Erratum to: Subgroups Analysis when Treatment and Moderators are Time-varying (Prev Sci, 10.1007/s11121-011-0208-7);Subgroups Analysis when Treatment and Moderators are Time-varying

Prevention Science

Volumn 14, Issue 2, 2013, Pages 169-178

  Almirall, Daniel ^a   McCaffrey, Daniel F ^b   Ramchand, Rajeev ^b   Murphy, Susan A ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

^b RAND CORPORATION   (United States)

Author keywords

Collider bias; Effect moderation; heterogeneity; Time varying treatment effects; Treatment effect

Indexed keywords

EID: 84875604241     PISSN: 13894986     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11121-012-0336-8     Document Type: Erratum

References (18)

1. Almirall, D., Ten Have, T., & Murphy, S. (2009). Structural nested mean models for assessing time-varying effect moderation. Biometrics, 66, 131-139.
2. Barber, J., Murphy, S., & Verbitsky, N. (2004). Adjusting for time-varying confounding in survival analysis. Sociological Methodology, 34, 163-192.
3. Baron, R., & Kenny, D. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173-1182.
4. Bray, B., Almirall, D., Zimmerman, R., Lynam, D., & Murphy, S. (2006). Assessing the total effect of time-varying predictors in prevention research. Prevention Science, 7, 1-17.
5. Dennis, M. L., Titus, J. C., White, M. K., Unsicker, J. I., & Hodgkins, D. (2002). Global appraisal of individual needs: Administration guide for the GAIN and related measures. Bloomington, IL. http://www. chestnut. org/li/gain.
6. Greenland, S., Pearl, J., & Robins, J. M. (1999). Causal diagrams for epidemiologic research. Epidemiology, 10, 37-48.
7. Holland, P. (1986). Statistics and causal inference. Journal of the American Statistical Association, 81, 945-970.
8. Murphy, S. (2005). An experimental design for the development of adaptive treatment strategies. Statistics in Medicine, 24, 1455-1481.
9. Murphy, S., Laan, M. J. van der, Robins, J. M., & Conduct Problems Prevention Research Group. (2001). Marginal mean models for dynamic regimes. Journal of the American Statistical Association, 96, 1410-1423.
10. Pearl, J. (1998). Graphs, causality, and structural equation models. Sociological Methods and Research, 27, 226-284.
11. R Development Core Team. (2009). R: A language and environment for statistical computing. http://www. R-project. org. Vienna, Austria.
12. Robins, J. M. (1987). A graphical approach to the identification and estimation of causal parameters in mortality studies with sustained exposure periods. Journal of Chronic Disease, 40(Supplement 2), 139s-161s.
13. Robins, J. M. (1989). The control of confounding by intermediate variables. Statistics in Medicine, 8, 679-701.
14. Robins, J. M. (1994). Correcting for non-compliance in randomized trials using structural nested mean models. Communications in Statistics, Theory and Methods, 23, 2379-2412.
15. Robins, J. M. (1997). Latent variable modeling and applications to causality. In M. Berkane (Ed.), Causal inference from complex longitudinal data (pp. 69-117). New York: Springer.
16. Robins, J. M., Hernan, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11, 550-560.
17. Rosenbaum, P. (1984). The consequences of adjustment for a concomitant variable that has been affected by the treatment. Journal of the Royal Statistical Society, A, 147, 656-666.
18. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688-701.