Methods Thousand Oaks, CA: Sage
  123. 123. 
    Richardson M, Garner P, Donegan S 2016. Cluster randomised trials in Cochrane Reviews: evaluation of methodological and reporting practice. PLOS ONE 11:e0151818
    [Google Scholar]
  124. 124. 
    Roberts C, Roberts SA. 2005. Design and analysis of clinical trials with clustering effects due to treatment. Clin. Trials 2:152–62
    [Google Scholar]
  125. 125. 
    Roberts C, Walwyn R. 2013. Design and analysis of non-pharmacological treatment trials with multiple therapists per patient. Stat. Med. 32:81–98
    [Google Scholar]
  126. 126. 
    Rutterford C, Taljaard M, Dixon S, Copas A, Eldridge S 2015. Reporting and methodological quality of sample size calculations in cluster randomized trials could be improved: a review. J. Clin. Epidemiol. 68:716–23
    [Google Scholar]
  127. 127. 
    Satterthwaite FE. 1946. An approximate distribution of estimates of variance components. Biometrics 2:110–14
    [Google Scholar]
  128. 128. 
    Scott JM, deCamp A, Juraska M, Fay MP, Gilbert PB 2017. Finite-sample corrected generalized estimating equation of population average treatment effects in stepped wedge cluster randomized trials. Stat. Methods Med. Res. 26:583–97
    [Google Scholar]
  129. 129. 
    Simpson JM, Klar N, Donner A 1995. Accounting for cluster randomization—a review of primary prevention trials, 1990 through 1993. Am. J. Public Health 85:1378–83
    [Google Scholar]
  130. 130. 
    South EC, Hohl BC, Kondo MC, MacDonald JM, Branas CC 2018. Effect of greening vacant land on mental health of community-dwelling adults: a cluster randomized trial. JAMA Netw. Open. 1:e180298
    [Google Scholar]
  131. 131. 
    Spatola CA, Manzoni GM, Castelnuovo G, Malfatto G, Facchini M et al. 2014. The ACTonHEART study: rationale and design of a randomized controlled clinical trial comparing a brief intervention based on acceptance and commitment therapy to usual secondary prevention care of coronary heart disease. Health Qual. Life Outcomes 12:22
    [Google Scholar]
  132. 132. 
    Sterba SK. 2017. Partially nested designs in psychotherapy trials: a review of modeling developments. Psychother. Res. 27:425–36
    [Google Scholar]
  133. 133. 
    Taljaard M, Hemming K, Shah L, Giraudeau B, Grimshaw JM, Weijer C 2017. Inadequacy of ethical conduct and reporting of stepped wedge cluster randomized trials: results from a systematic review. Clin. Trials 14:333–41
    [Google Scholar]
  134. 134. 
    Taljaard M, Weijer C, Grimshaw JM, Eccles MPOtt. Ethics Cluster Randomised Trials Consens. Group 2013. The Ottawa Statement on the ethical design and conduct of cluster randomised trials: precis for researchers and research ethics committees. BMJ 346:f2838
    [Google Scholar]
  135. 135. 
    Thompson JA, Davey C, Fielding K, Hargreaves JR, Hayes RJ 2018. Robust analysis of stepped wedge trials using cluster-level summaries within periods. Stat. Med. 37:2487–500
    [Google Scholar]
  136. 136. 
    Torgerson DJ. 2001. Contamination in trials: Is cluster randomisation the answer?. BMJ 322:355–57
    [Google Scholar]
  137. 137. 
    Turner EL, Li F, Gallis JA, Prague M, Murray DM 2017. Review of recent methodological developments in group-randomized trials: part 1-design. Am. J. Public Health 107:907–15
    [Google Scholar]
  138. 138. 
    Turner EL, Prague M, Gallis JA, Li F, Murray DM 2017. Review of recent methodological developments in group-randomized trials: part 2-analysis. Am. J. Public Health 107:1078–86
    [Google Scholar]
  139. 139. 
    Ukoumunne OC, Forbes AB, Carlin JB, Gulliford MC 2008. Comparison of the risk difference, risk ratio and odds ratio scales for quantifying the unadjusted intervention effect in cluster randomized trials. Stat. Med. 27:5143–55
    [Google Scholar]
  140. 140. 
    Varnell SP, Murray DM, Janega JB, Blitstein JL 2004. Design and analysis of group-randomized trials: a review of recent practices. Am. J. Public Health 94:393–99
    [Google Scholar]
  141. 141. 
    Walwyn R, Roberts C. 2010. Therapist variation within randomised trials of psychotherapy: implications for precision, internal and external validity. Stat. Methods Med. Res. 19:291–315
    [Google Scholar]
  142. 142. 
    Wang R, De Gruttola V 2017. The use of permutation tests for the analysis of parallel and stepped-wedge cluster-randomized trials. Stat. Med. 36:2831–43
    [Google Scholar]
  143. 143. 
    Whiting-O'Keefe QE, Henke C, Simborg DW 1984. Choosing the correct unit of analysis in medical care experiments. Med. Care 22:1101–14
    [Google Scholar]
  144. 144. 
    Woertman W, de Hoop E, Moerbeek M, Zuidema SU, Gerritsen DL, Teerenstra S 2013. Stepped wedge designs could reduce the required sample size in cluster randomized trials. J. Clin. Epidemiol. 66:752–58
    [Google Scholar]
  145. 145. 
    Wright N, Ivers N, Eldridge S, Taljaard M, Bremner S 2015. A review of the use of covariates in cluster randomized trials uncovers marked discrepancies between guidance and practice. J. Clin. Epidemiol. 68:603–9
    [Google Scholar]
  146. 146. 
    Wu S, Crespi CM, Wong WK 2012. Comparison of methods for estimating the intraclass correlation coefficient for binary responses in cancer prevention cluster randomized trials. Contemp. Clin. Trials 33:869–80
    [Google Scholar]
  147. 147. 
    Zhang K, Traskin M, Small DS 2012. A powerful and robust test statistic for randomization inference in group-randomized trials with matched pairs of groups. Biometrics 68:75–84
    [Google Scholar]
  148. 148. 
    Zou GY, Donner A. 2013. Extension of the modified Poisson regression model to prospective studies with correlated binary data. Stat. Methods Med. Res. 22:661–70
    [Google Scholar]
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