Bayesian Inference for the Causal Effect of Mediation

Biometrics

Volumn 68, Issue 4, 2012, Pages 1028-1036

  Daniels, Michael J ^a   Roy, Jason A ^b   Kim, Chanmin ^a   Hogan, Joseph W ^c   Perri, Michael G ^d  

^a University of Florida   (United States)

^b UNIVERSITY OF PENNSYLVANIA   (United States)

^c BROWN UNIVERSITY   (United States)

^d UNIVERSITY OF FLORIDA   (United States)

Author keywords

Causal inference; Direct effect; Indirect effect; Mediators; Nonparametric Bayes; Sensitivity analysis

Indexed keywords

BAYESIAN NETWORKS; INFERENCE ENGINES;

BAYESIAN APPROACHES; BAYESIAN INFERENCE; BINARY RESPONSE; CAUSAL INFERENCES; DIRECT EFFECT; INDIRECT EFFECTS; MEDIATOR; NON-PARAMETRIC BAYES; NON-PARAMETRIC BAYESIAN; SENSITIVITY PARAMETERS;

SENSITIVITY ANALYSIS;

BAYESIAN ANALYSIS; DATA SET; SENSITIVITY ANALYSIS; WEIGHT;

EID: 84871667740     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2012.01781.x     Document Type: Article

References (22)

1. Baron, R. M. and Kenny, D. A. (1986). The moderator mediator variable distinction in social psychological-research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology 51, 1173-1182.
2. Elliott, M. R., Raghunathan, T. E., and Li, Y. (2010). Bayesian inference for causal mediation effects using principal stratification using principal stratification with dichotomous mediators and outcomes. Biostatistics 11, 353-372.
3. Frangakis, C. E. and Rubin, D. B. (2002). Principal stratification in causal inference. Biometrics 58, 21-29.
4. Gallop, R., Small, D. S., Y., L. J., Elliot, M. R., Joffe, M., and Ten Have, T. R. (2009). Mediation analysis with principal stratification. Statistics in Medicine 28, 1108-1130.
5. Imai, K., Keele, L., and Tingley, D. (2010a). A general approach to causal mediation analysis. Psychological Methods 15, 309-334.
6. Imai, K., Keele, L., and Yamamoto, T. (2010b). Identification, inference and sensitivity analysis for causal mediation effects. Statistical Science 25, 51-71.
7. Joffe, M. and Greene, T. (2009). Related causal frameworks for surrogate outcomes. Biometrics 65, 530-538.
8. Kraemer, H. C., Wilson, G. T., Fairburn, C. G., and Agras, W. S. (2002). Mediators and moderators of treatment effect in randomized clinical trials. Archives of General Psychiatry 59, 877-883.
9. Li, Y., Taylor, J., Elliott, M., and Sargent, D. (2011). Causal assessment of surrogacy in a meta-analysis of colorectal cancer trials. Biostatistics 12, 479-492.
10. MacKinnon, D. P. (2008). Introduction to Statistical Mediation Analysis New York: Lawrence Earlbaum Associates.
11. Nelsen, R. (1999). An Introduction to Copulas New York: Springer-Verlag Inc.
12. Pearl, J. (2001). Direct and indirect effects. In Proceedings of the 17th Conference on Uncertainty in Artificial Intelligence, pp. 411-420. San Francisco, CA: Morgan Kaufman.
13. Perri, M., Limacher, M., Durning, P., Janicke, D., Lutes, L., Bobroff, L., Dale, M., Daniels, M., Radcliff, T., and Martin, A. (2008). Treatment of obesity in underserved rural settings (tours): A randomized trial of extended-care programs for weight management in women. Archives of Internal Medicine 168, 2347-2354.
14. Robins, J. (1999). Association, causation and marginal structural models. Synthese 121, 151-179.
15. Robins, J. M. and Greenland, S. (1992). Identifiability and exchangeability for direct and indirect effects. Epidemiology 3, 143-155.
16. Ten Have, T. R., Joffe, M. M., Lynch, K. G., Brown, G. K., Maisto, S. A., and Beck, A. T. (2007). Causal mediation analyses with rank preserving models. Biometrics 63, 926-934.
17. VanderWeele, T. J. (2008). Simple relations between principal stratification and direct and indirect effects. Statistics & Probability Letters 78, 2957-2962.
18. VanderWeele, T. J. (2009). Marginal structural models for the estimation of direct and indirect effects. Epidemiology 20, 18-26.
19. VanderWeele, T. J. (2010). Bias formulas for sensitivity analysis for direct and indirect effects. Epidemiology 21, 540-551.
20. Vansteelandt, S. (2009). Estimating direct effects in cohort and case-control studies. Epidemiology 20, 851-860.
21. Wolfson, J. and Gilbert, P. (2010). Statistical identifiability and the surrogate endpoint problem with application to vaccine trials. Biometrics 66, 1153-1161.
22. Yuan, Y. and MacKinnon, D. (2009). Bayesian mediation analysis. Psychological Methods 14, 301-322.