publication . Article . 2014

Causal mediation analysis with multiple mediators

Daniel, Rhian; De Stavola, B. L.; Cousens, S. N.; Vansteelandt, S.;
Open Access English
  • Published: 28 Oct 2014 Journal: volume 71, issue 1, pages 1-14issn: 0006-341X, eissn: 1541-0420, Copyright policy
  • Publisher: Blackwell Publishing Ltd
Abstract
: In diverse fields of empirical research-including many in the biological sciences-attempts are made to decompose the effect of an exposure on an outcome into its effects via a number of different pathways. For example, we may wish to separate the effect of heavy alcohol consumption on systolic blood pressure (SBP) into effects via body mass index (BMI), via gamma-glutamyl transpeptidase (GGT), and via other pathways. Much progress has been made, mainly due to contributions from the field of causal inference, in understanding the precise nature of statistical estimands that capture such intuitive effects, the assumptions under which they can be identified, and ...
Subjects
free text keywords: MODELS, Causal pathways, BAYESIAN-INFERENCE, DECOMPOSITION, Biometric Methodology, IDENTIFICATION, Mathematics and Statistics, Multiple mediation, SENSITIVITY-ANALYSIS, MORTALITY, FORMULA, Natural path-specific effects, R1
41 references, page 1 of 3

Albert, J. M. and Nelson, S. (2011). Generalized causal mediation analysis. Biometrics 67, 1028-1038.

Avin, C., Shpitser, I., and Pearl, J. (2005). Identifiability of pathspecific effects. In Proceedings of the 19th Joint Conference on Artificial Intelligence, 357-363. San Francisco, CA: Morgan Kaufmann Publishers Inc. [OpenAIRE]

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.

Bentler, P. M. (1980). Multivariate analysis with latent variables: Causal modeling. Annual Review of Psychology 31, 419-456.

Cole, S. R. and Frangakis, C. E. (2009). The consistency statement in causal inference: A definition or an assumption? Epidemiology 20, 3-5.

Daniel, R. M., De Stavola, B. L., and Cousens, S. N. (2011). g-formula: Estimating causal effects in the presence of timevarying confounding or mediation using the g-computation formula. The Stata Journal 11, 479-517. [OpenAIRE]

Daniel, R. M., De Stavola, B. L., Cousens, S. N., and Vansteelandt, S. (2014). A review of causal mediation analysis with one mediator. Technical Report, Department of Medical Statistics, London School of Hygiene and Tropical Medicine. [OpenAIRE]

Daniels, M. J., Roy, J. A., Kim, C., Hogan, J. W., and Perri, M. G. (2012). Bayesian inference for the causal effect of mediation. Biometrics 68, 1028-1036.

Imai, K., Keele, L., and Tingley, D. (2010). A general approach to causal mediation analysis. Psychological Methods 15, 309- 334. [OpenAIRE]

Imai, K., Keele, L., and Yamamoto, T. (2010). Identification, inference, and sensitivity analysis for causal mediation effects. Statistical Science 25, 51-71. [OpenAIRE]

Imai, K. and Yamamoto, T. (2013). Identification and sensitivity analysis for multiple causal mechanisms: Revisiting evidence from framing experiments. Political Analysis 21, 141.

Leon, D. A., Saburova, L., Tomkins, S., Andreev, E., Kiryanov, N., McKee, M., and Shkolnikov, V. M. (2007). Hazardous alcohol drinking and premature mortality in Russia: A population based case-control study. Lancet 16, 2001-2009.

MacKinnon, D. P. (2000). Contrasts in multiple mediator models. In: Multivariate Applications in Substance Use Research, Rose, J. S., et al. (eds), 141-160, Mahwah, NJ: Lawrence Erlbaum Associates Publishers.

Neyman, J. (1923). On the application of probability theory to agricultural experiments. Essay on principles. Section 9. Statistical Science 5, 465-472.

Pearl, J. (2001). Direct and indirect effects. In Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, 411-420. San Francisco, CA: Morgan Kaufmann Publishers Inc.

41 references, page 1 of 3
Abstract
: In diverse fields of empirical research-including many in the biological sciences-attempts are made to decompose the effect of an exposure on an outcome into its effects via a number of different pathways. For example, we may wish to separate the effect of heavy alcohol consumption on systolic blood pressure (SBP) into effects via body mass index (BMI), via gamma-glutamyl transpeptidase (GGT), and via other pathways. Much progress has been made, mainly due to contributions from the field of causal inference, in understanding the precise nature of statistical estimands that capture such intuitive effects, the assumptions under which they can be identified, and ...
Subjects
free text keywords: MODELS, Causal pathways, BAYESIAN-INFERENCE, DECOMPOSITION, Biometric Methodology, IDENTIFICATION, Mathematics and Statistics, Multiple mediation, SENSITIVITY-ANALYSIS, MORTALITY, FORMULA, Natural path-specific effects, R1
41 references, page 1 of 3

Albert, J. M. and Nelson, S. (2011). Generalized causal mediation analysis. Biometrics 67, 1028-1038.

Avin, C., Shpitser, I., and Pearl, J. (2005). Identifiability of pathspecific effects. In Proceedings of the 19th Joint Conference on Artificial Intelligence, 357-363. San Francisco, CA: Morgan Kaufmann Publishers Inc. [OpenAIRE]

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.

Bentler, P. M. (1980). Multivariate analysis with latent variables: Causal modeling. Annual Review of Psychology 31, 419-456.

Cole, S. R. and Frangakis, C. E. (2009). The consistency statement in causal inference: A definition or an assumption? Epidemiology 20, 3-5.

Daniel, R. M., De Stavola, B. L., and Cousens, S. N. (2011). g-formula: Estimating causal effects in the presence of timevarying confounding or mediation using the g-computation formula. The Stata Journal 11, 479-517. [OpenAIRE]

Daniel, R. M., De Stavola, B. L., Cousens, S. N., and Vansteelandt, S. (2014). A review of causal mediation analysis with one mediator. Technical Report, Department of Medical Statistics, London School of Hygiene and Tropical Medicine. [OpenAIRE]

Daniels, M. J., Roy, J. A., Kim, C., Hogan, J. W., and Perri, M. G. (2012). Bayesian inference for the causal effect of mediation. Biometrics 68, 1028-1036.

Imai, K., Keele, L., and Tingley, D. (2010). A general approach to causal mediation analysis. Psychological Methods 15, 309- 334. [OpenAIRE]

Imai, K., Keele, L., and Yamamoto, T. (2010). Identification, inference, and sensitivity analysis for causal mediation effects. Statistical Science 25, 51-71. [OpenAIRE]

Imai, K. and Yamamoto, T. (2013). Identification and sensitivity analysis for multiple causal mechanisms: Revisiting evidence from framing experiments. Political Analysis 21, 141.

Leon, D. A., Saburova, L., Tomkins, S., Andreev, E., Kiryanov, N., McKee, M., and Shkolnikov, V. M. (2007). Hazardous alcohol drinking and premature mortality in Russia: A population based case-control study. Lancet 16, 2001-2009.

MacKinnon, D. P. (2000). Contrasts in multiple mediator models. In: Multivariate Applications in Substance Use Research, Rose, J. S., et al. (eds), 141-160, Mahwah, NJ: Lawrence Erlbaum Associates Publishers.

Neyman, J. (1923). On the application of probability theory to agricultural experiments. Essay on principles. Section 9. Statistical Science 5, 465-472.

Pearl, J. (2001). Direct and indirect effects. In Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, 411-420. San Francisco, CA: Morgan Kaufmann Publishers Inc.

41 references, page 1 of 3
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publication . Article . 2014

Causal mediation analysis with multiple mediators

Daniel, Rhian; De Stavola, B. L.; Cousens, S. N.; Vansteelandt, S.;