Partnerships for the Design, Conduct, and Analysis of Effectiveness, and Implementation Research: Experiences of the Prevention Science and Methodology Group

  • C. Hendricks Brown
  • Sheppard G. Kellam
  • Sheila Kaupert
  • Bengt O. Muthén
  • Wei Wang
  • Linda K. Muthén
  • Patricia Chamberlain
  • Craig L. PoVey
  • Rick Cady
  • Thomas W. Valente
  • Mitsunori Ogihara
  • Guillermo J. Prado
  • Hilda M. Pantin
  • Carlos G. Gallo
  • José Szapocznik
  • Sara J. Czaja
  • John W. McManus
Original Paper

Abstract

What progress prevention research has made comes through strategic partnerships with communities and institutions that host this research, as well as professional and practice networks that facilitate the diffusion of knowledge about prevention. We discuss partnership issues related to the design, analysis, and implementation of prevention research and especially how rigorous designs, including random assignment, get resolved through a partnership between community stakeholders, institutions, and researchers. These partnerships shape not only study design, but they determine the data that can be collected and how results and new methods are disseminated. We also examine a second type of partnership to improve the implementation of effective prevention programs into practice. We draw on social networks to studying partnership formation and function. The experience of the Prevention Science and Methodology Group, which itself is a networked partnership between scientists and methodologists, is highlighted.

Keywords

Prevention science Implementation science Social networks Community-based participatory research 

References

  1. Aarons, G. A., Hurlburt, M., et al. (2011). Advancing a conceptual model of evidence-based practice implementation in public service sectors. Administration and Policy in Mental Health and Mental Health Services Research, 38(1), 4–23.Google Scholar
  2. Agar, M. (2005). Agents in living color: Towards emic agent-based models. Jasss-the Journal of Artificial Societies and Social Simulation, 8(1). http://jasss.soc.surrey.ac.uk/8/1/4.html.
  3. Arthur, M. A., Hawkins, J. D., et al. (2010). Implementation of the communities that care prevention system by coalitions in the community youth coalition study. Journal of Community Psychology, 38(2), 245–258.CrossRefGoogle Scholar
  4. Axelrod, R. M. (1997). The complexity of cooperation: Agent-based models of competition and collaboration. Princeton, NJ, US: Princeton University Press.Google Scholar
  5. Brown, C. H. (1993). Statistical methods for preventive trials in mental health. Statistics in Medicine, 12(3–4), 289–300.PubMedCrossRefGoogle Scholar
  6. Brown, C. H. (2003). Design principles and their application in preventive field trials. In W. J. Bukoski & Z. Sloboda (Eds.), Handbook of drug abuse prevention: Theory, science, and practice (pp. 523–540). New York: Kluwer Academic/Plenum Press.Google Scholar
  7. Brown, C. H., Costigan, T., et al. (2008). Data analytic frameworks: Analysis of variance, latent growth, and hierarchical models. In A. Nezu & C. Nezu (Eds.), Evidence-based outcome research: A practical guide to conducting randomized clinical trials for psychosocial interventions (pp. 285–313). New York: Oxford University Press.Google Scholar
  8. Brown, C. H., & Liao, J. (1999). Principles for designing randomized preventive trials in mental health: An emerging developmental epidemiology paradigm. American Journal of Community Psychology, 27(5), 673–710.PubMedCrossRefGoogle Scholar
  9. Brown, C. H., Ten Have, T. R., et al. (2009). Adaptive designs for randomized trials in public health. Annual Review of Public Health, 30, 1–25.PubMedCrossRefGoogle Scholar
  10. Brown, C. H., Wang, W., et al. (2008b). Methods for testing theory and evaluating impact in randomized field trials: Intent-to-treat analyses for integrating the perspectives of person, place, and time. Drug and Alcohol Dependence, 95(Suppl 1): S74–S104; Supplementary data associated with this article can be found, in the online version, at doi:110.1016/j.drugalcdep.2008.1001.1005.
  11. Brown, C. H., Wyman, P. A., et al. (2006). Dynamic wait-listed designs for randomized trials: New designs for prevention of youth suicide. Clinical Trials, 3(3), 259–271.PubMedCrossRefGoogle Scholar
  12. Brown, C. H., Wyman, P. A., et al. (2007). The role of randomized trials in testing interventions for the prevention of youth suicide. International Review of Psychiatry, 19(6), 617–631.PubMedCrossRefGoogle Scholar
  13. Bryk, A. S., & Raudenbush, S. W. (1987). Application of hierarchical linear models to assessing change. Psychological Bulletin, 101(1), 147–158.CrossRefGoogle Scholar
  14. Buchanan, D. R., Miller, F. G., et al. (2007). Ethical issues in community-based participatory research: balancing rigorous research with community participation in community intervention studies. Progress in Community Health Partnerships: Research, Education, and Action, 1(2), 153–160.Google Scholar
  15. Chamberlain, P., Roberts, R., et al. (2011). Three collaborative models for scaling up evidence-based practices. doi:10.1007/s10488-011-0349-9.
  16. Chamberlain, P., Brown, C. H., et al. (2008). Engaging and recruiting counties in an experiment on implementing evidence-based practice in California. Administration and Policy In Mental Health, 35(4), 250–260.PubMedCrossRefGoogle Scholar
  17. Chamberlain, P., Saldana, L., et al. (2010a). Implementation of MTFC in California: A randomized trial of an evidence-based practice. In M. Roberts-DeGennaro & S. Fogel (Eds.), Using evidence to inform practice for community and organizational change (pp 218–234). Chicago: Lyceum Books, Inc.Google Scholar
  18. Chamberlain, P., Saldana, L., et al. (2010b). Implementation of multidimensional treatment foster care in California: A randomized trial of an evidence-based practice. In M. Roberts-DeGennaro & S. Fogel (Eds.), Empirically supported interventions for community and organizational change. Chicago: Lyceum Books, Inc.Google Scholar
  19. Chambers, D. A. (2008). Advancing the science of implementation: A workshop summary. Administration and Policy in Mental Health and Mental Health Services Research, 35(1–2), 3–10.PubMedCrossRefGoogle Scholar
  20. Coatsworth, J., Pantin, H., et al. (2002). Familias Unidas: A family-centered ecodevelopmental intervention to reduce risk for problem behavior among Hispanic adolescents. Clinical Child and Family Psychology Review, 5(2), 113–132.PubMedCrossRefGoogle Scholar
  21. Dagne, G. A., Howe, G. W., et al. (2002). Hierarchical modeling of sequential behavioral data: An empirical Bayesian approach. Psychological Methods, 7(2), 262–280.PubMedCrossRefGoogle Scholar
  22. Dishion, T. J., McCord, J., et al. (1999). When interventions harm. Peer groups and problem behavior. American Psychologist, 54(9), 755–764.PubMedCrossRefGoogle Scholar
  23. Dishion, T. J., Spracklen, K. M., et al. (1996). Deviancy training in male adolescents friendships. Behavior Therapy, 27(3), 373–390.CrossRefGoogle Scholar
  24. Dolan, L. J., Kellam, S. G., et al. (1993). The short-term impact of two classroom-based preventive interventions on aggressive and shy behaviors and poor achievement. Journal of Applied Developmental Psychology, 14(3), 317–345.CrossRefGoogle Scholar
  25. Emshoff, J. (2008). Researchers, practitioners, and funders: Using the framework to get Us on the same page. American Journal of Community Psychology, 41(3), 393–403.PubMedCrossRefGoogle Scholar
  26. Epstein, J. M. (2007). Generative social science: Studies in agent-based computational modeling. Princeton, NJ: Princeton University Press.Google Scholar
  27. Flay, B. R. (1986). Efficacy and effectiveness trials (and other phases of research) in the development of health promotion programs. Preventive Medicine, 15(5), 451–474.PubMedCrossRefGoogle Scholar
  28. Gibbons, R. D., & Hedeker, D. (1997). Random effects probit and logistic regression models for three-level data. Biometrics, 53(4), 1527–1537.PubMedCrossRefGoogle Scholar
  29. Gibbons, R. D., Hedeker, D., et al. (1988). Random regression models: A comprehensive approach to the analysis of longitudinal psychiatric data. Psychopharmacology Bulletin, 24(3), 438–443.PubMedGoogle Scholar
  30. Guerra, N., & Knox, L. (2008). How culture impacts the dissemination and implementation of innovation: A case study of the Families and Schools Together Program (FAST) for preventing violence with immigrant Latino youth. American Journal of Community Psychology, 41(3), 304–313.PubMedCrossRefGoogle Scholar
  31. Hallfors, D., & Godette, D. (2002). Will the ‘Principles of Effectiveness’ improve prevention practice? Early findings from a diffusion study. Health Education Research, 17(4), 461–470.PubMedCrossRefGoogle Scholar
  32. Hallfors, D., Pankratz, M., et al. (2007). Does federal policy support the use of scientific evidence in school-based prevention programs? Prevention Science, 8(1), 75–81.PubMedCrossRefGoogle Scholar
  33. Hawkins, J. D. (1999). Preventing crime and violence through communities that care. European Journal on Criminal Policy and Research, 7(4), 443–458.CrossRefGoogle Scholar
  34. Hawkins, J. D., Arthur, M. W., et al. (1998). Community interventions to reduce risks and enhance protection against antisocial behavior. In D. W. Stoff, J. Breiling, & J. D. Masers (Eds.), Handbook of antisocial behaviors (pp. 365–374). New York: Wiley.Google Scholar
  35. Hawkins, J., Catalano, R., et al. (2008). Testing communities that care: The rationale, design and behavioral baseline equivalence of the community youth development study. Prevention Science, 9(3), 178–190.PubMedCrossRefGoogle Scholar
  36. Heath, B., Hill, R., et al. (2009). A survey of agent-based modeling practices (January 1998 to July 2008). The Journal of Artificial Societies and Social Simulation, 12(4), A143–A177.Google Scholar
  37. Hedeker, D., Siddiqui, O., et al. (2000). Random-effects regression analysis of correlated grouped-time survival data. Statistical Methods in Medical Research, 9(2), 161–179.PubMedCrossRefGoogle Scholar
  38. Horton, N. J., & Lipsitz, S. R. (1999). Review of software to fit Generalized Estimating Equation (GEE) regression models. American Statistician, 53, 160–169.Google Scholar
  39. Howe, G. W., Reiss, D., et al. (2002). Can prevention trials test theories of etiology? Development and Psychopathology, 14, 673–693.PubMedCrossRefGoogle Scholar
  40. Ialongo, N. S., Werthamer, L., et al. (1999). Proximal impact of two first-grade preventive interventions on the early risk behaviors for later substance abuse, depression, and antisocial behavior. American Journal of Community Psychology, 27(5), 599–641.PubMedCrossRefGoogle Scholar
  41. Inoue, M., Ogihara, M., et al. (2010). Utility of gestural cues in indexing semantic miscommunication. In: 5th international conference on Future Information Technology (Future Tech 2010).Google Scholar
  42. Ivanov, Y. A., & Bobick, A. F. (2000). Recognition of visual activities and interactions by stochastic parsing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 852–872.CrossRefGoogle Scholar
  43. Jasuja, G. K., Chou, C. P., et al. (2005). Using structural characteristics of community coalitions to predict progress in adopting evidence-based prevention programs. Eval Program Plan, 28, 173–184.CrossRefGoogle Scholar
  44. Kellam, S. G. (2000). Community and institutional partnerships for school violence prevention. Preventing school violence: Plenary papers of the 1999 conference on criminal justice research and evaluation—enhancing policy and practice through research (vol. 2, pp 1–21). Washington, DC: National Institute of Justice.Google Scholar
  45. Kellam, S. G., Brown, C. H., 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(Suppl 1): S5–S28; Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.drugalcdep.2008.1001.1004.
  46. Kellam, S. G., Koretz, D., et al. (1999). Core elements of developmental epidemiologically based prevention research. American Journal of Community Psychology, 27(4), 463–482.PubMedCrossRefGoogle Scholar
  47. Kellam, S. G., & Langevin, D. J. (2003). A framework for understanding “evidence” in prevention research and programs. Prevention Science, 4(3), 137–153.PubMedCrossRefGoogle Scholar
  48. Kellam, S. G., & Rebok, G. W. (1992). Building developmental and etiological theory through epidemiologically based preventive intervention trials. In J. McCord & R. E. Tremblay (Eds.), Preventing antisocial behavior: Interventions from birth through adolescence (pp. 162–195). New York, NY: Guilford Press.Google Scholar
  49. Kellam, S. G., Rebok, G. W., et al. (1994). The social field of the classroom: Context for the developmental epidemiological study of aggressive behavior. In R. K. Silbereisen & E. Todt (Eds.), Adolescence in context: The interplay of family, school, peers and work in adjustment (pp. 390–408). New York: Springer.Google Scholar
  50. Kelly, J. A., Somlai, A. M., et al. (2000). Bridging the gap between the science and service of HIV prevention: Transferring effective research-based HIV prevention interventions to community AIDS service providers. American Journal of Public Health, 90(7), 1082–1088.PubMedCrossRefGoogle Scholar
  51. Kuhn, T. S. (1996). The structure of scientific revolutions. Chicago: University of Chicago.Google Scholar
  52. Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974.PubMedCrossRefGoogle Scholar
  53. Landsverk, J., Brown, C. H., et al. (Accepted for publication). Design and analysis in dissemination and implementation research. London: Oxford University Press.Google Scholar
  54. Landsverk, J., Brown, C. H., et al. (2011). Design elements in implementation research: A structured review of child welfare and child mental health studies. Administration and Policy In Mental Health, 38(1), 54–63.PubMedCrossRefGoogle Scholar
  55. Li, T., & Ogihara, M. (2005). Semisupervised learning from different information sources. Knowledge and Information Systems, 7(3), 289–309.CrossRefGoogle Scholar
  56. Li, T., Zhu, S., et al. (2008). Text categorization via generalized discriminant analysis. Information Processing & Management, 44(5), 1684–1697.CrossRefGoogle Scholar
  57. Liang, K.-Y., & Zeger, S. L. (1986). Longitudinal data analysis using generalized estimating equations. Biometrika, 73(1), 13–22.CrossRefGoogle Scholar
  58. Mabry, P. L., Olster, D. H., et al. (2008). Interdisciplinarity and systems science to improve population health: A view from the NIH Office of Behavioral and Social Sciences Research. American Journal of Preventive Medicine, 35(2 Suppl), S211–S224.PubMedCrossRefGoogle Scholar
  59. Masyn, K. E. (2003). Discrete-time survival analysis for single and recurrent events using latent variables (p. 290). Los Angeles: University of California.Google Scholar
  60. McGuire, W. (1964). Inducing resistance to persuasion: Some contemporary approaches. In L. Berkowitz (Ed.), Advances in experimental social psychology (Vol. 1, pp. 191–229). New York: Academic Press.CrossRefGoogle Scholar
  61. Miller, J. H., & Page, S. E. (2007). Complex adaptive systems: An introduction to computational models of social life. Princeton, NJ, US: Princeton University Press.Google Scholar
  62. MRFIT-Research-Group. (1982). Multiple risk factor intervention trial: Risk factor changes and mortality results. JAMA, 248, 1465–1477.CrossRefGoogle Scholar
  63. Murray, D. M. (1998). Design and analysis of group-randomized trials. Monographs in epidemiology and biostatistics. Oxford: Oxford University Press.Google Scholar
  64. Muthén, B. (1997). Latent variable modeling of longitudinal and multilevel data. Sociological Methodology, 27, 453–480.CrossRefGoogle Scholar
  65. Muthén, B. O., Brown, C. H., et al. (2002). General growth mixture modeling for randomized preventive interventions. Biostatistics, 3, 459–475.PubMedCrossRefGoogle Scholar
  66. Muthén, L. K., & Muthén, B. O. (2007). Mplus: Statistical analysis with latent variables: User’s guide. Los Angeles, CA: Muthén & Muthén.Google Scholar
  67. O’Connell, M., Boat, T., & Warner, E. (2009). Preventing mental, emotional, and behavioral disorders among young people: Progress and possibilities. Washington, DC: The National Academies Press.Google Scholar
  68. Ormerod, P., Rosewell B. (2009). Validation and verification of agent-based models in the social sciences. Epistemological aspects of computer simulation in the social sciences: Second international workshop, EPOS 2006, Brescia, Italy, October 5–6, 2006: Revised selected and invited papers. F. Squazzoni. Berlin/New York: Springer. 5466, LNAI (pp. 130–140).Google Scholar
  69. Palinkas, L. A., Aarons, G. A. et al. (2011). Mixed method designs in implementation research. Administration & Policy in Mental Health and Mental Health Services, 38, 44–53.Google Scholar
  70. Pantin, H., Prado, G., et al. (2009). A Randomized controlled trial of a parent-centered preventive intervention for Hispanic behavior-problem adolescents: Effects on substance use, HIV risk outcomes, and externalizing disorders. Psychosomatic Medicine, 71, 787–995.CrossRefGoogle Scholar
  71. Patterson, G. R., Reid, J. B., et al. (1992). Antisocial boys. Eugene, OR: Castalia Pub. Co.Google Scholar
  72. Poduska, J., Kellam, S. G., et al. (2009). Study protocol for a group randomized controlled trial of a classroom-based intervention aimed at preventing early risk factors for drug abuse: Integrating effectiveness and implementation research. Implementation Science, 4, 56.PubMedCrossRefGoogle Scholar
  73. Prado, G. J., Schwartz, S. J., et al. (2009). Ecodevelopmental × intrapersonal risk: Substance use and sexual behavior in Hispanic adolescents. Health Education & Behavior, 36(1), 45–61.CrossRefGoogle Scholar
  74. Railsback, S. F., Lytinen, S. L., et al. (2006). Agent-based simulation platforms: Review and development recommendations. Simulation, 82(9), 609–623.CrossRefGoogle Scholar
  75. Siddique, J., Brown, C. H., et al. (2008). Missing data in longitudinal trials–Part B, Analytic issues. Psychiatric Annals, 38(12), 793–801.Google Scholar
  76. Szapocznik, J., Kurtines, W., et al. (1997). The evolution of structural ecosystemic theory for working with Latino families. In J. G. Garcia & M. C. Zea (Eds.), Psychological interventions and research with Latino populations (pp. 166–190). Needham Heights, MA: Allyn & Bacon.Google Scholar
  77. Szapocznik, J., & Prado, G. (2007). Negative effects on family functioning from psychosocial treatment: A recommendation for expanded safety monitoring. Journal of Family Psychology, 21(3), 468–478.PubMedCrossRefGoogle Scholar
  78. Valente, T. W. (1995). Network models of the diffusion of innovations. Cresskill, N.J.: Hampton Press.Google Scholar
  79. Valente, T. (2003). Diffusion of innovations. General Medicine, 5(2), 69.CrossRefGoogle Scholar
  80. Valente, T. W. (2005). Models and methods for innovation diffusion. Cambridge: Cambridge University Press.Google Scholar
  81. Valente, T. W. (2010). Social networks and health: Models, methods, and applications. New York, Oxford: Oxford University Press.Google Scholar
  82. Valente, T. W., Chou, C. P., et al. (2007). Community coalitions as a system: Effects of network change on adoption of evidence-based substance abuse prevention. American Journal of Public Health, 97(5), 880–886.PubMedCrossRefGoogle Scholar
  83. Valente, T. W., Coronges, K. A., et al. (2008). Collaboration and competition in a children’s health initiative coalition: A network analysis. Evaluation and program planning, 31(4), 392–402.PubMedCrossRefGoogle Scholar
  84. Valente, T., & Davis, R. (1999). Accelerating the diffusion of innovations using opinion leaders. Annals of the American Academy of Political and Social Science, 566, 55–67.Google Scholar
  85. Wang, W., Saldana, L., et al. (2010). Factors that influenced county system leaders to implement an evidence-based program: A baseline survey within a randomized controlled trial. Implementation Science, 5(1), 72.PubMedCrossRefGoogle Scholar
  86. West, S. G., Duan, N., et al. (2008). Alternatives to the randomized controlled trial. American Journal of Public Health, 98(8), 1359–1366.PubMedCrossRefGoogle Scholar
  87. Wyman, P. A., Brown, C. H., et al. (2008). Randomized trial of a gatekeeper program for suicide prevention: 1-year impact on secondary school staff. Journal of Consulting and Clinical Psychology, 76(1), 104–115.PubMedCrossRefGoogle Scholar
  88. Zeger, S. L., Liang, K. Y., et al. (1988). Models for longitudinal data: A generalized estimating equation approach. Biometrics, 44(4): 1049–1060, [erratum appears in Biometrics 1989 Mar; 45(1):347].Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • C. Hendricks Brown
    • 1
  • Sheppard G. Kellam
    • 2
  • Sheila Kaupert
    • 1
  • Bengt O. Muthén
    • 3
  • Wei Wang
    • 4
  • Linda K. Muthén
    • 5
  • Patricia Chamberlain
    • 6
  • Craig L. PoVey
    • 7
  • Rick Cady
    • 8
  • Thomas W. Valente
    • 9
  • Mitsunori Ogihara
    • 1
  • Guillermo J. Prado
    • 1
  • Hilda M. Pantin
    • 1
  • Carlos G. Gallo
    • 1
  • José Szapocznik
    • 1
  • Sara J. Czaja
    • 1
  • John W. McManus
    • 1
  1. 1.Prevention Science Methodology Group, Center for Family Studies, Department of Epidemiology and Public HealthUniversity of Miami Miller School of MedicineMiamiUSA
  2. 2.Johns Hopkins UniversityBaltimoreUSA
  3. 3.University of California Los AngelesLos AngelesUSA
  4. 4.University of South FloridaTampaUSA
  5. 5.Department of Product DevelopmentMuthén & MuthénLos AngelesUSA
  6. 6.Center for Research to PracticeEugeneUSA
  7. 7.Division of Substance Abuse and Mental HealthSalt Lake CityUSA
  8. 8.Department of Human Services OregonAddiction and Mental Health DivisionSalemUSA
  9. 9.University of Southern CaliforniaLos AngelesUSA

Personalised recommendations