1. Baier, RR, Mitchell, SL, Jutkowitz, E, et al. Identifying and supporting nonpharmacological dementia interventions ready for pragmatic trials: results from an expert workshop. J Am Med Dir Assoc2018; 19(7): 560–562.
Google Scholar | Crossref2. Zwarenstein, M . “Pragmatic” and “explanatory” attitudes to randomised trials. J Roy Soc Med 2017; 110(5): 208–218.
Google Scholar | SAGE Journals | ISI3. Mitchell, SL, Mor, V, Harrison, J, et al. Embedded pragmatic trials in dementia care: realizing the vision of the NIA IMPACT Collaboratory. J Am Geriatr Soc 2020; 68(Suppl. 2): S1–S7.
Google Scholar | Crossref | Medline4. Allore, HG, Goldfeld, KS, Gutman, R, et al. Statistical considerations for embedded pragmatic clinical trials in people living with dementia. J Am Geriatr Soc 2020; 68(Suppl. 2): S68–S73.
Google Scholar | Crossref | Medline5. Andridge, RR, Shoben, AB, Muller, KE, et al. Analytic methods for individually randomized group treatment trials and group-randomized trials when subjects belong to multiple groups. Stat Med 2014; 33(13): 2178–2190.
Google Scholar | Crossref | Medline6. Donner, A, Klar, N. Design and analysis of cluster randomization trials in health research. New York: Oxford University Press, 2000.
Google Scholar7. Fitzmaurice, GM, Laird, NM, Ware, JH. Applied longitudinal analysis. 2nd ed. Hoboken, NJ: Wiley, 2011, pp. Xxv +701.
Google Scholar | Crossref8. Hooper, R, Bourke, L. Cluster randomised trials with repeated cross sections: alternatives to parallel group designs. BMJ 2015; 350: h2925.
Google Scholar | Crossref | Medline9. Reed, RG, Butler, EA, Kenny, DA. Dyadic models for the study of health. Soc Pers Psychol Comp 2013; 7: 228–245.
Google Scholar | Crossref10. Kenny, DA . The effect of nonindependence on significance testing in dyadic research. Pers Relat 1995; 2: 67–75.
Google Scholar | Crossref | ISI11. Hardy, SE, Allore, H, Studenski, SA. Missing data: a special challenge in aging research. J Am Geriatr Soc 2009; 57(4): 722–729.
Google Scholar | Crossref | Medline | ISI12. Van Ness, PH, Charpentier, PA, Ip, EH, et al. Gerontologic biostatistics: the statistical challenges of clinical research with older study participants. J Am Geriatr Soc 2010; 58(7): 1386–1392.
Google Scholar | Crossref | Medline13. Turner, RM, Omar, RZ, Thompson, SG. Modelling multivariate outcomes in hierarchical data, with application to cluster randomised trials. Biom J 2006; 48(3): 333–345.
Google Scholar | Crossref | Medline | ISI14. Li, D, Cao, J, Zhang, S. Power analysis for cluster randomized trials with multiple binary co-primary endpoints. Biometrics 2020; 76(4): 1064–1074.
Google Scholar | Crossref | Medline15. Food and Drug Administration . Guidance for industry: Alzheimer’s disease: developing drugs for the treatment of early stage disease. Rockville, MD: U.S. Department of Health and Human Services, Food and Drug Administration, 2013.
Google Scholar16. Campbell, MK, Piaggio, G, Elbourne, DR, et al. Consort 2010 statement: extension to cluster randomised trials. BMJ 2012; 345: e5661.
Google Scholar | Crossref | Medline17. Hemming, K, Taljaard, M, McKenzie, JE, et al. Reporting of stepped wedge cluster randomised trials: extension of the CONSORT 2010 statement with explanation and elaboration. BMJ 2018; 363: k1614.
Google Scholar | Crossref | Medline18. Kahan, BC, Morris, TP. Reporting and analysis of trials using stratified randomisation in leading medical journals: review and reanalysis. BMJ 2012; 345: e5840.
Google Scholar | Crossref | Medline19. Taljaard, M, Weijer, C, Grimshaw, JM, et al. Developing a framework for the ethical design and conduct of pragmatic trials in healthcare: a mixed methods research protocol. Trials 2018; 19: 525.
Google Scholar | Crossref | Medline20. Taljaard, M, McDonald, S, Nicholls, SG, et al. A search filter to identify pragmatic trials in MEDLINE was highly specific but lacked sensitivity. J Clin Epidemiol 2020; 124: 75–84.
Google Scholar | Crossref | Medline21. Nicholls, SG, Carroll, K, Hey, SP, et al. A review of pragmatic trials found a high degree of diversity in design and scope, deficiencies in reporting and trial registry data, and poor indexing. J Clin Epidemiol 2021; 137: 45–57.
Google Scholar | Crossref | Medline22. ALOIS: a comprehensive, open-access register of dementia studies , https://alois.medsci.ox.ac.uk/about-alois (accessed 4 December 2020).
Google Scholar23. Diaz-Ordaz, K, Froud, R, Sheehan, B, et al. A systematic review of cluster randomised trials in residential facilities for older people suggests how to improve quality. BMC Med Res Methodol 2013; 13: 127.
Google Scholar | Crossref | Medline | ISI24. Loudon, K, Treweek, S, Sullivan, F, et al. The PRECIS-2 tool: designing trials that are fit for purpose. BMJ 2015; 350: h2147.
Google Scholar | Crossref | Medline25. Nicholls, SG, Zwarenstein, M, Hey, SP, et al. The importance of decision intent within descriptions of pragmatic trials. J Clin Epidemiol 2020; 125: 30–37.
Google Scholar | Crossref | Medline26. Zwarenstein, M, Treweek, S, Gagnier, JJ, et al. Improving the reporting of pragmatic trials: an extension of the CONSORT statement. BMJ 2008; 337: a2390.
Google Scholar | Crossref | Medline27. NIA IMPACT Collaboratory: transforming dementia care , https://impactcollaboratory.org/ (accessed 13 December 2020).
Google Scholar28. Lee, EJ, Patel, A, Acedillo, RR, et al. Cultivating innovative pragmatic cluster-randomized registry trials embedded in hemodialysis care: workshop proceedings from 2018. Can J Kidney Health Dis2019; 6: 1–12.
Google Scholar | SAGE Journals29. Murray, DM, Taljaard, M, Turner, EL, et al. Essential ingredients and innovations in the design and analysis of group-randomized trials. Ann Rev Publ Health 2020; 41: 1–19.
Google Scholar | Crossref | Medline30. Xiong, C, Yu, K, Gao, F, et al. Power and sample size for clinical trials when efficacy is required in multiple endpoints: application to an Alzheimer’s treatment trial. Clin Trials 2005; 2(5): 387–393.
Google Scholar | SAGE Journals | ISI31. Sozu, T, Sugimoto, T, Hamasaki, T. Sample size determination in superiority clinical trials with multiple co-primary correlated endpoints. J Biopharm Stat 2011; 21(4): 650–668.
Google Scholar | Crossref | Medline32. Lafaye de Micheaux, P, Liquet, B, Marque, S, et al. Power and sample size determination in clinical trials with multiple primary continuous correlated endpoints. J Biopharm Stat 2014; 24(2): 378–397.
Google Scholar | Crossref | Medline33. Moulton, LH . Covariate-based constrained randomization of group-randomized trials. Clin Trials 2004; 1(3): 297–305.
Google Scholar | SAGE Journals34. Li, F, Lokhnygina, Y, Murray, DM, et al. An evaluation of constrained randomization for the design and analysis of group-randomized trials. Stat Med 2016; 35(10): 1565–1579.
Google Scholar | Crossref | Medline35. Hossain, A, Diaz-Ordaz, K, Bartlett, JW. Missing continuous outcomes under covariate dependent missingness in cluster randomised trials. Stat Meth Med Res 2016; 26: 1543–1562.
Google Scholar | SAGE Journals36. Hossain, A, Diaz-Ordaz, K, Bartlett, JW. Missing binary outcomes under covariate-dependent missingness in cluster randomised trials. Stat Med 2017; 36: 3092–3109.
Google Scholar | Crossref | Medline37. Turner, EL, Yao, L, Li, F, et al. Properties and pitfalls of weighting as an alternative to multilevel multiple imputation in cluster randomized trials with missing binary outcomes under covariate-dependent missingness. Stat Meth Med Res 2020; 29(5): 1338–1353.
Google Scholar | SAGE Journals | ISI38. Hemming, K, Kasza, J, Hooper, R, et al. A tutorial on sample size calculation for multiple-period cluster randomized parallel, cross-over and stepped-wedge trials using the Shiny CRT Calculator. Int J Epidemiol 2020; 49(3): 979–995.
Google Scholar | Crossref | Medline39. Korevaar, E, Kasza, J, Taljaard, M, et al. Intra-cluster correlations from the CLustered OUtcome Dataset bank to inform the design of longitudinal cluster trials. Clin Trials 2021; 18: 529–540.
Google Scholar | SAGE Journals | ISI