594
Views
0
CrossRef citations to date
0
Altmetric
Research Articles

A Causal Approach to Functional Mediation Analysis with Application to a Smoking Cessation Intervention

ORCID Icon, , , ORCID Icon, & ORCID Icon

References

  • Adam, E. K., Quinn, M. E., Tavernier, R., McQuillan, M. T., Dahlke, K. A., & Gilbert, K. E. (2017). Diurnal cortisol slopes and mental and physical health outcomes: A systematic review and meta-analysis. Psychoneuroendocrinology, 83, 25–41. https://doi.org/10.1016/j.psyneuen.2017.05.018
  • Albert, J. M., Li, Y., Sun, J., Woyczynski, W. A., & Nelson, S. (2019). Continuous-time causal mediation analysis. Statistics in Medicine, 38(22), 4334–4347. https://doi.org/10.1002/sim.8300
  • Baker, T. B., Piper, M. E., Stein, J. H., Smith, S. S., Bolt, D. M., Fraser, D. L., & Fiore, M. C. (2016). Effects of nicotine patch vs. varenicline vs. combination nicotine replacement therapy on smoking cessation at 26 weeks: A randomized clinical trial. JAMA, 315(4), 371–379. https://doi.org/10.1001/jama.2015.19284
  • Bind, M. A., Vanderweele, T. J., Coull, B. A., & Schwartz, J. D. (2016). Causal mediation analysis for longitudinal data with exogenous exposure. Biostatistics (Oxford, England), 17(1), 122–134. https://doi.org/10.1093/biostatistics/kxv029
  • Cai, X., Coffman, D. L., Piper, M. E., & Li, R. (2022). Estimation and inference for the mediation effect in a time-varying mediation model. BMC Medical Research Methodology, 22(1), 113. https://doi.org/10.1186/s12874-022-01585-x
  • Canty, A., & Ripley, B. D. (2020). boot: Bootstrap R (S-Plus) functions [Computer software manual]. (R package version 1.3-25).
  • Chen, Y.-H., Mukherjee, B., Ferguson, K. K., Meeker, J. D., & VanderWeele, T. J. (2016). Mediation formula for a binary outcome and a time-varying exposure and mediator, accounting for possible exposure-mediator interaction. American Journal of Epidemiology, 184(2), 157–159. https://doi.org/10.1093/aje/kww045
  • Ciarleglio, A., Petkova, E., & Harel, O. (2020). Multiple imputation in functional regression with applications to eeg data in a depression study. arXiv:2001.08175
  • Daniel, R. M., de Stavola, B. L., Cousens, S. N., & Vansteelandt, S. (2015). Causal mediation analysis with multiple mediators. Biometrics, 71(1), 1–14. https://doi.org/10.1111/biom.12248
  • Davison, A. C., & Hinkley, D. V. (1997). Bootstrap methods and their applications. Cambridge University Press.
  • de Stavola, B., Herle, M., & Pickles, A. (2022). Framing causal questions in life course epidemiology. Annual Review of Statistics and Its Application, 9(1), 223–248. https://doi.org/10.1146/annurev-statistics-040120-024748
  • Derkach, A., Pfeiffer, R. M., Chen, T., & Sampson, J. N. (2019). High dimensional mediation analysis with latent variables. Biometrics, 75(3), 745–756. https://doi.org/10.1111/biom.13053
  • Doretti, M., Raggi, M., & Stanghellini, E. (2021). Exact parametric causal mediation analysis for a binary outcome with a binary mediator. Statistical Methods and Applications, 31, 87–108. https://doi.org/10.1007/s10260-021-00562-w
  • Dziak, J. J., Coffman, D. L., Reimherr, M., Petrovich, J., Li, R., Shiffman, S., & Shiyko, M. P. (2019). Scalar-on-function regression for predicting distal outcomes from intensively gathered longitudinal data: Interpretability for applied scientists. Statistics Surveys, 13, 150–180. https://doi.org/10.1214/19-SS126
  • Dziak, J. J., Coffman, D. L., Li, R., Litson, K., & Chakraborti, Y. (2021). tvem R package (time-varying effect models) [Computer software manual]. https://cran.r-project.org/web/packages/tvem/tvem.pdf
  • Eilers, P. H. C., & Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11(2), 89–121. https://doi.org/10.1214/ss/1038425655
  • Fan, J., & Zhang, J.-T. (2000). Two-step estimation of functional linear models with applications to longitudinal data. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 62(2), 303–322. https://doi.org/10.1111/1467-9868.00233
  • Feingold, A., MacKinnon, D. P., & Capaldi, D. M. (2019). Mediation analysis with binary outcomes: Direct and indirect effects of proalcohol influences on alcohol use disorders. Addictive Behaviors, 94, 26–35. https://doi.org/10.1016/j.addbeh.2018.12.018
  • Fritz, M. S. (2014). An exponential decay model for mediation. Prevention Science : The Official Journal of the Society for Prevention Research, 15(5), 611–622. https://doi.org/10.1007/s11121-013-0390-x
  • Fritz, M. S., Kenny, D. A., & MacKinnon, D. P. (2016). The combined effects of measurement error and omitting confounders in the single-mediator model. Multivariate Behavioral Research, 51(5), 681–697. https://doi.org/10.1080/00273171.2016.1224154
  • Gao, Y., & Kowal, D. R. (2022). Bayesian adaptive and interpretable functional regression for exposure profiles. arXiv:1903.04697. https://arxiv.org/abs/2203.00784
  • Geldhof, G. J., Anthony, K. P., Selig, J. P., & Mendez-Luck, C. A. (2018). Accommodating binary and count variables in mediation. International Journal of Behavioral Development, 42(2), 300–308. https://doi.org/10.1177/0165025417727876
  • Gelman, A., & Vehtari, A. (2021). What are the most important statistical ideas of the past 50 years? Journal of the American Statistical Association, 116(536), 2087–2097. https://doi.org/10.1080/01621459.2021.1938081
  • Goldsmith, J., Bobb, J., Crainiceanu, C. M., Caffo, B., & Reich, D. (2011). Penalized functional regression. Journal of Computational and Graphical Statistics : A Joint Publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America, 20(4), 830–851. https://doi.org/10.1198/jcgs.2010.10007
  • Goldsmith, J., Scheipl, F., Huang, L., Wrobel, J., Di, C., Gellar, J., Harezlak, J., McLean, M. W., Swihart, B., Xiao, L., Crainiceanu, C., Reiss, P. T., Chen, Y., Greven, S., Huo, S., Kundu, M. G., Park, S. Y., et al. (2020). refund R package (regression with functional data) [Computer software manual]. https://cran.r-project.org/web/packages/refund/refund.pdf
  • Green, M. J., & Popham, F. (2017). Life course models: Improving interpretation by consideration of total effects. International Journal of Epidemiology, 46(3), 1057–1062. https://doi.org/10.1093/ije/dyw329
  • Hastie, T., & Tibshirani, R. (1993). Varying-coefficient models. Journal of the Royal Statistical Society, B, 55, 757–796.
  • Hernán, M. A., & Robins, J. M. (2020). Causal inference: What if. Chapman & Hall/CRC.
  • Huang, J., & Yuan, Y. (2017). Bayesian dynamic mediation analysis. Psychological Methods, 22(4), 667–686.
  • Imai, K., Keele, L., & Yamamoto, T. (2010). Identification, inference and sensitivity analysis for causal mediation effects. Statistical Science, 25(1), 51–71. https://doi.org/10.1214/10-STS321
  • Kim, K., Sentürk, D., & Li, R. (2011). Recent history of functional linear models for sparse longitudinal data. Journal of Statistical Planning and Inference, 141(4), 1554–1566. https://doi.org/10.1016/j.jspi.2010.11.003
  • Le Foll, B., Piper, M. E., Fowler, C. D., Tonstad, S., Bierut, L., Lu, L., Jha, P., & Hall, W. D. (2022). Tobacco and nicotine use. Nature Reviews Disease Primers, 8(19).
  • Lee, K. J., Roberts, G., Doyle, L. W., Anderson, P. J., & Carlin, J. B. (2016). Multiple imputation for missing data in a longitudinal cohort study: A tutorial based on a detailed case study involving imputation of missing outcome data. International Journal of Social Research Methodology, 19(5), 575–591. https://doi.org/10.1080/13645579.2015.1126486
  • Liang, K.-Y., & Zeger, S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika, 73(1), 13–22. https://doi.org/10.1093/biomet/73.1.13
  • Lindquist, M. A. (2012). Functional causal mediation analysis with an application to brain connectivity. Journal of the American Statistical Association, 107(500), 1297–1309. https://doi.org/10.1080/01621459.2012.695640
  • Lindquist, M. A., & McKeague, I. W. (2009). Logistic regression with Brownian-like predictors. Journal of the American Statistical Association, 104(488), 1575–1585. https://doi.org/10.1198/jasa.2009.tm08496
  • Liu, K., Saarela, O., Feldman, B. M., & Pullenayegum, E. (2020). Estimation of causal effects with repeatedly measured outcomes in a bayesian framework. Statistical Methods in Medical Research, 29(9), 2507–2519.
  • Liu, X. (2014). Joint modeling of longitudinal and survival data: New models, computing techniques and applications [Doctoral dissertation, Penn State University Libraries]. https://etda.libraries.psu.edu/catalog/23528
  • Loh, W. W., Moerkerke, B., Loeys, T., Poppe, L., Crombez, G., & Vansteelandt, S. (2020). Estimation of controlled direct effects in longitudinal mediation analyses with latent variables in randomized studies. Multivariate Behavioral Research, 55(5), 763–785. https://doi.org/10.1080/00273171.2019.1681251
  • Lok, J. J., & Bosch, R. J. (2020). Causal organic indirect and direct effects: Closer to Baron and Kenny, with a product method for binary mediators. arXiv:1903.04697.
  • MacKinnon, D. P. (2008). Introduction to statistical mediation analysis. Routledge.
  • Magidson, J. F., Blashill, A. J., Safren, S., A., & Wagner, G. J. (2015). Depressive symptoms, lifestyle structure, and ART adherence among HIV-infected individuals: A longitudinal mediation analysis. AIDS and Behavior, 19(1), 34–40. https://doi.org/10.1007/s10461-014-0802-3
  • Maxwell, S. E., Cole, D. A., & Mitchell, M. A. (2011). Bias in cross-sectional analyses of longitudinal mediation: Partial and complete mediation under an autoregressive model. Multivariate Behavioral Research, 46(5), 816–841. https://doi.org/10.1080/00273171.2011.606716
  • Miočević, M., Gonzalez, O., Valente, M. J., & MacKinnon, D. P. (2018). A tutorial in bayesian potential outcomes mediation analysis. Structural Equation Modeling: A Multidisciplinary Journal, 25(1), 121–136. https://doi.org/10.1080/10705511.2017.1342541
  • Moreno-Betancur, M., & Carlin, J. B. (2018). Understanding interventional effects: A more natural approach to mediation analysis? Epidemiology (Cambridge, Mass.), 29(5), 614–617. https://doi.org/10.1097/EDE.0000000000000866
  • Nguyen, T. Q., Schmid, I., & Stuart, E. A. (2021). Clarifying causal mediation analysis for the applied researcher: Defining effects based on what we want to learn. Psychological Methods, 26(2), 255–271. https://doi.org/10.1037/met0000299
  • Park, S., Steiner, P. M., & Kaplan, D. (2018). Identification and sensitivity analysis for average causal mediation effects with time-varying treatments and mediators: Investigating the underlying mechanisms of kindergarten retention policy. Psychometrika, 83(2), 298–320. https://doi.org/10.1007/s11336-018-9606-0
  • Pinto Pereira, S. M., De Stavola, B. L., Rogers, N. T., Hardy, R., Cooper, R., & Power, C. (2020). Adult obesity and mid-life physical functioning in two british birth cohorts: Investigating the mediating role of physical inactivity. International Journal of Epidemiology, 49(3), 845–856. https://doi.org/10.1093/ije/dyaa014
  • Qian, T., Yoo, H., Klasnja, P., Almirall, D., & Murphy, S. A. (2019). Estimating time-varying causal excursion effect in mobile health with binary outcomes. arXiv:1906.00528
  • R Core Team. (2020). R: A language and environment for statistical computing [Computer software manual]. https://www.R-project.org/
  • Ramsay, J. O., & Silverman, B. W. (2005). Functional data analysis (2nd. ed.). Springer.
  • Rijnhart, J. J. M., Twisk, J. W., Chinapaw, M. J. M., de Boer, M. R., & Heymans, M. W. (2017). Comparison of methods for the analysis of relatively simple mediation models. Contemporary Clinical Trials Communications, 7, 130–135. https://doi.org/10.1016/j.conctc.2017.06.005
  • Rijnhart, J. J. M., Twisk, J. W. R., Eekhout, I., & Heymans, M. W. (2019). Comparison of logistic-regression based methods for simple mediation analysis with a dichotomous outcome variable. BMC Medical Research Methodology, 19(19). https://doi.org/10.1186/s12874-018-0654-z
  • Rijnhart, J. J. M., Valente, M. J., MacKinnon, D. P., Twisk, J. W. R., & Heymans, M. W. (2021). The use of traditional and causal estimators for mediation models with a binary outcome and exposure-mediator interaction. Structural Equation Modeling: A Multidisciplinary Journal, 28(3), 345–355. https://doi.org/10.1080/10705511.2020.1811709
  • Robins, J. M. (2003). Semantics of causal DAG models and the identification of direct and indirect effects. In P. Green, N. Hjort, & S. Richardson (eds.), Highly structured stochastic systems (pp. 70–81).
  • Rubin, D. (1980). Randomization analysis of experimental data: The Fisher randomization test (comment). Journal of the American Statistical Association, 75(371), 591–593. https://doi.org/10.2307/2287653
  • Schafer, J. L., & Graham, J. W. (2002). Missing data: Our view of the state of the art. Psychological Methods, 7(2), 147–177.
  • Shiffman, S., Stone, A. A., & Hufford, M. R. (2008). Ecological momentary assessment. Annual Review of Clinical Psychology, 4, 1–32. https://doi.org/10.1146/annurev.clinpsy.3.022806.091415
  • Sullivan, A. J., Gunzler, D. D., Morris, N., & VanderWeele, T. J. (2021). Longitudinal mediation analysis with latent growth curves. arXiv:2103.05765
  • Tan, X., Shiyko, M. P., Li, R., Li, Y., & Dierker, L. (2012). A time-varying effect model for intensive longitudinal data. Psychological Methods, 17(1), 61–77.
  • Vafaie, N., & Kober, H. (2022). Association of drug cues and craving with drug use and relapse: A systematic review and meta-analysis. JAMA Psychiatry, 79(7), 641. https://doi.org/10.1001/jamapsychiatry.2022.1240
  • VanderWeele, T. J. (2015). Explanation in causal inference: Methods for mediation and interaction. Oxford University Press.
  • VanderWeele, T. J., & Tchetgen Tchetgen, E. J. (2017). Mediation analysis with time varying exposures and mediators. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 79(3), 917–938. https://doi.org/10.1111/rssb.12194
  • VanderWeele, T. J., & Vansteelandt, S. (2009). Conceptual issues concerning mediation, interventions, and composition. Statistics and Its Interface, 2(4), 457–468. https://doi.org/10.4310/SII.2009.v2.n4.a7
  • VanderWeele, T. J., & Vansteelandt, S. (2010). Odds ratios for mediation analysis for a dichotomous outcome. American Journal of Epidemiology, 172(12), 1339–1348. https://doi.org/10.1093/aje/kwq332
  • Vansteelandt, S., & Daniel, R. M. (2017). Interventional effects for mediation analysis with multiple mediators. Epidemiology (Cambridge, Mass.), 28(2), 258–265. https://doi.org/10.1097/EDE.0000000000000596
  • Vansteelandt, S., Linder, M., Vandenberghe, S., Steen, J., & Madsen, J. (2019). Mediation of time-to-event endpoints accounting for repeatedly measured mediators subject to time-varying confounding. Statistics in Medicine, 38(24), 4828–4840.
  • Wang, W., Nelson, S., & Albert, J. M. (2013). Estimation of causal mediation effects for a dichotomous outcome in multiple-mediator models using the mediation formula. Statistics in Medicine, 32(24), 4211–4228. https://doi.org/10.1002/sim.5830
  • Wood, S. N. (2003). Thin plate regression splines. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 65(1), 95–114. https://doi.org/10.1111/1467-9868.00374
  • Wood, S. N. (2017). Generalized additive models: An introduction with R (2nd Ed.). Chapman and Hall/CRC.
  • Wood, S. N. (2021). mgcv R package (Mixed GAM Computation Vehicle with Automatic Smoothness Estimation) [Computer software manual]. https://cran.r-project.org/web/packages/mgcv/index.html
  • Zeng, S., Rosenbaum, S., Archie, E., Alberts, S., & Li, F. (2021). Causal mediation analysis for sparse and irregular longitudinal data. arXiv:2007.01796
  • Zhao, Y., Lindquist, M. A., & Caffo, B. S. (2020). Sparse principal component based high-dimensional mediation analysis. Computational Statistics & Data Analysis, 142, 106835. https://doi.org/10.1016/j.csda.2019.106835
  • Zhao, Y., Luo, X., Lindquist, M., & Caffo, B. (2018). Functional mediation analysis with an application to functional magnetic resonance imaging data.
  • Zheng, W., & van der Laan, M. (2017). Longitudinal mediation analysis with time-varying mediators and exposures, with application to survival outcomes. Journal of Causal Inference, 5(2), 20160006.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.