Partnerships for the Design, Conduct, and Analysis of Effectiveness, and Implementation Research: Experiences of the Prevention Science and Methodology Group
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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.
KeywordsPrevention science Implementation science Social networks Community-based participatory research
We thank our colleagues in the Prevention Science and Methodology Group for many comments and improvements in this presentation. Also, we thank the many community, organization, and policy leaders with whom we have partnered in our previous and continuing research. We acknowledge funding support for this work through joint support from the National Institute of Mental Health (NIMH) and the National Institute on Drug Abuse (R01-MH040859), from NIMH (R01-MH076158), and from NIDA (P30-DA027828).
- 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
- 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.
- Axelrod, R. M. (1997). The complexity of cooperation: Agent-based models of competition and collaboration. Princeton, NJ, US: Princeton University Press.Google Scholar
- 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
- 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
- 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.
- 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
- Chamberlain, P., Roberts, R., et al. (2011). Three collaborative models for scaling up evidence-based practices. doi: 10.1007/s10488-011-0349-9.
- 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
- 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
- Epstein, J. M. (2007). Generative social science: Studies in agent-based computational modeling. Princeton, NJ: Princeton University Press.Google Scholar
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- Kuhn, T. S. (1996). The structure of scientific revolutions. Chicago: University of Chicago.Google Scholar
- Landsverk, J., Brown, C. H., et al. (Accepted for publication). Design and analysis in dissemination and implementation research. London: Oxford University Press.Google Scholar
- 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
- 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
- Murray, D. M. (1998). Design and analysis of group-randomized trials. Monographs in epidemiology and biostatistics. Oxford: Oxford University Press.Google Scholar
- Muthén, L. K., & Muthén, B. O. (2007). Mplus: Statistical analysis with latent variables: User’s guide. Los Angeles, CA: Muthén & Muthén.Google Scholar
- 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
- 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
- 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
- Patterson, G. R., Reid, J. B., et al. (1992). Antisocial boys. Eugene, OR: Castalia Pub. Co.Google Scholar
- 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
- 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
- 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
- Valente, T. W. (1995). Network models of the diffusion of innovations. Cresskill, N.J.: Hampton Press.Google Scholar
- Valente, T. W. (2005). Models and methods for innovation diffusion. Cambridge: Cambridge University Press.Google Scholar
- Valente, T. W. (2010). Social networks and health: Models, methods, and applications. New York, Oxford: Oxford University Press.Google Scholar
- 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
- 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