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Prevention Science

, Volume 13, Issue 4, pp 437–447 | Cite as

Introducing the At-Risk Average Causal Effect with Application to HealthWise South Africa

  • Donna L. Coffman
  • Linda L. Caldwell
  • Edward A. Smith
Article

Abstract

Researchers often hypothesize that a causal variable, whether randomly assigned or not, has an effect on an outcome behavior and that this effect may vary across levels of initial risk of engaging in the outcome behavior. In this paper, we propose a method for quantifying initial risk status. We then illustrate the use of this risk-status variable as a moderator of the causal effect of leisure boredom, a non-randomized continuous variable, on cigarette smoking initiation. The data come from the HealthWise South Africa study. We define the causal effects using marginal structural models and estimate the causal effects using inverse propensity weights. Indeed, we found leisure boredom had a differential causal effect on smoking initiation across different risk statuses. The proposed method may be useful for prevention scientists evaluating causal effects that may vary across levels of initial risk.

Keywords

Causal inference Marginal Structural Models Leisure boredom Cigarette smoking initiation 

Notes

Authors’ note

Preparation of this article was supported by NIDA Center Grant P50 DA100075, NIDA R03 DA026543, and NIDDK 5R21DK082858-02. HealthWise was supported by NIDA grant R01 DA01749. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Drug Abuse (NIDA), the National Institute on Diabetes and Digestive and Kidney Diseases (NIDDK), or the National Institutes of Health (NIH).

References

  1. Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. Thousand Oaks, CA: Sage.Google Scholar
  2. Barber, J. S., Murphy, S. A., & Verbitsky, N. (2004). Adjusting for time-varying confounding in survival analysis. Sociological Methodology, 34, 163–192.CrossRefGoogle Scholar
  3. Bray, B. C., Almirall, D., Zimmerman, R. S., Lynam, D., & Murphy, S. A. (2006). Assessing the total effect of time-varying predictors in prevention research. Prevention Science, 7, 1–17.PubMedCrossRefGoogle Scholar
  4. Brumback, B. A., Hernan, M. A., Hanseuse, S. J. P. S., & Robins, J. M. (2004). Sensitivity analysis for unmeasured confounding assuming a marginal structural model for repeated measures. Statistics in Medicine, 23, 749–767.PubMedCrossRefGoogle Scholar
  5. Caldwell, L. L., Smith, E., Flisher, A. J., Wegner, L., Vergnani, T., Mathews, C., & Mpofu, E. (2004). HealthWise South Africa: Development of a life skills curriculum for young adults. World Leisure Journal, 46, 4–17.CrossRefGoogle Scholar
  6. Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  7. Cole, S. R., & Hernan, M. A. (2008). Constructing inverse probability weights for marginal structural models. American Journal of Epidemiology, 168, 656–664.PubMedCrossRefGoogle Scholar
  8. Hirano, K., & Imbens, G. W. (2004). The propensity score with continuous treatments. In A. Gelman & X.-L. Meng (Eds.), Applied Bayesian modeling and causal inference from incomplete-data perspectives (pp. 73–84). Hoboken, NJ: Wiley.Google Scholar
  9. Hong, G., & Raudenbush, S. W. (2005). Effects of kindergarten retention policy on children’s cognitive growth in reading and mathematics. Educational Evaluation and Policy Analysis, 27, 205–224.CrossRefGoogle Scholar
  10. Hong, G., & Raudenbush, S. W. (2006). Evaluating kindergarten retention policy: A case study of causal inference for multi-level observational data. Journal of the American Statistical Association, 101, 901–910.CrossRefGoogle Scholar
  11. Imai, K., & van Dyk, D. A. (2004). Causal inference with general treatment regimes: Generalizing the propensity score. Journal of the American Statistical Association, 99, 854–866.CrossRefGoogle Scholar
  12. Imbens, G. W. (2000). The role of the propensity score in estimating dose-response functions. Biometrika, 83, 706–710.CrossRefGoogle Scholar
  13. Little, R. J. A., & Rubin, D. B. (2002). Statistical analysis with missing data. Hoboken, NJ: Wiley.Google Scholar
  14. Lumley, T. (2010). Survey: Analysis of complex survey samples [software manual]. Retrieved from http://CRAN.R-project.org/package=survey (R package version 3.22-1).
  15. MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
  16. Robins, J. M., Hernan, M. A., & Brumback, B. A. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11, 550–560.PubMedCrossRefGoogle Scholar
  17. Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–55.CrossRefGoogle Scholar
  18. Rosenbaum, P. R., & Rubin, D. B. (1984). Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association, 79, 516–524.CrossRefGoogle Scholar
  19. Rosenbaum, P. R., & Rubin, D. B. (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. The American Statistician, 39, 33–38.Google Scholar
  20. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688–701.CrossRefGoogle Scholar
  21. Rubin, D. B. (2005). Causal inference using potential outcomes: Design, modeling, decisions. Journal of the American Statistical Association, 100, 322–331.CrossRefGoogle Scholar
  22. Schafer, J. L. (1997). Analysis of incomplete multivariate data. London, England: Chapman & Hall.CrossRefGoogle Scholar
  23. Schafer, J. L., & Kang, J. D. Y. (2008). Average causal effects from non-randomized studies: A practical guide and simulated example. Psychological Methods, 13, 279–313.PubMedCrossRefGoogle Scholar
  24. van der Wal, W. M., Prins, M., Lumbreras, B., & Geskus, R. B. (2009). A simple g-computation algorithm to quantify the causal effect of a secondary illness on the progression of a chronic disease. Statistics in Medicine, 28, 2325–2337.PubMedCrossRefGoogle Scholar

Copyright information

© Society for Prevention Research 2012

Authors and Affiliations

  • Donna L. Coffman
    • 1
  • Linda L. Caldwell
    • 2
  • Edward A. Smith
    • 3
  1. 1.The Methodology CenterThe Pennsylvania State UniversityState CollegeUSA
  2. 2.Dept. of Recreation, Park and Tourism ManagementThe Pennsylvania State UniversityUniversity ParkUSA
  3. 3.Prevention Research CenterThe Pennsylvania State UniversityUniversity ParkUSA

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