Estimation of direct and indirect effects

Causality: Wiley Series in Probability and Statistics

Volumn , Issue , 2012, Pages 126-150

  Vansteelandt, Stijn ^a  

^a GHENT UNIVERSITY   (Belgium)

Author keywords

Direct and indirect effect estimation, and principal stratification; Direct, indirect effect, between total effect and natural direct effect; Estimation techniques, and consistency assumption; G estimation of controlled direct effects, transforming mediator effects; G estimation principle to logistic, for dichotomous outcomes; Mediation analyses, measurements on mediator outcome confounders; Mediation analyses, separating 'indirect effects' from 'direct effects'; Potential outcomes terminology; Specialized estimation techniques, adjusting to confounders; Statistical mediation, direct effect with residual outcome exposure association

Indexed keywords

EID: 84957098999     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1002/9781119945710.ch11     Document Type: Chapter

References (50)

1. Aalen, O. (1989) A linear-regression model for the analysis of life times. Statistics in Medicine, 8, 907-925.
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. Berzuini, C., Vansteelandt, S., Foco, L., Pastorino, R. and Bernardinelli, L. (2011) Direct genetic effects and their estimation from matched case-control data. arxiv: 1112.5130v1 [stat.ME] (submitted 21 December 2011).
4. Cao, W., Tsiatis, A. and Davidian, M. (2009) Improving efficiency and robustness of the doubly robust estimator for a population mean with incomplete data. Biometrika, 96, 723-734.
5. Cole, S. and Hernan, M. (2002) Fallibility in estimating direct effects. International Journal of Epidemiology, 31, 163-165.
6. Elliott, M.R., Raghunathan, T.E. and Li, Y. (2010) Bayesian inference for causal mediation effects using principal stratification with dichotomous mediators and outcomes. Biostatistics, 11, 353-372.
7. Emsley, R., Dunn, G. and White, I.R. (2010) Mediation and moderation of treatment effects in randomised controlled trials of complex interventions. Statistical Methods in Medical Research, 19, 237-270.
8. Fosen, J., Ferkingstad, E., Borgan, O. and Aalen, O.O. (2006) Dynamic path analysis - a new approach to analyzing time-dependent covariates. Lifetime Data Analysis, 12, 143-167.
9. Frangakis, C. and Rubin, D. (2002) Principal stratification in causal inference. Biometrics, 58, 21-29.
10. Gallop, R., Small, D.S., Lin, J.Y., Elliott, M.R., Joffe, M. and Ten Have, T.R. (2009) Mediation analysis with principal stratification. Statistics in Medicine, 28, 1108-1130.
11. Goetgeluk, S., Vansteelandt, S. and Goetghebeur, E. (2008) Estimation of controlled direct effects. Journal of the Royal Statistical Society, Series B, 70, 1049-1066.
12. Hafeman, D.M. and VanderWeele, T.J. (2011) Alternative assumptions for the identification of direct and indirect effects. Epidemiology, 22, 753-764.
13. Imai, K., Keele, L. and Tingley, D. (2010a) A general approach to causal mediation analysis. Psychological Methods, 15, 309-334.
14. Imai, K., Keele, L. and Yamamoto, T. (2010b) Identification, inference and sensitivity analysis for causal mediation effects. Statistical Science, 25, 51-71.
15. Joffe, M. and Greene, T. (2009) Related causal frameworks for surrogate outcomes. Biometrics, 65, 530-538.
16. 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.
17. Joffe, M., Yang, W. and Feldman, H. (2011) G-estimation and artificial censoring: problems, challenges, and applications, in Biometrics. Blackwell Publishing Inc. DOI: 10.1111/j.1541-0420.2011.01656.x
18. MacKinnon, D. (2008) An Introduction to Statistical Mediation Analysis. New York: Lawrence Erlbaum Associates.
19. Martinussen, T., Vansteelandt, S., Gerster, M. and Hjelmborg, J. (2011) Estimation of direct effects for survival data using the Aalen additive hazards model. Journal ofthe Royal Statistical Society, Series B, 73, 773-788.
20. Pearl, J. (2001) Direct and indirect effects, in Proceedings of the Seventeenth Conference on Uncertainty and Artificial Intelligence. San Francisco: Morgan Kaufmann, 411-420.
21. Pearl, J. (2009) Causality: Models, Reasoning and Inference. Cambridge: Cambridge University Press.
22. Pearl, J. (2011) Principal stratification - a goal or a tool? The International Journal of Biostatistics, 7, Article 20.
23. Petersen, M., Sinisi, S. and van der Laan, M. (2006) Estimation of direct causal effects. Epidemiology, 17, 276-284.
24. Robins, J. (1986) A new approach to causal inference in mortality studies with a sustained exposure period - application to control of the healthy worker survivor effect. Mathematical Modelling, 7, 1393-1512.
25. Robins, J.M. (1997) Causal inference from complex longitudinal data, in Latent Variable Modelling and Applications to Causality (Los Angeles, CA, 1994), vol. 120 of Lecture Notes in Statistics. New York: Springer, 69-117.
26. Robins, J. (1999a) Marginal structural models versus structural nested models as tools for causal inference, in Statistical Models in Epidemiology: the environment and clinical trials (eds M. Halloran and D. Berry) New York: Springer-Verlag, 95-134.
27. Robins, J. (1999b) Testing and estimation of direct effects by reparameterizing directed acyclic graphs with structural nested models, in Computation, Causation, and Discovery (eds C. Glymour and G. Cooper), Menlo Park, CA/Cambridge, MA: AAAI Press/The MIT Press, 349-405.
28. Robins, J.M. (2003) Semantics of causal dag models and the identification of direct and indirect effects, in Highly Structured Stochastic Systems (eds, P. Green, N. Hjort and S. Richardson). New York: Oxford University Press, 70-81.
29. Robins, J. and Greenland, S. (1994) Adjusting for differential rates of prophylaxis therapy for PCP in high-dose versus low-dose AZT treatment arms in an aids randomized trial. Journal of the American Statistical Association, 89, 737-749.
30. Robins, J.M. and Greenland, S. (1992) Identifiability and exchangeability for direct and indirect effects. Epidemiology, 3, 143-155.
31. Robins, J. and Tsiatis, A. (1991) Correcting for non-compliance in randomized trials using rank-preserving structural failure time models. Communications in Statistics, 20, 26092631.
32. Robins, J., Hernán, M. and Brumback, B. (2000) Marginal structural models and causal inference in epidemiology. Epidemiology, 11, 550-560.
33. Robins, J., Rotnitzky, A. and Vansteelandt, S. (2007) Principal stratification designs to estimate input data missing due to death - discussion. Biometrics, 63, 650-653.
34. 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.
35. Rubin, D. (2004) Direct and indirect causal effects via potential outcomes. Scandinavian Journal of Statistics, 31, 161-170.
36. Sjölander, A. (2009) Bounds on natural direct effects in the presence of confounded intermediate variables. Statistics in Medicine, 28, 558-571.
37. Sobel, M.E. (2008) Identification of causal parameters in randomized studies with mediating variables. Journal ofEducational and Behavioral Statistics, 33, 230-251.
38. Ten Have, T. and Joffe, M. (2011) A review of causal estimation of effects in mediation analyses. Statistical Methods in Medical Research, 21, 77-107.
39. Ten Have, T., Joffe, M., Lynch, K., Brown, G., Maisto, S. and Beck, A. (2007) Causal mediation analyses with rank preserving models. Biometrics, 63, 926-934.
40. van der Laan, M.J. and Petersen, M.L. (2008) Direct effect models. The International Journal of Biostatistics, 4 (1), Article 23.
41. VanderWeele, T.J. (2008) Simple relations between principal stratification and direct and indirect effects. Statistics and Probability Letters, 78, 2957-2962.
42. VanderWeele, T. (2009a) Mediation and mechanism. European Journal of Epidemiology, 24, 217-224. VanderWeele, T.J. (2009b) Marginal structural models for the estimation of direct and indirect effects.
43. VanderWeele, T.J. (2009b) Marginal structural models for the estimation of direct and indirect effects Epidemiology, 20, 18-26.
44. VanderWeele, T.J. (2010) Bias formulas for sensitivity analysis for direct and indirect effects. Epidemiology, 21,540-551.
45. VanderWeele, T.J. and Vansteelandt, S. (2009) Conceptual issues concerning mediation, interventions and composition. Statistics and Its Interface, 2, 457-468.
46. VanderWeele, T.J. and Vansteelandt, S. (2010) Odds ratios for mediation analysis for a dichotomous outcome. American Journal of Epidemiology, 172, 1339-1348.
47. Vansteelandt, S. (2009) Estimating direct effects in cohort and case-control studies. Epidemiology, 20, 851-860.
48. Vansteelandt, S. (2010) Estimation of controlled direct effects on a dichotomous outcome using logistic structural direct effect models. Biometrika, 97, 921-934.
49. Vansteelandt, S. and VanderWeele, T. (2011) Natural direct and indirect effects on the exposed: effect decomposition under weaker assumptions. Technical Report, Ghent University.
50. Vansteelandt, S., Goetgeluk, S., Lutz, S., Waldman, I., Lyon, H., Schadt, E., Weiss, S. and Lange, C. (2009) On the adjustment for covariates in genetic association analysis: a novel, simple principle to infer causality. Genetic Epidemiology, 33, 394-405.