Assessing mediation using marginal structural models in the presence of confounding and moderation

Psychological Methods

Volumn 17, Issue 4, 2012, Pages 642-664

  Coffman, Donna L ^a   Zhong, Wei ^b  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

^b XIAMEN UNIVERSITY   (China)

Author keywords

Inverse propensity weighting; Marginal structural model; Mediation

Indexed keywords

ARTICLE; EPIDEMIOLOGY; HUMAN; PROPENSITY SCORE; REGRESSION ANALYSIS; STATISTICAL MODEL;

CONFOUNDING FACTORS (EPIDEMIOLOGY); EFFECT MODIFIER, EPIDEMIOLOGIC; HUMANS; MODELS, STATISTICAL; PROPENSITY SCORE; REGRESSION ANALYSIS;

EID: 84861176367     PISSN: 1082989X     EISSN: None     Source Type: Journal    
DOI: 10.1037/a0029311     Document Type: Article

References (82)

1. Albert, J. M. (2008). Mediation analysis via potential outcomes. Statistics in Medicine, 27, 1282-1304. doi:10.1002/sim.3016
2. Angrist, J. D., Imbens, G. W., & Rubin, D. B. (1996). Identification of causal effects using instrumental variables. Journal of the American Statistical Association, 91, 444-455.
3. Avin, C., Shpitser, I., & Pearl, J. (2005). Identifiability of path-specific effects. In Proceedings of the 19th International Joint Conference on Artificial Intelligence (pp. 357-363). Palo Alto, CA: Association for the Advancement of Artificial Intelligence.
4. Barber, J. S., Murphy, S. A., & Verbitsky, N. (2004). Adjusting for time-varying confounding in survival analysis. Sociological Methodology, 34, 163-192. doi:10.1111/j.0081-1750.2004.00151.x
5. Baron, R. M., & 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. doi:10.1037/0022-3514.51.6.1173
6. Bodnar, L. M., Davidian, M., Siega-Riz, A. M., & Tsiatis, A. A. (2004). Marginal structural models for analyzing causal effects of timedependent treatments: An application in perinatal epidemiology. American Journal of Epidemiology, 159, 926-934. doi:10.1093/aje/kwh131
7. Bray, B. C., Almirall, D., Zimmerman, R. S., Lynam, D., & Murphy, S. A. (2006). Assessing the total effect of time-varying predictors in prevention research. Prevention Science, 7, 1-17. doi:10.1007/s11121-0050023-0
8. Coffman, D. L. (2011). Causal inference for mediation analysis with propensity scores. Structural Equation Modeling, 18, 357-369.
9. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
10. Cole, S. R., & Hernan, M. A. (2008). Constructing inverse probability weights for marginal structural models. American Journal of Epidemiology, 168, 656-664. doi:10.1093/aje/kwn164
11. Cole, S. R., Hernan, M. A., Anastos, K., Jamieson, B. D., & Robins, J. M. (2007). Determining the effect of highly active antiretroviral therapy on changes in human immunodeficiency virus Type 1 RNA viral load using a marginal structural left-censored mean model. American Journal of Epidemiology, 166, 219-227. doi:10.1093/aje/kwm047
12. Derogatis, L. R., Lipman, R. S., Rickels, K., Uhlenhuth, E. H., & Covi, L. (1974). The Hopkins Symptom Checklist (HSCL): A self-report symptom inventory. Behavioral Science, 19, 1-15. doi:10.1002/bs .3830190102
13. Edwards, J. R., & Lambert, L. S. (2007). Methods for integrating moderation and mediation: A general analytical framework using moderated path analysis. Psychological Methods, 12, 1-22. doi:10.1037/1082989X.12.1.1
14. Elliott, M. R., Raghunathan, T. E., & Li, Y. (2010). Bayesian inference for causal mediation effects using principal stratification with dichotomous mediators and outcomes. Biostatistics, 11, 353-372. doi:10.1093/ biostatistics/kxp060
15. Emsley, R., Dunn, G., & 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. doi: 10.1177/0962280209105014
16. Frangakis, C. E. (2004). Principal stratification. In A. Gelman & X.-L. Meng (Eds.), Applied Bayesian modeling and causal inference from incomplete-data perspectives (pp. 97-108). Hoboken, NJ: Wiley.
17. Frangakis, C. E., & Rubin, D. B. (2002). Principal stratification in causal inference. Biometrics, 58, 21-29. doi:10.1111/j.0006-341X.2002 .00021.x
18. Gallop, R., Small, D. S., Lin, J. Y., Elliot, M. R., Joffe, M., & Ten Have, T. R. (2009). Mediation analysis with principal stratification. Statistics in Medicine, 28, 1108-1130. doi:10.1002/sim.3533
19. Ghosh, D., Elliott, M. R., & Taylor, J. M. G. (2010). Links between analysis of surrogate endpoints and endogeneity. Statistics in Medicine, 29, 2869-2879. doi:10.1002/sim.4027
20. Greenland, S., & Morgenstern, H. (2001). Confounding in health research. Annual Review of Public Health, 22, 189-212. doi:10.1146/ annurev.publhealth.22.1.189
21. Hafeman, D. M., & VanderWeele, T. J. (2011). Alternative assumptions for the identification of direct and indirect effects. Epidemiology, 22, 753-764. doi:10.1097/EDE.0b013e3181c311b2
22. Holland, P. W. (1986). Statistics and causal inference. Journal of the American Statistical Association, 81, 945-970.
23. Holland, P. W. (1988). Causal inference, path analysis, and recursive structural equations models. Sociological Methodology, 18, 449-484. doi:10.2307/271055
24. Hong, G., & Raudenbush, S. W. (2005). Effects of kindergarten retention policy on children's cognitive growth in reading and mathematics. Educational Evaluation and Policy Analysis, 27, 205-224. doi:10.3102/ 01623737027003205
25. Hong, G., & Raudenbush, S. W. (2006). Evaluating kindergarten retention policy: A case study of causal inference for multi-level observational data. Journal of the American Statistical Association, 101, 901-910. doi:10.1198/016214506000000447
26. Horvitz, D. G., & Thompson, D. J. (1952). A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47, 663-685.
27. Hudgens, M. G., & Halloran, M. E. (2008). Toward causal inference with interference. Journal of the American Statistical Association, 103, 832-842. doi:10.1198/016214508000000292
28. Imai, K., Keele, L., & Tingley, D. (2010). A general approach to causal mediation analysis. Psychological Methods, 15, 309-334. doi:10.1037/ a0020761
29. Imai, K., Keele, L., & Yamamoto, T. (2010). Identification, inference, and sensitivity analysis for causal mediation effects. Statistics in Medicine, 25, 51-71.
30. Jo, B. (2008). Causal inference in randomized experiments with mediational processes. Psychological Methods, 13, 314-336. doi:10.1037/ a0014207
31. Judd, C. M., & Kenny, D. A. (1981). Process analysis: Estimating mediation in treatment evaluations. Evaluation Review, 5, 602-619. doi: 10.1177/0193841X8100500502
32. Kang, J., & Schafer, J. L. (2007). Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data. Statistical Science, 22, 523-539. doi:10.1214/07STS227
33. Keele, L., Tingley, D., Yamamoto, T., & Imai, K. (2009). Mediation: R package for causal mediation analysis (R package Version 2.1) [Computer software]. Retrieved from http://cran.r-project.org/web/packages/mediation/index.html
34. Ko, H., Hogan, J. W., & Mayer, K. H. (2003). Estimating causal treatment effects from longitudinal HIV natural history studies using marginal structural models. Biometrics, 59, 152-162. doi:10.1111/15410420.00018
35. Li, L., Evans, E., & Hser, Y. (2010). A marginal structural modeling approach to assess the cumulative effect of treatment on later drug use abstinence. Journal of Drug Issues, 40, 221-240. doi:10.1177/ 002204261004000112
36. Li, Y., Bienias, J. L., & Bennett, D. A. (2007). Confounding in the estimation of mediation effects. Computational Statistics and Data Analysis, 51, 3173-3186. doi:10.1016/j.csda.2006.10.016
37. Little, R. J. A., & Rubin, D. B. (2000). Causal effects in clinical and epidemiological studies via potential outcomes: Concepts and analytical approaches. Annual Review of Public Health, 21, 121-145. doi:10.1146/ annurev.publhealth.21.1.121
38. Luellen, J. K., Shadish, W. R., & Clark, M. H. (2005). Propensity scores: An introduction and experimental test. Evaluation Review, 29, 530-558. doi:10.1177/0193841X05275596
39. Lumley, T. (2010). Survey: Analysis of complex survey samples (R package Version 3.22-1) [Computer software]. Retrieved from http://cran.rproject.org/web/packages/survey/index.html
40. Lynch, K. G., Kerry, M., Gallop, R., & Ten Have, T. R. (2008). Causal mediation analyses for randomized trials. Health Services Outcomes Research Methodology, 8, 57-76. doi:10.1007/s10742-008-0028-9
41. MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. New York, NY: Erlbaum.
42. MacKinnon, D. P., Krull, J. L., & Lockwood, C. M. (2000). Equivalence of the mediation, confounding, and suppression effect. Prevention Science, 1, 173-181. doi:10.1023/A:1026595011371
43. MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002). A comparison of methods to test the significance of the intervening variable effect. Psychological Methods, 7, 83-104. doi: 10.1037/1082-989X.7.1.83
44. MacKinnon, D. P., Taborga, M. P., & Morgan-Lopez, A. A. (2002). Mediation designs for tobacco prevention research. Drug and Alcohol Dependence, 68, S69-S83. doi:10.1016/S0376-8716(02)00216-8
45. McCaffrey, D. F., Ridgeway, G., & Morral, A. R. (2004). Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychological Methods, 9, 403-425. doi:10.1037/ 1082-989X.9.4.403
46. Morgan, S. L., & Winship, C. (2007). Counterfactuals and causal inference: Methods and principles for social research. New York, NY: Cambridge University Press.
47. Mortimer, K. M., Heugebauer, R., van der Laan, M., & Tager, I. B. (2005). An application of model-fitting procedures for marginal structural models. American Journal of Epidemiology, 162, 382-388. doi:10.1093/aje/ kwi208
48. Muller, D., Judd, C. M., & Yzerbyt, V. Y. (2005). When moderation is mediated and mediation is moderated. Journal of Personality and Social Psychology, 89, 852-863. doi:10.1037/0022-3514.89.6.852
49. Pearl, J. (2001). Direct and indirect effects. In J. S. Breese & D. Koller (Eds.), Proceedings of the 17th Conference on Uncertainty in Artificial Intelligence (pp. 411-420). San Francisco, CA: Morgan Kaufman.
50. Pearl, J. (2010a). The causal foundations of structural equation modeling (Tech. Rep. No. R-370). Los Angeles: University of California, Los Angeles.
51. Pearl, J. (2010b). The mediation formula-A guide to the assessment of causal pathways in non-linear models (Tech. Rep. No. R-363). Los Angeles: University of California, Los Angeles.
52. Pearl, J. (2011). Invited commentary: Understanding bias amplification. American Journal of Epidemiology, 174, 1223-1227. doi:10.1093/aje/ kwr352
53. Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Assessing moderated mediation hypotheses: Theory, methods, and prescriptions. Multivariate Behavioral Research, 42, 185-227. doi:10.1080/ 00273170701341316
54. Robins, J. M. (1986). A new approach to causal inference in mortality studies with sustained exposure periods-Application to control of the healthy worker survival effect. Mathematical Modelling, 7, 1393-1512. doi:10.1016/0270-0255(86)90088-6
55. Robins, J. M. (1989). The analysis of randomized and non-randomized AIDS treatment trials using a new approach to causal inference in longitudinal studies. In L. Secrest, H. Freeman, & A. Mulley (Eds.), Health service research methodology: A focus on AIDS (pp. 113-159). Washington, DC: U.S. Public Health Service, National Center for Health Services Research.
56. Robins, J. M., & Greenland, S. (1992). Identifiability and exchangeability of direct and indirect effects. Epidemiology, 3, 143-155. doi:10.1097/ 00001648-199203000-00013
57. Robins, J. M., Hernan, M., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11, 550-560. doi:10.1097/00001648-200009000-00011
58. Robins, J. M., Rotnitzky, A., & Zhao, L. P. (1995). Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. Journal of the American Statistical Association, 90, 106-121.
59. Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41-55. doi:10.1093/biomet/70.1.41
60. Rosenbaum, P. R., & Rubin, D. B. (1984). Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association, 79, 516-524.
61. Rosenbaum, P. R., & Rubin, D. B. (1985). Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. American Statistician, 39, 33-38.
62. Rubin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688-701. doi:10.1037/h0037350
63. Rubin, D. B. (2004). Direct and indirect causal effects via potential outcomes. Scandinavian Journal of Statistics, 31, 161-170. doi:10.1111/ j.1467-9469.2004.02-123.x
64. Rubin, D. B. (2005). Causal inference using potential outcomes: Design, modeling, decisions. Journal of the American Statistical Association, 100, 322-331. doi:10.1198/016214504000001880
65. Schafer, J. L., & Kang, J. (2008). Average causal effects from nonrandomized studies: A practical guide and simulated example. Psychological Methods, 13, 279-313. doi:10.1037/a0014268
66. Sobel, M. E. (1982). Asymptotic intervals for indirect effects in structural equation models. In S. Leinhart (Ed.), Sociological methodology (Vol. 13, pp. 290-312). San Francisco, CA: Jossey-Bass. doi:10.2307/270723
67. Sobel, M. E. (2006). What do randomized studies of housing mobility demonstrate? Causal inference in the face of interference. Journal of the American Statistical Association, 101, 1398-1407. doi:10.1198/ 016214506000000636
68. Sobel, M. E. (2008). Identification of causal parameters in randomized studies with mediating variables. Journal of Educational and Behavioral Statistics, 33, 230-251. doi:10.3102/1076998607307239
69. Steiner, P. M., Cook, T. D., Shadish, W. R., & Clark, M. H. (2010). The importance of covariate selection in controlling for selection bias in observational studies. Psychological Methods, 15, 250-267. doi: 10.1037/a0018719
70. Ten Have, T. R., Joffe, M. M., Lynch, K. G., Brown, G., Maisto, S. A., & Beck, A. T. (2007). Causal mediation analysis with rank preserving models. Biometrics, 63, 926-934. doi:10.1111/j.1541-0420.2007.00766.x
71. van der Wal, W. M., Prins, M., Lumbreras, B., & Geskus, R. B. (2009). A simple G-computation algorithm to quantify the causal effect of a secondary illness on the progression of a chronic disease. Statistics in Medicine, 28, 2325-2337. doi:10.1002/sim.3629
72. VanderWeele, T. J. (2008a). Ignorability and stability assumptions in neighborhood effects research. Statistics in Medicine, 27, 1934-1943. doi:10.1002/sim.3139
73. VanderWeele, T. J. (2008b). Simple relations between principal stratification and direct and indirect effects. Statistics and Probability Letters, 78, 2957-2962. doi:10.1016/j.spl.2008.05.029
74. VanderWeele, T. J. (2009). Marginal structural models for the estimation of direct and indirect effects. Epidemiology, 20, 18-26. doi:10.1097/ EDE.0b013e31818f69ce
75. VanderWeele, T. J. (2010). Direct and indirect effects for neighborhoodbased clustered and longitudinal data. Sociological Methods and Research, 38, 515-544. doi:10.1177/0049124110366236
76. VanderWeele, T. J., Hawkley, L. C., Thisted, R. A., & Cacioppo, J. T. (2011). A marginal structural model analysis for loneliness: Implications for intervention trials and clinical practice. Journal of Consulting and Clinical Psychology, 79, 225-235. doi:10.1037/a0022610
77. VanderWeele, T. J., & Vansteelandt, S. (2009). Conceptual issues concerning mediation, interventions, and composition. Statistics and Its Interface, 2, 457-468.
78. Vinokur, A. D., & Schul, Y. (1997). Mastery and inoculation against setbacks as active ingredients in the JOBS intervention for the unem-ployed. Journal of Consulting and Clinical Psychology, 65, 867-877. doi:10.1037/0022-006X.65.5.867
79. West, S. G., Biesanz, J. C., & Pitts, S. C. (2000). Causal inference and generalization in field settings: Experimental and quasi-experimental designs. In H. T. J. Reis & C. Judd (Eds.), Handbook of research methods in social and personality psychology (pp. 40-84). New York, NY: Cambridge University Press.
80. Westreich, D., & Cole, S. R. (2010). Invited commentary: Positivity in practice. American Journal of Epidemiology, 171, 674-677. doi: 10.1093/aje/kwp436
81. Wimer, C., Sampson, R. J., & Laub, J. H. (2008). Estimating time-varying causes and outcomes with application to incarceration and crime. In P. Cohen (Ed.), Applied data analytic techniques for turning points research (pp. 37-59). New York, NY: Routledge.
82. Winship, C., & Morgan, S. L. (1999). The estimation of causal effects from observational data. Annual Review of Sociology, 25, 659-706. doi: 10.1146/annurev.soc.25.1.659