A Viable Alternative When Propensity Scores Fail: Evaluation of Inverse Propensity Weighting and Sequential G-Estimation in a Two-Wave Mediation Model

References

• Austin, P. C. (2011). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research, 46(3), 399–424. doi:10.1080/00273171.2011.568786
   PubMed Web of Science ®Google Scholar
• Baron, R., & Kenny, D. (1986). The moderator-mediator variable distinction in social psychological research. Journal of Personality and Social Psychology, 51(6), 1173–1182. doi:10.1037/0022-3514.51.6.1173
   PubMed Web of Science ®Google Scholar
• Bradley, J. V. (1978). Robustness? British Journal of Mathematical and Statistical Psychology, 31(2), 144–152. doi:10.1111/j.2044-8317.1978.tb00581.x
   Web of Science ®Google Scholar
• Bullock, J. G., Green, D. P., & Ha, S. E. (2010). Yes, but what’s the mechanism? (don’t expect an easy answer). Journal of Personality and Social Psychology, 98(4), 550–558. doi:10.1037/a0018933
   PubMed Web of Science ®Google Scholar
• Coffman, D. L., & Zhong, W. (2012). Assessing mediation using marginal structural models in the presence of confounding and moderation. Psychological Methods, 17(4), 642–644. doi:10.1037/a0029311
   PubMed Web of Science ®Google Scholar
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.
   Google Scholar
• Cole, D. A., & Maxwell, S. E. (2003). Testing mediational models with longitudinal data: Questions and tips in the use of structural equation modeling. Journal of Abnormal Psychology, 112(4), 558–577. doi:10.1037/0021-843X.112.4.558
   PubMed Web of Science ®Google Scholar
• Cole, S. R., & Hernán, M. A. (2008). Constructing inverse probability weights for marginal structural models. American Journal of Epidemiology, 168(6), 656–664. doi:10.1093/aje/kwn164
   PubMed Web of Science ®Google Scholar
• Collins, L. M., & Graham, J. W. (2002). The effect of the timing and spacing of observations in longitudinal studies of tobacco and other drug use: Temporal design considerations. Drug and Alcohol Dependence, 68(1), 85–96. doi:10.1016/S0376-8716(02)00217-X
   Google Scholar
• De Stavola, B. L., Daniel, R. M., Ploubidis, G. B., & Micali, N. (2015). Mediation analysis with intermediate confounding: Structural equation modeling viewed through the causal inference lens. American Journal of Epidemiology, 181(1), 64–80. doi:10.1093/aje/kwu239
   PubMed Web of Science ®Google Scholar
• Dukes, O., & Vansteelandt, S. (2018). A note on G-estimation of causal risk ratios. American Journal of Epidemiology, 187(5), 1079–1084. doi:10.1093/aje/kwx347
   PubMed Web of Science ®Google Scholar
• Goetgeluk, S., Vansteelandt, S., & Goetghebeur, E. (2008). Estimation of controlled direct effects. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 70(5), 1049–1066. doi:10.1111/j.1467-9868.2008.00673.x
   Web of Science ®Google Scholar
• Goldberg, L., Elliot, D., Clarke, G. N., MacKinnon, D. P., Moe, E., Zoref, L., … Lapin, A. (1996). Effects of a multidimensional anabolic steroid prevention intervention: The Adolescents Training and Learning to Avoid Steroids (ATLAS) Program. JAMA, 276(19), 1555–1562.[Mismatch] doi:10.1001/jama.1996.03540190027025
   PubMed Web of Science ®Google Scholar
• Gollob, H. F., & Reichardt, C. S. (1991). Interpreting and estimating indirect effects assuming time lags really matter. Best methods for the analysis of change: Recent advances, unanswered questions, future directions. Washington, DC: American Psychological Association. doi:10.1037/10099-015
   Google Scholar
• Hawk, L. W., Fosco, W. D., Colder, C. R., Waxmonsky, J. G., Pelham, W. E., Jr., & Rosch, K. S. (2018). How do stimulant treatments for ADHD work? Evidence for mediation for improved cognition. Journal of Child Psychology and Psychiatry, 59(12), 1271–1281. doi:10.1111/jcpp.12917
   PubMed Web of Science ®Google Scholar
• Hernán, M. A., & Robins, J. M. (2006). Estimating causal effects from epidemiological data. Journal of Epidemiology and Community Health, 60(7), 578–586. doi:10.1 1 36/jech. 2004.02
   PubMed Web of Science ®Google Scholar
• Hill, J., Weiss, C., & Zhai, F. (2011). Challenges with propensity score strategies in a high-dimensional setting with a potential alternative. Multivariate Behavioral Research, 46(3), 477–513. doi:10.1080/00273171.2011.570161
   PubMed Web of Science ®Google Scholar
• Holland, P. W. (1988). Causal inference, path analysis, and recursive structural equations models. Sociological Methodology, 18(1), 449–484. doi:10.1002/j.2330-8516.1988.tb00270.x
   Google Scholar
• Imai, K., Jo, B., & Stuart, E. A. (2011). Commentary: Using potential outcomes to understand causal mediation analysis. Multivariate Behavioral Research, 46, 842–854. doi:10.1080/00273171.2011.606743
   PubMed Web of Science ®Google Scholar
• Imai, K., Keele, L., & Tingley, D. (2010). A general approach to causal mediation analysis. Psychological Methods, 15(4), 309–326. doi:10.1037/a0020761
   PubMed Web of Science ®Google Scholar
• Imai, K., Keele, L., & Yamamoto, T. (2010). Identification, inference and sensitivity analysis for causal mediation effects. Statistical Science, 25(1), 51–71. doi:10.1214/10-STS321
   Web of Science ®Google Scholar
• Imbens, G. W., & Rubin, D. B. (2015). Causal inference in statistics, social, and biomedical sciences. Cambridge: Cambridge University Press. doi:10.1017/CBO9781139025751
   Google Scholar
• Jo, B., Stuart, E. A., MacKinnon, D. P., & Vinokur, A. D. (2011). The use of propensity scores in mediation analysis. Multivariate Behavioral Research, 46(3), 425–452. doi:10.1080/00273171.2011.576624
   PubMed Web of Science ®Google Scholar
• Kang, J. D. Y., & Schafer, J. L. (2007). Demystifying double robustness: A comparison of alternative strategies for estimating a population mean from incomplete data. Statistical Science, 22(4), 523–539. doi:10.1214/07-STS227
   Web of Science ®Google Scholar
• Kisbu-Sakarya, Y., MacKinnon, D. P., Valente, M. J., & Cetinkaya, E. (2019). Mediation analysis in the presence of confounding variables: Alternative approaches based on modern causal models. Unpublished manuscript.
   Google Scholar
• Landau, S., Emsley, R., & Dunn, G. (2018). Beyond total treatment effects in randomised controlled trials: Baseline measurement of intermediate outcomes needed to reduce confounding in mediation investigations. Clinical. Trials, 15(3), 247–256. doi:10.1177/1740774518760300
   PubMed Web of Science ®Google Scholar
• Lazarsfeld, P. F. (1955). Interpretation of statistical relations as a research operation. In P. F. Lazarsfeld & M. Rosenberg (Eds.), The language of social research: A reader in the methodology of social research (pp. 115–125). Glencoe, IL: Free Press.
   Google Scholar
• Lepage, B., Lamy, S., Dedieu, D., Savy, N., & Lang, T. (2015). Estimating the causal effect of an exposure on change from baseline using directed acyclic graphs and path analysis. Epidemiology, 26(1), 122–129. doi:10.1097/EDE.0000000000000192
   PubMed Web of Science ®Google Scholar
• MacKinnon, D. P. (2008). (2019 2nd Edition). Introduction to statistical mediation analysis. New York, NY: Taylor & Francis Group/Lawrence Erlbaum Associates. doi:10.4324/9780203809556
   Google Scholar
• MacKinnon, D. P., Goldberg, L., Clarke, G. N., Elliot, D. L., Cheong, J., Lapin, A., … Krull, J. L. (2001). Mediating mechanisms in a program to reduce intentions to use anabolic steroids and improve exercise self-efficacy and dietary behavior. Prevention Science, 2(1), 15–28. doi:0.1023/A:1010082828000
   PubMedGoogle Scholar
• MacKinnon, D. P., & Pirlott, A. G. (2015). Statistical approaches for enhancing causal interpretation of the M to Y relation in mediation analysis. Personality and Social Psychology Review, 19(1), 30–43. doi:10.1177/1088868314542878
   PubMed Web of Science ®Google Scholar
• Maxwell, S. E., & Cole, D. A. (2007). Bias in cross-sectional analyses of longitudinal mediation. Psychological Methods, 12(1), 23–44. doi:10.1037/1082-989X.12.1.23
   PubMed Web of Science ®Google Scholar
• Mayer, A., Thoemmes, F., Rose, N., Steyer, R., & West, S. G. (2014). Theory and analysis of total, direct, and indirect causal effects. Multivariate Behavioral Research, 49(5), 425–442. doi:10.1080/00273171.2014.931797
   PubMed Web of Science ®Google Scholar
• Moerkerke, B., Loeys, T., & Vansteelandt, S. (2015). Structural equation modeling versus marginal structural modeling for assessing mediation in the presence of posttreatment confounding. Psychological Methods, 20(2), 204. doi:10.1037/a0036368
   PubMed Web of Science ®Google Scholar
• Naimi, A. I., Moodie, E. E. M., Auger, N., & Kaufman, J. S. (2014). Constructing inverse probability weights for continuous exposures. Epidemiology, 25(2), 292–299. doi:10.1097/EDE.0000000000000053
   PubMed Web of Science ®Google Scholar
• Pearl, J. (2001, August). Direct and indirect effects. In J. Breese & D. Koller (Eds.), Proceedings of the seventeenth conference on uncertainty in artificial intelligence (pp. 411–420). San Francisco, CA: Morgan Kaufmann Publishers Inc.
   Google Scholar
• Pearl, J. (2009). Causality. New York, NY: Cambridge University Press. doi:10.1017/CBO9780511803161
   Google Scholar
• Reichardt, C. S. (2011). Commentary: Are three waves of data sufficient for assessing mediation?. Multivariate Behavioral Research, 46(5), 842–851. doi:10.1080/00273171.2011.606740
   PubMed Web of Science ®Google Scholar
• Reshetnyak, E., Cham, H., & Hughes, J. H. (2016). Using marginal structural modeling for grade retention effects. Multivariate Behavioral Research, 51(6), 871. 876. doi:10.1080/00273171.2016.1200454
   PubMed Web of Science ®Google Scholar
• Robins, J. M. (2000). Marginal structural models versus structural nested models as tools for causal inference. In M. Halloran & D. Berry (Eds.), Statistical models in epidemiology: The environment and clinical trials (pp. 95–134). New York: Springer-Verlag. doi:10.1007/978-1-4612-1284-3_2
   Google Scholar
• Robins, J. M., & Greenland, S. (1992). Identifiability and exchangeability for direct and indirect effects. Epidemiology, 3(2), 143–155. doi:10.1097/00001648-199203000-00013
   PubMed Web of Science ®Google Scholar
• Robins, J. M., Hernán, M. A., & Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11(5), 550–560. doi:10.1097/00001648-2000090000-00011
   PubMed Web of Science ®Google Scholar
• Robins, J. M., Sued, M., Lei-Gomez, Q., & Rotnitzky, A. (2007). Comment: Performance of double-robust estimators when “inverse probability” weights are highly variable. Statistical Science, 22 (4), 544–559. doi:10.1214/07-STS227REJ
   Google Scholar
• Rosenbaum, P. R. (2002). Observational studies (2nd ed.). New York, NY: doi:10.1007/978-1-4757-3692-2 Springer-Verlag
   Google Scholar
• Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 7(1), 41–55. doi:10.1093/biomet/70.1.41
   Web of Science ®Google Scholar
• Rubin, D. (1997). Estimating causal effects from large data set using propensity scores. Annals of Internal Medicine, 127 (8_Part_2), 757–763. doi:10.7326/0003-4819-127-8_Part_2-199710151-00064
   PubMed Web of Science ®Google Scholar
• Schafer, J. L., & Kang, J. (2008). Average causal effects from nonrandomized studies: A practical guide and simulated example. Psychological Methods, 13(4), 279–313. doi:10.1037/a0014268
   PubMed Web of Science ®Google Scholar
• 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(3), 250–267. doi:10.1037/a0018719
   PubMed Web of Science ®Google Scholar
• Steiner, P. M., Park, S., & Kim, Y. (2016). Identifying causal estimands for time-varying treatments measured with time-varying (age or grade-based) instruments. Multivariate Behavioral Research, 51(6), 865. 870. doi:10.1080/00273171.2016.1205470
   PubMed Web of Science ®Google Scholar
• Tingley, D., Yamamoto, T., Hirose, K., Keele, L., & Imai, K. (2014). mediation: R package for causal mediation analysis. Journal of Statistical Software, 59(5), 1–38. doi:10.18637/jss.v059.i05
   PubMed Web of Science ®Google Scholar
• Valente, M. J., & MacKinnon, D. P. (2017). Comparing models of change to estimate the mediated effect in the pretest–posttest control group design. Structural Equation Modeling: A Multidisciplinary Journal, 24(3), 1–23. doi:10.1080/00273171.2016.1205470
   Web of Science ®Google Scholar
• Vandecandelaere, M., Vansteelandt, S., De Fraine, B., & Van Damme, J. (2016). Time-varying treatments in observational studies: Marginal structural models of the effects of early grade retention on math achievement. Multivariate Behavioral Research, 51(6), 843–864, 1. doi:10.1080/00273171.2016.1155146
   Google Scholar
• VanderWeele, T. J. (2009). Marginal structural models for the estimation of direct and indirect effects. Epidemiology, 20(1), 18–26. doi:10.1097/EDE.0b013e31818f69ce
   PubMed Web of Science ®Google Scholar
• VanderWeele, T. J. (2015). Explanation in causal inference: Methods for mediation and interaction. New York, NY: Oxford University Press. doi:10.3102/1076998617698112
   Google Scholar
• VanderWeele, T. J., & Vansteelandt, S. (2009). Conceptual issues concerning mediation, interventions and composition. Statistics and Its Interface, 2, 457–468. doi:10.4310/SII.2009.v2.n4.a7
   Web of Science ®Google Scholar
• Vansteelandt, S. (2009). Estimating direct effects in cohort and case-control studies. Epidemiology, 20(6), 851–860. doi:10.1097/EDE.0b013e3181b6f4c9
   PubMed Web of Science ®Google Scholar
• Vansteelandt, S. (2010). Estimation of controlled direct effects on a dichotomous outcome using logistic structural direct effect models. Biometrika, 97(4), 921–934. doi:10.1093/biomet/asq053
   Web of Science ®Google Scholar
• Vansteelandt, S. (2012). Estimation of direct and indirect effects. In C. Berzuini, P. Dawid, & L. Bernardinelli (Eds.), Causality: Statistical perspectives and applications (pp. 126–150). Hoboken, NJ: Wiley. doi:10.1002/9781119945710.ch11
   Google Scholar
• Vansteelandt, S., & Joffe, M. (2014). Structural Nested Models and G-estimation: The Partially Realized Promise. Statistical Science, 29(4), 707–731. doi:10.1214/14-STS493
   Web of Science ®Google Scholar
• Williamson, E. J., Forbes, A., & White, I. R. (2014). Variance reduction in randomized trials by inverse probability weighting using the propensity score. Statistics in Medicine, 33(5), 721–737. doi:10.1002/sim.5991
   PubMed Web of Science ®Google Scholar