Prevention Science

, Volume 5, Issue 3, pp 185–196 | Cite as

A Conceptual Framework for Adaptive Preventive Interventions

  • Linda M. Collins
  • Susan A. Murphy
  • Karen L. Bierman


Recently, adaptive interventions have emerged as a new perspective on prevention and treatment. Adaptive interventions resemble clinical practice in that different dosages of certain prevention or treatment components are assigned to different individuals, and/or within individuals across time, with dosage varying in response to the intervention needs of individuals. To determine intervention need and thus assign dosage, adaptive interventions use prespecified decision rules based on each participant's values on key characteristics, called tailoring variables. In this paper, we offer a conceptual framework for adaptive interventions, discuss principles underlying the design and evaluation of such interventions, and review some areas where additional research is needed.

adaptive interventions prevention research design 


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  1. Bather, J. A. (2000). Decision theory: An introduction to dynamic programming and sequential decisions. New York: Wiley.Google Scholar
  2. Bertsekas, D. P., & Tsitsiklis, J. N. (1996). Neuro-dynamic programming.Belmont, MA: Athena Scientific.Google Scholar
  3. Bierman, K. L., Nix, R. L., Maples, J. J., Murphy, S. A., & Conduct Problems Prevention Research Group. (2004). Evaluating the use of clinical judgment in the context of an adaptive intervention design: The Fast Track Prevention Program.Manuscript submitted for publication.Google Scholar
  4. Borhani, N.O., Applegate, W.B., Cutler, J.A., Davis, B. R., Furberg, C. D., Lakatos, E., Page, L., Perry, H. M., Smith, W. M., & Probstfield, J. L. (1991). Systolic hypertension in the elderly program (SHEP). Part 1: Rationale and design. Hypertension, 17(Suppl II), 2–15.Google Scholar
  5. Breslin, F., Sobell, M. B., Sobell, L. C., Buchan, G., & Cunningham, J. A. (1997). Toward a stepped care approach to treating problem drinkers: The predictive utility of within-treatment variables and therapist prognostic ratings. Addiction, 92 ,1479–1489.CrossRefPubMedGoogle Scholar
  6. Breslin, F., Sobell, M. B., Sobell, L. C., Cunningham, J. A., Sdao-Jarvie, K., & Borsoi, D. (1999). Problem drinkers: Evaluation of a stepped-care approach. Journal of Substance Abuse, 10 ,217–232.CrossRefGoogle Scholar
  7. Brooner, R. K., & Kidorf, M. (2002). Using behavioral reinforcement to improve methadone treatment participation. Science and Practice Prespectives, 1(1), 38–46.Google Scholar
  8. Collins, L. M., Graham, J. W., & Flaherty, B. P. (1998). An alternative framework for defining mediation. Multivariate Behavioral Research, 33 ,295–312.Google Scholar
  9. Conduct Problems Prevention Research Group. (1992).A developmental and clinical model for the prevention of conduct disorders: TheFastTrack Program. Development and Psychopathology, 4 ,509–528.Google Scholar
  10. Conduct Problems Prevention Research Group. (1999a). Initial impact of the Fast Track prevention trial for conduct problems: I. The high-risk sample. Journal of Consulting and Clinical Psychology, 67 ,631–647.Google Scholar
  11. Conduct Problems Prevention Research Group. (1999b). Initial impact of the Fast Track prevention trial for conduct problems: II. Classroom effects. Journal of Consulting and Clinical Psychology, 67 ,648–657.Google Scholar
  12. Cowell, R. G., Dawid, A. P., Lauritzen, S. L., & Spiegelhalter, D. J. (1999). Probabilistic networks and expert systems. New York: Springer.Google Scholar
  13. Dawes, R. M., Faust, D., & Meehl, P. E. (1989). Clinical versus actuarial judgment. Science, 243 ,1668–1674.PubMedGoogle Scholar
  14. Dishion, T. J., & Kavanagh, K. (2000). A multilevel approach to family-centered prevention in schools. Addictive Behaviors, 25, 899–911.PubMedGoogle Scholar
  15. 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.PubMedGoogle Scholar
  16. Kenny, D. A, Kashy, D. A., & Bolger, N. (1998). Data analysis in social psychology. In D. T. Gilbert & S. T. Fiske (Eds.), The handbook of social psychology (Vol. 2, 4th ed., pp. 233–265). Boston, MA McGraw-Hill.Google Scholar
  17. Kreuter, M. W., & Strecher, V. J. (1996). Do tailored behavior change messages enhance the effectiveness of health risk appraisal? Results from a randomized trial. Health Education Research, 11 ,97–105.PubMedGoogle Scholar
  18. Kreuter, M. W., Strecher, V. J., & Glassman, B. (1999). One size does not fit all: the case for tailoring print materials. Annals of Behavioral Medicine, 21 ,276–283.PubMedGoogle Scholar
  19. Lavori, P. W., & Dawson, R. (1998). Developing and comparing treatment strategies: An annotated portfolio of designs. Psychopharmacology Bulletin, 34 ,13–18.PubMedGoogle Scholar
  20. Lavori, P.W., Dawson, R., & Rush, A. J. (2000). Flexible treatment strategies in chronic disease: Clinical and research implications. Biological Psychiatry, 48 ,605–614.CrossRefPubMedGoogle Scholar
  21. McDonald, R. P. (1999). Test theory: A unified treatment. Mahwah, NJ: Erlbaum.Google Scholar
  22. Murphy, S. A. (2003). Optimal dynamic treatment regimes (with discussion). Journal of the Royal Statistical Society, Series B, 65 ,331–366.Google Scholar
  23. Murphy, S. A., van der Laan, M. J., Robins, J. M., & Conduct Problems Prevention Research Group. (2001). Marginal mean models for dynamic regimes. Journal of the American Statistical Association, 96 ,1410–1423.CrossRefGoogle Scholar
  24. Owens, D. K., Shachter, R. D., & Nease, R. F. (1997). Representation and analysis of medical decision problems with influence diagrams. Medical Decision Making, 17 ,241–262.PubMedGoogle Scholar
  25. Prochaska, J. O., Velicer, W. F., Fava, J. L., Rossi, J. S., & Tsoh, J.Y. (2001). Evaluating a population-based recruitment approach and a stage-based expert system intervention for smoking cessation. Addictive Behaviors, 26 ,583–602.CrossRefPubMedGoogle Scholar
  26. Robins, J. M. (1986). A new approach to causal inference in mortality studies with sustained exposure periods-application to control of the healthy worker survivor effect. Computers and Mathematics with Applications, 14 ,1393–1512.Google Scholar
  27. Robins, J. M. (1989). The analysis of randomized and nonrandomized AIDS treatment trials using a new approach to causal inference in longitudinal studies. In L. Sechrest, H. Freeman, & A. Mulley (Eds.), Health service research methodology: A focus on AIDS (pp. 113–159). NCHSR, U.S. Public Health Service, Washington, DC.Google Scholar
  28. Robins, J. M. (1993). Information recovery and bias adjustment in proportional hazards regression analysis of randomized trials using surrogate markers. Proceedings of the Biopharmaceutical Section, American Statistical Association, 24–33.Google Scholar
  29. Robins, J. M. (1997). Causal inference from complex longitudinal data. In M. Berkane (Ed.), Latent variable modeling and applications to causality: Lecture notes in statistics (pp. 69–117). New York: Springer.Google Scholar
  30. Schulte, D., Kunzel, R. l., Pepping, G., & Schulte-Bahrenberg, T. (1992). Tailor-made versus standardized therapy of phobic patients. Advances in Behavior Research and Therapy, 14 ,67–92.Google Scholar
  31. Shachter, R. D. (1986). Evaluating influence diagrams. Operations Research, 34 ,871–882.Google Scholar
  32. Sobel, M. B., & Sobell, L. C. (1999). Stepped care for alcohol problems: An efficient method for planning and delivering clinical services. In: J. A. Tucker, D. M. Donovan, & G. A. Marlatt (Eds.), Changing addictive behavior: Bridging clinical and public health strategies (pp. 331–343). New York: Guilford Press.Google Scholar
  33. Sobel, M. B., & Sobell, L. C. (2000). Stepped care as a heuristic approach to the treatment of alcohol problems. Journal of Consulting and Clinical Psychology, 68 ,573–579.CrossRefPubMedGoogle Scholar
  34. Weissberg, R.P., & Greenberg, M.T. (1998). Prevention science and collaborative community action research: Combining the best from both perspectives. Journal of Mental Health, 7 ,479–492.CrossRefGoogle Scholar
  35. Winship, C., & Morgan, S. L. (1999). The estimation of causal effects from observational longitudinal data. Annual Review of Sociology, 25 ,659–706.CrossRefGoogle Scholar

Copyright information

© Society for Prevention Research 2004

Authors and Affiliations

  • Linda M. Collins
    • 1
  • Susan A. Murphy
    • 2
  • Karen L. Bierman
    • 3
  1. 1.The Methodology Center and Department of Human Development and Family StudiesThe Pennsylvania State UniversityUniversity Park
  2. 2.Institute for Social Research and Department of StatisticsUniversity of MichiganAnn Arbor
  3. 3.Department of PsychologyThe Pennsylvania State UniversityUniversity Park

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