Natural Direct and Indirect Effects on the Exposed: Effect Decomposition under Weaker Assumptions

Biometrics

Volumn 68, Issue 4, 2012, Pages 1019-1027

  Vansteelandt, Stijn ^a   Vanderweele, Tyler J ^b  

^a GHENT UNIVERSITY   (Belgium)

^b HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Causal inference; Direct effect; Indirect effect; Mediation; Pathway; Time varying confounding

Indexed keywords

ADDITIONAL KNOWLEDGE; CAUSAL INFERENCES; CONDITION; CONFOUNDER; DIRECT EFFECT; INDIRECT EFFECTS; MEDIATION; PATHWAY; TIME VARYING; TIME-VARYING CONFOUNDING;

BIOMETRICS;

BIOMETRY; DATABASE; TEMPORAL VARIATION;

EID: 84871661363     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2012.01777.x     Document Type: Article

References (28)

1. Avin, C., Shpitser, I., and Pearl, J. (2005). Identifiability of path-specific effects. In Proceedings of the International Joint Conference on Artificial Intelligence, pp. 357-363, Edinburgh
2. Baron, R. and 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.
3. Cole, S. and Hernán, M. (2002). Fallibility in estimating direct effects. International Journal of Epidemiology 31, 163-165.
4. Didelez, V., Dawid, A., and Geneletti, S. (2006). Direct and indirect effects of sequential treatments. In Proceedings of the 22nd Annual Conference on Uncertainty in Artifical Intelligence, pp. 138-146, Cambridge, MA
5. Hernan, M. and Robins, J. (2006). Instruments for causal inference-An epidemiologist's dream? Epidemiology 17, 360-372.
6. Hernan, M. A. and VanderWeele, T. J. (2011). Compound treatments and transportability of causal inference. Epidemiology 22, 368-377.
7. Imai, K., Keele, L., and Yamamoto, T. (2010). Identification, inference and sensitivity analysis for causal mediation effects. Statistical Science 25, 51-71.
8. Joffe, M., Byrne, C., and Colditz, G. (2001). Postmenopausal hormone use, screening, and breast cancer: Characterization and control of a bias. Epidemiology 12, 429-438.
9. Kotelchuck, M. (1994). An evaluation of the Kessner adequacy of prenatal care index and a proposed adequacy of prenatal care utilization index. American Journal of Public Health 84, 1414-1420.
10. Kurth, T., Walker, A., Glynn, R., Chan, K., Gaziano, J., Berger, K., and Robins, J. (2006). Results of multivariable logistic regression, propensity matching, propensity adjustment, and propensity-based weighting under conditions of nonuniform effect. American Journal of Epidemiology 163, 262-270.
11. MacKinnon, D. (2008). An Introduction to Statistical Mediation Analysis New York: Lawrence Erlbaum Associates.
12. Ogburn, E. and VanderWeele, T. (2012). Analytic results on the bias due to nondifferential misclassification of a binary mediator. American Journal of Epidemiology, in press.
13. Pearl, J. (2001). Direct and indirect effects. In Proceedings of the Seventeenth Conference on Uncertainty and Artificial Intelligence, pp. 411-420, San Francisco Morgan Kaufmann.
14. Petersen, M., Sinisi, S., and van der Laan, M. (2006). Estimation of direct causal effects. Epidemiology 17, 276-284.
15. Robins, J. M. (1999). Testing and estimation of direct effects by reparameterizing directed acyclic graphs with structural nested models. In Computation, Causation, and Discovery, C. Glymour and G. Cooper (eds), pp. 349-405. Menlo Park, CA: AAAI Press.
16. Robins, J. M. (2003). Semantics of causal dag models and the identification of direct and indirect effects. In Highly Structured Stochastic Systems, Green, P., Hjort, N., and Richardson, S. (eds), pp. 70-81. New York: Oxford University Press.
17. Robins, J. M. and Greenland, S. (1992). Identifiability and exchangeability for direct and indirect effects. Epidemiology 3, 143-155.
18. Rosenblum, M., Jewell, N. P., van der Laan, M., Shiboski, S., van der Straten, A., and Padian, N. (2009). Analysing direct effects in randomized trials with secondary interventions: An application to human immunodeficiency virus prevention trials. Journal of the Royal Statistical Society, Series A 172, 443-465.
19. Tchetgen Tchetgen, E. and Shpitser, I. (2011). Semiparametric estimation of models for natural direct and indirect Effects Technical Report, Harvard University.
20. van der Laan, M. and Petersen, M., (2008).Direct effect models. The International Journal of Biostatistics 4, 1-27.
21. VanderWeele, T. (2009). Mediation and mechanism. European Journal of Epidemiology 24, 217-224.
22. VanderWeele, T. J. (2009). Marginal structural models for the estimation of direct and indirect effects. Epidemiology 20, 18-26.
23. VanderWeele, T. J., Lantos, J. D., Siddique, J., and Lauderdale, D. S. (2009). A comparison of four prenatal care indices in birth outcome models: Comparable results for predicting small-for-gestational-age outcome but different results for preterm birth or infant mortality. Journal of Clinical Epidemiology 62, 438-445.
24. VanderWeele, T. J. and Vansteelandt, S. (2009). Conceptual issues concerning mediation, interventions and composition. Statistics and Its Interface 2, 457-468.
25. Vansteelandt, S. (2009a). Discussion on "Identifiability and estimation of causal effects in randomized trials with noncompliance and completely nonignorable missing data". Biometrics 65, 686-689.
26. Vansteelandt, S. (2009b). Estimating direct effects in cohort and case-control studies. Epidemiology 20, 851-860.
27. Vansteelandt, S., Mertens, K., Suetens, C., and Goetghebeur, E. (2009). Marginal structural models for partial exposure regimes. Biostatistics 10, 46-59.
28. Zhang, M., Joffe, M. M., and Small, D. S. (2011). Causal inference for continuous-time processes when covariates are observed only at discrete times. Annals of Statistics 39, 131-173.