Investigating the impact of selection bias in dose-response analyses of preventive interventions

Prevention Science

Volumn 11, Issue 3, 2010, Pages 239-251

  McGowan, Herle M ^a   Nix, Robert L ^b   Murphy, Susan A ^c   Bierman, Karen L ^b  

^a NORTH CAROLINA STATE UNIVERSITY   (United States)

^b PENNSYLVANIA STATE UNIVERSITY   (United States)

^c UNIVERSITY OF MICHIGAN   (United States)

Author keywords

Dose response; Preventive interventions; Selection bias; Simulations

Indexed keywords

ARTICLE; EPIDEMIOLOGY; MONTE CARLO METHOD; ORGANIZATION AND MANAGEMENT; PREVENTIVE HEALTH SERVICE; SELECTION BIAS;

MONTE CARLO METHOD; PREVENTIVE HEALTH SERVICES; SELECTION BIAS;

EID: 77954621624     PISSN: 13894986     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11121-010-0169-2     Document Type: Article

References (47)

1. Achenbach, T. M. (1991). Manual for the Teacher's Report Form and 1991 profile. Burlington: University of Vermont Department of Psychiatry.
2. Barber, J. S., Murphy, S. A., & Verbitsky, N. (2004). Adjusting for time-varying confounding in survival analysis. Sociological Methodology, 34, 163-192.
3. Bierman, K. L., Greenberg, M. T., & Conduct Problems Prevention Research Group. (1996). Social skills training in the Fast Track program. In R. D. Peters & R. J. McMahon (Eds.), Preventing childhood disorders, substance abuse, and delinquency (pp. 65-89). Thousand Oaks, CA: Sage.
4. Bodnar, L. M., Davidian, M., Siega-Riz, A. M., & Tsiatis, A. A. (2004). Marginal structural models for analyzing causal effects of time-dependent treatments: An application in perinatal epidemiology. American Journal of Epidemiology, 159, 926-934.
5. Box, G. E. P., Hunter, W. G., & Hunter, J. S. (1978). Statistics for experimenters. An introduction to design, data analysis, and model building. New York: Wiley.
6. Bray, B., Almirall, D., Zimmerman, R., Lynam, D., & Murphy, S. A. (2006). Assessing the total effect of time-varying predictors in prevention research. Prevention Science, 7, 1-17.
7. Cicchetti, D., & Hinshaw, S. P. (2002). Prevention and intervention science: Contributions to developmental theory. Development and Psychopathology, 14, 667-671.
8. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
9. Coie, J. D., & Dodge, K. A. (1988). Multiple sources of data on social behavior and social status in the school: A cross-age comparison. Child Development, 59, 815-829.
10. Cole, S. R., & Hernán, M. A. (2008). Constructing inverse probability weights for marginal structural models. American Journal of Epidemiology, 168, 656-664.
11. Collins, L. M., Murphy, S. A., & Bierman, K. A. (2004). A conceptual framework for adaptive preventive interventions. Prevention Science, 5, 185-196.
12. Collins, L. M., Murphy, S. A., Nair, V. N., & Strecher, V. (2005). A strategy for optimizing and evaluating behavioral interventions. Annals of Behavioral Medicine, 30, 65-73.
13. Collins, L. M., Murphy, S. A., & Strecher, V. (2007). The Multiphase Optimization Strategy (MOST) and the Sequential Multiple Assignment Randomized Trial (SMART): New methods for more potent ehealth interventions. American Journal of Preventive Medicine, 32, S112-S118.
14. Conduct Problems Prevention Research Group. (1992). A developmental and clinical model for the prevention of conduct disorders: The FAST Track program. Development and Psychopathology, 4, 509-527.
15. Conduct Problems Prevention Research Group. (1999). 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.
16. Conduct Problems Prevention Research Group. (2002). Evaluation of the first 3 years of the Fast Track prevention trial with children at high risk for adolescent conduct problems. Journal of Abnormal Child Psychology, 30, 19-35.
17. Dimidjian, S., Hollon, S. D., Dobson, K. S., Schmaling, K. B., Kohlenberg, R. J., Addis, M. E., et al. (2006). Randomized trial of behavioral activation, cognitive therapy, and antidepressant medication in the acute treatment of adults with major depression. Journal of Consulting and Clinical Psychology, 74, 658-670.
18. Dobson, K. S., Hollon, S. D., Dimidjian, S., Schmaling, K. B., Kohlenberg, R. J., Gallop, R., et al. (2008). Randomized trial of behavioral activation, cognitive therapy, and anti-depressant medication in the prevention of relapse and recurrence of major depression. Journal of Consulting and Clinical Psychology, 76, 468-477.
19. Domitrovich, C. E., & Greenberg, M. T. (2000). The study of implementation: Current findings from effective programs that prevent mental disorders in school-aged children. Journal of Educational and Psychological Consultation, 11, 193-221.
20. Durlak, J. A., & DuPre, E. P. (2008). Implementation matters: A review of research on the influence of implementation on program outcomes and the factors affecting implementation. American Journal of Community Psychology, 41, 327-350.
21. Feinstein, A. L. (1991). Intention to treat policy for analyzing randomized trials: statistical distortions and neglected clinical challenges. In J. Cramer & B. Spilker (Eds.), Patient compliance in medical practice and clinical trials (pp 359-370). New York: Raven.
22. Hall, R. C. W. (1995). Global assessment of functioning: A modified scale. Psychosomatics: Journal of Consultation Liaison Psychiatry, 36, 267-275.
23. Heckman, J. (1976). The common structure of statistical models of truncation, sample selection, and limited dependent variables and a simple estimator for such models. Annals of Economic and Social Measurement, 5, 475-492.
24. Hernán, M. A., Brumback, B., & Robins, J. M. (2000). Estimating the causal effect of zidovudine on CD4 count with a marginal structural model for repeated measures. Statistics in Medicine, 21, 1689-1709.
25. Hill, J. L., Brooks-Gunn, J., & Waldfogel, J. (2003). Sustained effects of high participation in an early intervention for low birth-weight premature infants. Developmental Psychology, 39, 730-744.
26. Jacobson, N. S., Schmaling, K. B., Holtzworth-Munroe, A., Katt, J. L., Wood, L. F., & Follette, V. M. (1989). Research-structured vs. clinically flexible versions of social learning-based marital therapy. Behaviour Research and Therapy, 27, 173-180.
27. Kreuter, M., Farrell, D., Olevitch, L., & Brennan, L. (2000). Tailoring health messages: Customizing communication with computer technology. Mahwah, NJ: Erlbaum.
28. Lavori, P. W., Dawon, R., & Roth, A. J. (2000). Flexible treatment strategies in chronic disease: Clinical and research implications. Biological Psychiatry, 48, 605-614.
29. Lyons-Ruth, K., & Melnick, S. (2004). Dose-response effect of mother-infant clinical home visiting on aggressive behavior problems in kindergarten. Journal of the American Academy of Child and Adolescent Psychiatry, 43, 699-707.
30. Mortimer, K. M., Neugebauer, R., van der Laan, M., & Tager, I. B. (2005). An application of model-fitting procedures for marginal structural models. American Journal of Epidemiology, 162, 382-388.
31. Murphy, S. A., Oslin, D., Rush, A. J., & Zhu, J. for MCATS. (2006). Methodological challenges in constructing effective treatment sequences for chronic disorders. Neuropsychopharmacology, 32, 257-262.
32. Neter, J., Kutner, M. H., Nachtsheim, C. J., & Wasserman, W. (1996). Applied linear statistical models (4th ed.). New York: McGraw-Hill.
33. Pocock, S. J., & Abdalla, M. (1998). The hope and hazards of using compliance data in randomized controlled trials. Statistics in Medicine, 17, 303-317.
34. Robins, J. M. (1999). Association, causation, and marginal structural models. Synthese, 121, 151-179.
35. Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11, 550-560.
36. Rohrbach, L. A., Graham, J. W., & Hansen, W. B. (1993). Diffusion of school-based substance abuse prevention program: Predictors of program implementation. Preventive Medicine, 22, 237-260.
37. Rosenbaum, P. R. (1984a). The consequences of adjustment for a concomitant variable that has been affected by the treatment. Journal of the Royal Statistical Society, Series A, 147, 656-666.
38. Rosenbaum, P. R. (1984b). From association to causation in observational studies: The role of tests of strongly ignorable treatment assignment. Journal of the American Statistical Association, 79, 41-48.
39. Rosenbaum, P. R. (2002). Observational studies (2nd ed.). New York: Springer.
40. Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41-55.
41. Rubin, D. B. (1997). Estimating causal effects from large data sets using propensity scores. Annals of Internal Medicine, 127, 757-763.
42. Seitz, V., Apfel, N. H., & Rosenbaum, L. K. (1991). Effects of an intervention program for pregnant adolescents: Educational outcomes at two years postpartum. American Journal of Community Psychology, 19, 911-930.
43. Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. New York: Houghton Mifflin.
44. Silverman, W. K. (2006). Shifting our thinking and training from evidence-based treatments to evidence-based explanations of treatments. In Balance: Society of Clinical Child and Adolescent Psychology Newsletter, 21.
45. Trochim, W. M. (2006). The research methods knowledge base (2nd ed.). Retrieved November 10, 2009, from http://www. socialresearchmethods. net/kb/expfact. php.
46. Wilkinson, L., & The Task Force on Statistical Inference on Statistical Inference. (1999). Statistical methods in psychology journals: Guidelines and explanations. American Psychologist, 54, 594-604.
47. Winship, C., & Mare, R. D. (1992). Models for sample selection bias. Annual Review of Sociology, 18, 327-350.