Invited Commentary: Boundless Science - Putting Natural Direct and Indirect Effects in a Clearer Empirical Context

American Journal of Epidemiology

Volumn 182, Issue 2, 2015, Pages 109-114

  Naimi, Ashley I ^a  

^a MCGILL UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

EMPIRICAL ANALYSIS; EPIDEMIOLOGY; ESTIMATION METHOD; IDENTIFICATION METHOD; RISK ASSESSMENT; UNCERTAINTY ANALYSIS;

ATTRIBUTABLE RISK; AUTHOR; EMPIRICISM; HUMAN; REVIEW; RISK; SCIENCE; STATISTICAL PARAMETERS; UNCERTAINTY; EPIDEMIOLOGY; STATISTICS;

EPIDEMIOLOGIC METHODS; STATISTICS AS TOPIC;

EID: 84936754983     PISSN: 00029262     EISSN: 14766256     Source Type: Journal    
DOI: 10.1093/aje/kwv060     Document Type: Review

References (30)

1. Judd CM, Kenny DA. Process analysis: estimating mediation in treatment evaluations. Eval Rev. 1981;5(5):602-619.
2. Wright S. The method of path coefficients. Ann Math Stat. 1934;5(3):161-215.
3. Robins JM, Greenland S. Identifiability and exchangeability for direct and indirect effects. Epidemiology. 1992;3(2): 143-155.
4. Cole SR, Hernán MA. Fallibility in estimating direct effects. Int J Epidemiol. 2002;31(1):163-165.
5. Kaufman JS, Maclehose RF, Kaufman S. A further critique of the analytic strategy of adjusting for covariates to identify biologic mediation. Epidemiol Perspect Innov. 2004;1(1):4.
6. Ten Have TR, Joffe MM. A review of causal estimation of effects in mediation analyses. Stat Methods Med Res. 2012; 21(1):77-107.
7. Jiang Z, VanderWeele TJ. When is the difference method conservative for assessing mediation? Am J Epidemiol. 2015; 182(2):105-108.
8. Naimi AI, Kaufman JS, MacLehose RF. Mediation misgivings: ambiguous clinical and public health interpretations of natural direct and indirect effects. Int J Epidemiol. 2014;43(5): 1656-1661.
9. Rubin DB. Causal inference using potential outcomes. J Am Stat Assoc. 2005;100(469):322-331.
10. Robins JM. Semantics of causal DAG models and the identification of direct and indirect effects. In: Green PJ, Hjort NL, Richardson S, eds. Highly Structured Stochastic Systems. New York, NY: Oxford University Press; 2003: 70-80.
11. Richardson TS, Robins JM. Single World Intervention Graphs (SWIGs): A Unification of the Counterfactual and Graphical Approaches to Causality. (Technical report no. 128). Seattle, WA: Center for Statistics and the Social Sciences, University of Washington; 20. http://www.csss.washington.edu/Papers/ wp128.pdf. Accessed August 26, 2013.
12. Tchetgen Tchetgen EJ, Phiri K. Bounds for pure direct effect. Epidemiology. 2014;25(5):775-776.
13. Robins J, Richardson T. Alternative graphical causal models and the identification of direct effects. In: Keyes KM, Ornstein K, Shrout PE, eds. Causality and Psychopathology: Finding the Determinants of Disorders and Their Cures. New York, NY: Oxford University Press; 2011:103-158.
14. VanderWeele TJ. Marginal structural models for the estimation of direct and indirect effects [published correction appears in Epidemiology. 2009;20(4):629]. Epidemiology. 2009;20(1): 18-26.
15. Cai Z, Kuroki M, Pearl J, et al. Bounds on direct effects in the presence of confounded intermediate variables. Biometrics. 2008;64(3):695-701.
16. Kaufman S, Kaufman JS, Maclehose RF. Analytic bounds on causal risk differences in directed acyclic graphs involving three observed binary variables. J Stat Plan Inference. 2009;139(10): 3473-3487.
17. Sjölander A. Bounds on natural direct effects in the presence of confounded intermediate variables. Stat Med. 2009;28(4): 558-571.
18. Chiba Y, Taguri M. Alternative monotonicity assumptions for improving bounds on natural direct effects. Int J Biostat. 2013; 9(2):235-249.
19. VanderWeele TJ, Vansteelandt S. Odds ratios for mediation analysis for a dichotomous outcome. Am J Epidemiol. 2010; 172(12):1339-1348.
20. Tchetgen Tchetgen EJ. A note on formulae for causal mediation analysis in an odds ratio context. Epidemiol Method. 2014;2(1): 21-31.
21. Zou G. A modified Poisson regression approach to prospective studies with binary data. Am J Epidemiol. 2004;159(7): 702-706.
22. Spiegelman D, Hertzmark E. Easy SAS calculations for risk or prevalence ratios and differences. Am J Epidemiol. 2005; 162(3):199-200.
23. Chu H, Cole SR. Estimation of risk ratios in cohort studies with common outcomes: a Bayesian approach. Epidemiology. 2010; 21(6):855-862.
24. Tchetgen Tchetgen E. Estimation of risk ratios in cohort studies with a common outcome: a simple and efficient two-stage approach. Int J Biostat. 2013;9(2):251-264.
25. Tchetgen Tchetgen EJ, VanderWeele TJ. Identification of natural direct effects when a confounder of the mediator is directly affected by exposure. Epidemiology. 2014;25(2):282-291.
26. Avin C, Shpitser I, Pearl J. Identifiability of path-specific effects. In: Bacchus F, Jaakkola T, eds. Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence. (Technical report R-321). Arlington, VA: AUAI Press; 2005:357-363.
27. VanderWeele TJ, Vansteelandt S, Robins JM. Effect decomposition in the presence of an exposure-induced mediator-outcome confounder. Epidemiology. 2014;25(2): 300-306.
28. Richardson TS, Robins JM. Analysis of the binary instrumental variable model. In: Dechter R, Geffner H, Halpern J, eds. Heuristics, Probability and Causality: A Tribute to Judea Pearl. London, United Kingdom: College Publications; 2010:415-444.
29. Sato T, Matsuyama Y. Marginal structural models as a tool for standardization. Epidemiology. 2003;14(6):680-686.
30. Localio AR, Margolis DJ, Berlin JA. Relative risks and confidence intervals were easily computed indirectly from multivariable logistic regression. J Clin Epidemiol. 2007;60(9): 874-882.