Person Mobility in the Design and Analysis of Cluster-Randomized Cohort Prevention Trials

Prevention Science

Volumn 13, Issue 3, 2012, Pages 300-313

  Vuchinich, Sam ^a   Flay, Brian R ^a   Aber, Lawrence ^b   Bickman, Leonard ^c  

^a OREGON STATE UNIVERSITY   (United States)

^b NEW YORK UNIVERSITY   (United States)

^c VANDERBILT UNIVERSITY   (United States)

Author keywords

Cluster randomized trials; Cohort prevention trials; Generalizability; Mobility; Validity

Indexed keywords

ARTICLE; CLINICAL TRIAL (TOPIC); CLUSTER ANALYSIS; COHORT ANALYSIS; CONSUMER; HUMAN; METHODOLOGY; PRIMARY PREVENTION; PSYCHOLOGICAL ASPECT; REPRODUCIBILITY; STUDENT; VALIDATION STUDY;

CLINICAL TRIALS AS TOPIC; CLUSTER ANALYSIS; COHORT STUDIES; CONSUMER PARTICIPATION; HUMANS; PRIMARY PREVENTION; REPRODUCIBILITY OF RESULTS; RESEARCH DESIGN; STUDENTS;

EID: 84861231710     PISSN: 13894986     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11121-011-0265-y     Document Type: Article

References (85)

1. Aber, J. L., Brown, J. L., & Jones, S. M. (2003). Developmental trajectories toward violence in middle childhood: Course, demographic differences, and response to school-based intervention. Developmental Psychology, 39, 324-348.
2. Baker, S. G., Fitzmaurice, G. M., Freedman, L. S., & Kramer, B. S. (2006). Simple adjustments for randomized trials with nonrandomly missing or censored outcomes arising from informative covariates. Biostatistics, 7, 29-40.
3. Barnard, J., Frangakis, C. E., Hill, & Rubin, D. T. (2003). Principal stratification approach to broken randomized experiments: A case study of school choice vouchers in New York City. Journal of the American Statistical Association, 98, 299-311.
4. Battistich, V., Schaps, E., Watson, M., Solomon, D., & Lewis, C. (2000). Effects of the Child Development Project on students' drug use and other problem behaviors. The Journal of Primary Prevention, 21, 75-99.
5. Bauman, K. E., Suchindran, C. M., & Murray, D. M. (1999). The paucity of effects in community trials: Is secular trend the culprit? Preventive Medicine, 28, 426-429.
6. Beets, M. W., Flay, B. R., Vuchinich, S., Acock, A. C., Li, K., & Allred, C. (2008). School climate and teachers' beliefs and attitudes associated with implementation of the Positive Action program: A diffusion of innovations model. Prevention Science, 9, 264-275.
7. Bloom, H. S. (2005) Randomizing groups to evaluate place-based programs. In H. S. Bloom (Ed.), Learning more from social experiments: Evolving analytic approaches (pp. 115-172). New York: Russell Sage Foundation.
8. Bloom, H. S., Bos, J. M., & Lee, S.-W. (1999). Using cluster random assignment to measure program impacts: Statistical implications for the evaluation of education programs. Evaluation Review, 23, 445-469.
9. Bonell, C., Sorhaindo, A., Strange, V., Wiggins, M., Allen, E., Fletcher, A., et al. (2010). A pilot whole-school intervention to improve school ethos and reduce substance use. Health Education, 110, 252-272.
10. Borman, G. D., & Dowling, N. M. (2006). Longitudinal achievement effects of multiyear summer school: Evidence from the Teach Baltimore randomized field trial. Educational Evaluation and Policy Analysis, 28, 25-48.
11. Boruch, R. G., & Foley, E. (2000). The honestly experimental society: Sites and other entities as the units of allocation and analysis in randomized trials. In L. Bickman (Ed.), Validity and social experimentation: Donald Campbell's legacy, Volume 1 (pp. 198-238). Thousand Oaks, CA: Sage Publications.
12. Brown, C. H., Wang, W., Kellam, S. G., Muthen, B. O., Petras, H., Toyinbo, P., et al. (2008). Models for testing and evaluating impact in randomized field trials: Intent-to-treat analyses for integrating the perspectives of person, place, and time. Drug and Alcohol Dependence, 95S, S74-S104.
13. Brown, E. C., Graham, J. W., Hawkins, J. D., Arthur, M. W., Baldwin, M. M., Oesterle, S., et al. (2009). Design and analysis of the Community Youth Development Study longitudinal cohort sample. Evaluation Review, 33, 311-334.
14. Campbell, D. T. (1957). Factors relevant to the validity of experiments in social settings. Psychological Bulletin, 54, 297-312.
15. Carpenter, J. R., Goldstein, H. & Kenward, M. G. (2011). REALCOM-IMPUTE software for multilevel multiple imputation with mixed response types. Journal of Statistical Software, 45, 1-14.
16. Christakis, N. A., & Fowler, J. H. (2007). The spread of obesity in a large social network over 32 years. The New England Journal of Medicine, 357, 370-379.
17. Clark, T. W., Pareek, M., Hoschler, K., Dillon, H., Nicholson, K. G., Groth, N., & Stephenson, I. (2009). Trial of 2009 Influenza A (H1N1) monovalent MF59-adjvanted vaccine. The New England Journal of Medicine, 361, 2424-2435.
18. Collins, L. M., Schafer, J. L., & Kam, C.-M. (2001). A comparison of inclusive and restrictive strategies in missing data procedures. Psychological Methods, 6, 330-351.
19. Cook, T. D. (2005). Emergent principles for the design, implementation, and analysis of cluster-based experiments in social science. The Annals of the American Academy of Political and Social Science, 599, 176-198.
20. Cornfield, J. (1978). Randomization by group: A formal analysis. American Journal of Epidemiology, 108, 100-102.
21. Crouchley, R., & Davies, R. B. (1999). A comparison of population average and random-effect models for the analysis of longitudinal count data with base-line information. Journal of the Royal Statistical Society, Series A, 162, 331-347.
22. Danese, A., Moffitt, T. E., Pariente, C., Poulton, R., Caspi, A. (2008). Elevated inflammation levels in depressed adults with a history of childhood maltreatment. Archives of General Psychiatry, 65, 409-415.
23. DeGarmo, D. S., Eddy, J. M., Reid, J. B., & Fetrow, R. A. (2009). Evaluating mediators of the impact of the Linking the Interests of Families and Teachers (LIFT) multimodal preventive intervention on substance use initiation and growth across adolescence. Prevention Science, 10, 208-220.
24. Dodge, K. A., Greenberg, M. T., Malone, S. M., & Conduct Problems Prevention Group. (2008). Testing an idealized dynamic model of the development of serious violence in adolescence. Child Development, 79, 1907-1927.
25. Donner, A., & Klar, N. (2000). Design and analysis of cluster randomization trials in health research. London, England: Arnold.
26. Donner, A., & Klar, N. (2004). Pitfalls and controversies in cluster randomization trials. American Journal of Public Health, 94, 416-422.
27. Eckenrode, J., Rowe, E., Laird, M., & Brathwaite, J. (1995). Mobility as a mediator of the effects of child maltreatment on academic performance. Child Development, 66, 1130-1142.
28. Fairchild, A. J., & MacKinnon, D. P. (2009). A general model for testing mediation and moderation effects. Prevention Science, 10, 87-99.
29. Fitzmaurice, G. M., & Laird, N. M. (2000). Generalized linear mixture models for handling nonignorable dropouts in longitudinal studies. Biostatistics, 1, 141-156.
30. Flay, B. R. (1986). Efficacy and effectiveness trials (and other phases of research) in the development of health promotion programs. Preventive Medicine, 15, 451-474.
31. Flay, B. R., & Collins, L. M. (2005). Historical review of school-based randomized trials for evaluating problem behavior prevention programs. The Annals of the American Academy of Political and Social Science, 599, 115-146.
32. Flay, B. R., & Cook, T. D. (1981). Evaluation of mass media prevention campaigns. In R. R. Rice & W. Paisley (Eds.), Public communication campaigns (pp. 239-313). Beverly Hills, CA: Sage.
33. Flay, B. R., Graumlich, S., Segawa, S., Burns, J. L., Holliday, M. Y., & Investigators, A. A. (2004). Effects of two prevention programs on high-risk behaviors among African-American youth: A randomized trial. Archives of Pediatric & Adolescent Medicine, 158, 377-384.
34. Foshee, V. A., Bauman, K. E., Ennett, S. T., Suchindran, C., Benefield, T., & Linder, G. F. (2005). Assessing the effects of the dating violence prevention program "Safe Dates" using random coefficient regression modeling. Prevention Science, 6, 245-258.
35. Frangakis, C. E. (2009). The calibration of treatment effects from clinical trials to target populations. Clinical Trials, 6, 136-140.
36. Frangakis, C. E., & Rubin, D. B. (2002). Principal stratification in causal inference. Biometrics, 58, 1-29.
37. Frangakis, C. E., Rubin, D. B., & Zhou, X. H. (2002). Clustered encouragement design with individual noncompliance: Bayesian inference and application to advance directive forms. Biostatistics, 3, 147-164.
38. Giraudeau, B., & Rivaud, P. (2009). Preventing bias in cluster randomized trials. PLoS Medicine, 6, e1000065.
39. Goldstein, H., Burgess, S., & McConnell, B. (2007). Modelling the effect of pupil mobility on school differences in educational achievement. Journal of the Royal Statistical Society: Series A, 170, 941-954.
40. Graham, J. W. (2009). Missing data analysis: Making it work in the real world. Annual Review of Psychology, 60, 549-576.
41. Gruman, D. H., Harachi, D. W., Abbott, R. D., Catalano, R. F., & Fleming, C. B. (2008). Longitudinal effects of student mobility on three dimensions of elementary school engagement. Child Development, 79, 1833-1852.
42. Hewitt, C. E., Kumaravel, B., Dumville, J. C., & Torgerson, D. J. (2010). Assessing the impact of attrition in randomized controlled trials. Journal of Clinical Epidemiology, 63, 1264-1270.
43. Hirano, K., Imbens, G., Ridder, G., & Rubin, D. (2001). Combining panel data sets with attrition and refreshment samples. Econometrica, 69, 1645-1659.
44. Hox, J. (2010). Multilevel analysis: Techniques and applications. Mahwah, NJ: Lawrence Erlbaum.
45. Jo, B. (2002). Estimation of intervention effects with noncompliance: Alternative model specifications. Journal of Educational and Behavioral Statistics, 27, 385-409.
46. Jo, B., & Muthen, B. (2003). Longitudinal studies with intervention and noncompliance: Estimation of causal effects in growth mixture modeling. In S. P. Reise & N. Duan (Eds.), Multilevel modeling: Methodological advances, issues and applications (pp. 112-139). Mahwah NJ: Lawrence Erlbaum.
47. Jo, B., & Stuart, E. A. (2009). On the use of propensity scores in principal causal effect estimation. Statistics in Medicine, 28, 2857-2875.
48. Jo, B., Asparouhov, T., Muthen, B., Ialongo, N., & Brown, C. H. (2008). Intention-to-treat analysis in cluster randomized trials with noncompliance. Statistics in Medicine, 27, 5565-5577.
49. Jo, B., Ginexi, E. M., & Ialongo, N. S. (2010). Handling missing data in randomized experiments with noncompliance. Prevention Science, 11, 384-396.
50. Jones, S., Brown, J., & Aber, J. L. (2011). Two-year impacts of a universal school-based social-emotional and literacy intervention: An experiment in translational developmental research. Child Development, 82, 533-554.
51. Kellam, S. G., Brown, C. H., Poduska, J. M., Ialongo, N., Wang, W., Toyinbo, P., et al. (2008). Effects of a universal classroom behavior management program in first and second grades on young adult behavioral, psychiatric, and social outcomes. Drug and Alcohol Dependence, 95, S5-S28.
52. Leon A. C., Demirtas, H., & Hedeker, D. (2007). Bias reduction with an adjustment for participants' intent to drop out of a randomized controlled clinical trial. Clinical Trials, 4, 540-547.
53. Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data (2nd ed). New York: Wiley.
54. Little, R. J., Long, Q., & Lin, X. (2009). A comparison of methods for estimating the causal effect of a treatment in randomized clinical trials subject to noncompliance. Biometrics, 65, 640-649.
55. Liu, L. C., Flay, B., & Investigators, A. A. (2009). Evaluating mediation in longitudinal multivariate data: Mediation effects for the Aban Aya Youth Project drug prevention program. Prevention Science, 10, 197-207.
56. Localio, A. R., Berlin, J. A., & Ten Have, T. R. (2006). Longitudinal and repeated cross-sectional cluster-randomization designs using mixed effects regression for binary outcomes: Bias and coverage of frequentist and Bayesian methods. Statistics in Medicine, 25, 2720-2736.
57. Multisite Violence Prevention Project. (2008). Impact of a universal school-based violence prevention program on social-cognitive outcomes. Prevention Science, 9, 231-244.
58. Murray, D. M. (1998). Design and analysis of group-randomized trials. New York: Oxford University Press.
59. Murray, D. M., van Horn, M. L., Hawkins, J. D., & Arthur, M. W. (2006). Analysis strategies for a community trial to reduce adolescent ATOD use: A comparison of random coefficient and ANOVA/ANCOVA models. Contemporary Clinical Trials, 27, 188-206.
60. Murray, D. M., Pals, S. L., Blitstein, J. L., Alfano, C. M., & Lehman, J. (2008). Design and analysis of group-randomized trials in cancer: A review of current practices. Journal of the National Cancer Institute, 100, 483-491.
61. Muthen, B., Brown, C. H., Masyn, K., Jo, B., Khoo, S.-T., Yang, C.-P., et al. (2002). General growth mixture modeling for randomized preventive interventions. Biostatistics, 3, 459-475.
62. National Research Council and Institute of Medicine. (2010). Student mobility: Exploring the impact of frequent moves on achievement: Summary of a workshop. (Beatty, A., Rapporteur). Committee on the impact of mobility and change on the lives of young children, schools, and neighborhoods. Board on Children, Youth, and Families, Division of Behavioral and Social Sciences and Education. Washington, DC: The National Academies Press.
63. Osann K., Wenzel L., Dogan A., Hsieh S., Chase D. M., Sappington S., Monk B. J., & Nelson E. L. (2011). Recruitment and retention results for a population-based cervical cancer biobehavioral clinical trial. Gynecologic Oncology, 121, 558-563.
64. Pals, S. L., Murray, D. M., Alfano, C. M., Shadish, W. R., Hannan, P. J., & Baker, W. L. (2008). Individually randomized group treatment trials: A critical appraisal of frequently used design and analytic procedures. American Journal of Public Health, 98, 1418-1424.
65. Pribesh, S., & Downey, D. B. (1999). Why are residential and school moves associated with poor school performance? Demography, 36, 521-534.
66. Prinz, R. J., Dumas, J. E., Smith, E. P., Laughlin, J., White, D., & Barrón, R. (2001). Recruitment and retention of participants in prevention trials. American Journal of Preventive Medicine, 20 (Supplement), 31-37.
67. Rabe-Hesketh, S., & Skrondal, A. (2008). Multilevel and longitudinal modeling using Stata (2nd ed.). College Station, TX: Stata Press.
68. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Newbury Park, CA: Sage.
69. Ribisl, K. M., Walton, M. A., Mowbray, C. T., Luke, D. A., Davidson, W. S., & Bootsmiller, B. J. (1996). Minimizing participant attrition in panel studies through the use of effective retention and tracking strategies: Review and recommendations. Evaluation and Program Planning, 19, 1-25.
70. Roy, J., Hogan, J. W., & Marcus, B. H. (2008). Principal stratification with predictors of compliance for randomized trials with two active treatments. Biostatistics, 9, 277-289.
71. Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. New York: Wiley.
72. Rubin, D. B. (2006). Causal inference through potential outcomes and principal stratification. Statistical Science, 21, 299-309.
73. Sampson, R. J., Sharkey, P., & Raudenbush, S. W. (2008). Durable effects of concentrated disadvantage on verbal ability among African-American children. PNAS, 105, 845-852.
74. Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7, 147-177.
75. Shadish, W., Cook, T., & Campbell, D. (2002). Experimental and quasi-experimental designs for generalized causal inference. New York: Wadsworth.
76. Sherwin, R. (1978). Controlled trials of the diet-heart hypothesis: Some comments on the experimental unit. American Journal of Epidemiology, 108, 92-99.
77. Stevens, J., Murray, D. M., Catellier, D. J., Hannan, P. J., Lytle, L. A., Elder, J. P., et al. (2005). Design of the Trial of Activity in Adolescent Girls (TAAG). Contemporary Clinical Trials, 26, 223-233.
78. Stuart, E. A., Perry, D. F., Le, H.-N., & Ialongo, N. (2008). Estimating intervention effects of prevention programs: Accounting for noncompliance. Prevention Science, 9, 288-298.
79. Taljaard, M., Donner, A., & Klar, N. (2008). Imputation strategies for missing continuous outcomes in cluster randomized trials. Biometrical Journal, 50, 329-345.
80. Taylor, L., & Zhou, X. H. (2009). Multiple imputation methods for treatment noncompliance and nonresponse in randomized clinical trials. Biometrics, 65, 88-95.
81. U. S. Census Bureau. (2009). Current population survey, annual social and economic supplement. Table 1. General Mobility: 2008 to 2009. Washington, DC: US Census Bureau.
82. U. S. Department of Education, National Center for Education Statistics. (2006). ECLS-K longitudinal kindergarten-fifth grade public-use data file and electronic codebook (CD-ROM). (NCES 2006-035). Washington, DC: Author.
83. Varnell, S., Murray, D. M., Janega, J. B., & Blitstein, J. L. (2004). Design and analysis of group-randomized trials: A review of recent practices. American Journal of Public Health, 94, 393-399.
84. West, S. G., Duan, N., Pequegnat, W., Gaist, P., Des Jaríais, D. C., Holtgrave, D., et al. (2008). Alternatives to randomized controlled trials. American Journal of Public Health, 98, 1359-1366.
85. Yucel, R. M. (2008). Multiple imputation inference for multivariate multilevel continuous data with ignorable non-response. Philosophical Transactions of the Royal Society A, 366, 2389-2403.