Austin, P.C., Stuart, E.A.: Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat. Med. 34(28), 3661–3679 (2015). https://doi.org/10.1002/sim.6607

Article  PubMed  PubMed Central  Google Scholar 

Baron, R.M., Kenny, D.A.: The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J. Pers. Soc. Psychol. 51(6), 1173–1182 (1986). https://doi.org/10.1037//0022-3514.51.6.1173

Article  CAS  PubMed  Google Scholar 

Berli, C., Inauen, J., Stadler, G., Scholz, U., Shrout, P.E.: Understanding between-person interventions with time-intensive longitudinal outcome data: longitudinal mediation analyses. Ann. Behav. Med. 55(5), 476–488 (2021). https://doi.org/10.1093/abm/kaaa066

Article  PubMed  Google Scholar 

Bind, M.A., Vanderweele, T.J., Coull, B.A., Schwartz, J.D.: Causal mediation analysis for longitudinal data with exogenous exposure. Biostatistics 17(1), 122–134 (2016). https://doi.org/10.1093/biostatistics/kxv029

Article  PubMed  Google Scholar 

Brown, R.L.: Assessing specific mediational effects in complex theoretical models. Struct. Equ. Modeling 4, 142–156 (1997)

Article  Google Scholar 

Cai, X., Coffman, D.L., Piper, M.E., Li, R.: Estimation and inference for the mediation effect in a time-varying mediation model. BMC Med. Res. Methodol. 22(1), 113 (2022). https://doi.org/10.1186/s12874-022-01585-x

Article  PubMed  PubMed Central  Google Scholar 

Carroll, R. J., Ruppert, D., Stefanski, L. A., Crainiceanu, C. M.: Measurement error in nonlinear models: a modern perspective. Chapman and Hall/CRC (2006)

Cashin, A.G., Lee, H., Lamb, S.E., Hopewell, S., Mansell, G., Williams, C.M., Kamper, S.J., Henschke, N., McAuley, J.H.: An overview of systematic reviews found suboptimal reporting and methodological limitations of mediation studies investigating causal mechanisms. J. Clin. Epidemiol. 111, 60–68 (2019). https://doi.org/10.1016/j.jclinepi.2019.03.005

Article  PubMed  Google Scholar 

Cashin, A.G., McAuley, J.H., Lamb, S.E., Hopewell, S., Kamper, S.J., Williams, C.M., Henschke, N., Lee, H.: Development of a guideline for reporting mediation analyses (AGReMA). BMC Med. Res. Methodol. 20(1), 19 (2020). https://doi.org/10.1186/s12874-020-0915-5

Article  PubMed  PubMed Central  Google Scholar 

Chakraborti, Y., Coffman, D.L., Piper, M.E.: Time-varying mediation of pharmacological smoking cessation treatments on smoking lapse via craving, cessation fatigue, and negative mood. Nicotine Tob. Res. (2022). https://doi.org/10.1093/ntr/ntac068

Article  PubMed  PubMed Central  Google Scholar 

Coffman, D.L., Zhong, W.: Assessing mediation using marginal structural models in the presence of confounding and moderation. Psychol. Methods 17(4), 642–664 (2012). https://doi.org/10.1037/a0029311

Article  PubMed  PubMed Central  Google Scholar 

Coffman, D. L.: Third variables: scientific meanings and modeling in non-randomized studies. In S. J. Henley (Ed.), Routledge international handbook of advanced quantitative methods in nursing research. routledge international (2015)

Cole, D.A., Preacher, K.J.: Manifest variable path analysis: potentially serious and misleading consequences due to uncorrected measurement error. Psychol. Methods 19(2), 300–315 (2014). https://doi.org/10.1037/a0033805

Article  PubMed  Google Scholar 

Cook, J.R., Stefanski, L.A.: Simulation-extrapolation estimation in parametric measurement error models. JASA 89(428), 1314–1328 (1994)

Article  Google Scholar 

Daniel, R.M., De Stavola, B.L., Cousens, S.N., Vansteelandt, S.: Causal mediation analysis with multiple mediators. Biometrics 71(1), 1–14 (2015). https://doi.org/10.1111/biom.12248

Article  CAS  PubMed  Google Scholar 

De Stavola, B.L., Daniel, R.M., Ploubidis, G.B., Micali, N.: Mediation analysis with intermediate confounding: Structural equation modeling viewed through the causal inference lens. Am. J. Epidemiol. 181(1), 64–80 (2015). https://doi.org/10.1093/aje/kwu239

Article  PubMed  Google Scholar 

Del Boca, F.K., Noll, J.A.: Truth or consequences: the validity of self-report data in health services research on addictions. Addiction 95(3), S347-360 (2000). https://doi.org/10.1080/09652140020004278

Article  PubMed  Google Scholar 

Didelez, V., Dawid, A. P., Geneletti, S.: Direct and indirect effects of sequential treatments. Proceedings of the 22nd conference on uncertainty in artificial intelligence (2006)

Fairchild, A.J., Cai, C., McDaniel, H., Shi, D., Gottschall, A., Masyn, K.E.: Evaluating a method to estimate mediation effects with discrete-time survival outcomes. Front. Psychol. 10, 740 (2019). https://doi.org/10.3389/fpsyg.2019.00740

Article  PubMed  PubMed Central  Google Scholar 

Fritz, M.S., Kenny, D.A., MacKinnon, D.P.: The combined effects of measurement error and omitting confounders in the single-mediator model. Multivariate Behav Res 51(5), 681–697 (2016). https://doi.org/10.1080/00273171.2016.1224154

Article  PubMed  PubMed Central  Google Scholar 

Fuller, W.A.: Measurement error models. John Wiley & Sons (1987)

Book  Google Scholar 

Gao, T., Albert, J.M.: Bayesian causal mediation analysis with multiple ordered mediators. Stat Modelling 19(6), 634–652 (2019). https://doi.org/10.1177/1471082x18798067

Article  PubMed  Google Scholar 

Gelfand, L.A., Mensinger, J.L., Tenhave, T.: Mediation analysis: a retrospective snapshot of practice and more recent directions. J. Gen. Psychol. 136(2), 153–176 (2009). https://doi.org/10.3200/GENP.136.2.153-178

Article  PubMed  PubMed Central  Google Scholar 

Gollob, H.F., Reichardt, C.S.: Taking account of time lags in causal models. Child Develop. 58(1), 80–92 (1987)

Article  CAS  PubMed  Google Scholar 

Gunzler, D., Tang, W., Lu, N., Wu, P., Tu, X.M.: A class of distribution-free models for longitudinal mediation analysis. Psychometrika 79(4), 543–568 (2014). https://doi.org/10.1007/s11336-013-9355-z

Article  CAS  PubMed  Google Scholar 

Hayes, A.F., Preacher, K.J.: Quantifying and testing indirect effects in simple mediation models when the constituent paths are nonlinear. Multivar. Behav. Res. 45(4), 627–660 (2010). https://doi.org/10.1080/00273171.2010.498290

Article  Google Scholar 

Hill, A.B.: The environment and disease: association or causation? Proceed. Royal Soc. Med. 58, 295–300 (1965)

Article  CAS  Google Scholar 

Holland, P.W.: Statistics and causal inference. J. Am. Stat. Assoc. 81(396), 945–960 (1986). https://doi.org/10.2307/2289069

Article  Google Scholar 

Holmberg, M.J., Andersen, L.W.: Collider bias. JAMA (2022). https://doi.org/10.1001/jama.2022.1820

Article  PubMed  Google Scholar 

Hong, G.: Causality in a social world: moderation, mediation and spill-over. Wiley (2015)

Book  Google Scholar 

Hong, G., Deutsch, J., Hill, H.D.: Ratio-of-mediator-probability weighting for causal mediation analysis in the presence of treatment-by-mediator interaction. J Educ Behav Stat 40(3), 307–340 (2015)

Article  Google Scholar 

Hong, G., Qin, X., Yang, F.: Weighting-based sensitivity analysis in causal mediation studies. J Educ Behav Stat 43(1), 32–56 (2018)

Article  Google Scholar 

Hong, G., Yang, F., Qin, X.: Posttreatment confounding in causal mediation studies: a cutting-edge problem and a novel solution via sensitivity analysis. Biometrics (2022). https://doi.org/10.1111/biom.13705

Article  PubMed  Google Scholar 

Imai, K., Yamamoto, T.: Identification and sensitivity analysis for multiple causal mechanisms: revisiting evidence from framing experiments. Polit. Anal. 21(2), 141–171 (2013). https://doi.org/10.1093/pan/mps040

Article  Google Scholar 

Imai, K., Keele, L., Tingley, D.: A general approach to causal mediation analysis. Psychol. Methods 15(4), 309–334 (2010). https://doi.org/10.1037/a0020761

Article  PubMed  Google Scholar 

Jerolon, A., Baglietto, L., Birmele, E., Alarcon, F., Perduca, V.: Causal mediation analysis in presence of multiple mediators uncausally related. Int. J. Biostat. 17(2), 191–221 (2020). https://doi.org/10.1515/ijb-2019-0088

Article  PubMed  Google Scholar 

Johnson, T., Fendrich, M.: Modeling sources of self-report bias in a survey of drug use epidemiology. Ann. Epidemiol. 15(5), 381–389 (2005). https://doi.org/10.1016/j.annepidem.2004.09.004

Article  PubMed  Google Scholar 

Judd, C.M., Kenny, D.A.: Estimating the effects of social intervention. Cambridge University Press, Cambridge (1981a)

Google Scholar 

Judd, C.M., Kenny, D.A.: Process analysis–estimating mediation in treatment evaluations. Eval. Rev. 5, 602–619 (1981b)

Article  Google Scholar 

Keele, L., Tingley, D., Yamamoto, T.: Identifying mechanisms behind policy interventions via causal mediation analysis. J. Policy Anal. Manage. 34(4), 937–963 (2015)

Article  Google Scholar 

Kraemer, H.C., Kiernan, M., Essex, M., Kupfer, D.J.: How and why criteria defining moderators and mediators differ between the Baron & Kenny and MacArthur approaches. Health Psychol. 27(2S), S101-108 (2008). https://doi.org/10.1037/0278-6133.27.2(Suppl.).S101

Article  PubMed  PubMed Central  Google Scholar 

Lange, T., Rasmussen, M., Thygesen, L.C.: Assessing natural direct and indirect effects through multiple pathways. Am. J. Epidemiol. 179(4), 513–518 (2013). https://doi.org/10.1093/aje/kwt270

Article  PubMed  Google Scholar 

le Cessie, S., Debeij, J., Rosendaal, F.R., Cannegieter, S.C., Vandenbroucke, J.P.: Quantification of bias in direct effects estimates due to different types of measurement error in the mediator. Epidemiology 23(4), 551–560 (2012). https://doi.org/10.1097/EDE.0b013e318254f5de

Article