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Causal Mediation for Survival Data: A Unifying Approach via GLM
Mediación causal para datos de supervivencia: un enfoque unificador a través de GLM
DOI:
https://doi.org/10.15446/rce.v45n1.94553Keywords:
Mediation, Causal inference, Survival Analysis, Generalized Linear Models (en)Mediación, Inferencia causal, Análisis de supervivencia, Modelos lineales generalizados (es)
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Mediation analysis has been receiving much attention from the scientific community in the last years, mainly due to its ability to disentangle causal pathways from exposures to outcomes. Particularly, causal mediation analysis for time-to-event outcomes has been widely discussed using accelerated failures times, Cox and Aalen models, with continuous or binary mediator. We derive general expressions for the Natural Direct Effect and Natural Indirect Effect for the time-to-event outcome when the mediator is modeled using generalized linear models, which includes existing procedures as particular cases. We also define a responsiveness measure to assess the variations in continuous exposures in the presence of ediation. We consider a community-based prospective cohort study that investigates the mediation of hepatitis B in the relationship between hepatitis C and liver cancer. We fit different models as well as distinct distributions and link functions associated to the mediator. We also notice that estimation of NDE and NIE using different models leads to non-contradictory conclusions despite their effect scales. The survival models provide a compelling framework that is appropriate to answer many research questions involving causal mediation analysis. The extensions through GLMs for the mediator may encompass
a broad field of medical research, allowing the often necessary control for confounding.
El análisis de mediación ha recibido mucha atención en los últimos años, principalmente debido a su capacidad para desenredar las vías causales. Particularmente, mediación causal para el tiempo hasta el evento se ha discutido ampliamente utilizando tiempos de falla acelerados, modelos de Cox y Aalen, con mediador continuo o binario. Derivamos expresiones generales para el efecto directo natural y el efecto indirecto natural para el el tiempo hasta el evento cuando el mediador se modela utilizando modelos lineales generalizados, que incluyen procedimientos existentes como casos particulares. Definimos una medida para evaluar variaciones en exposiciones continuas en presencia de mediación. Consideramos un estudio de cohorte prospectivo que investiga la mediación de la hepatitis B en la relación entre la hepatitis C y el cáncer de hígado. Encajamos iferentes modelos, así como distintas distribuciones y funciones de enlace. Todos los enfoques dan como resultado evaluaciones consistentes de los effectos considerando sus correspondientes escalas. Los modelos de supervivencia proporcionan un marco convincente apropiado para responder a muchas preguntas de
investigación que involucran mediación causal. Las extensiones a través de GLM para el mediador pueden abarcar un amplio campo de investigación médica, lo que permite el control necesario para los factores de confusión.
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References
Aalen, O. O., Borgan, O., and Gjessing, H. K. (2008). Survival and Event History Analysis: A Process Point of View. New York: Springer.
Aalen, O.O., Roysland, K. and Gran, J.M. (2012). Causality, mediation and time: a dynamic viewpoint. Journal of the Royal Statistical Society - Serie A, 175 (4), 831-861.
Aalen, O.O., Stensrud, M.J., Didelez, V., Daniel, R., Roysland, K., Strohmaier, S. (2020). Time-dependent mediators in survival analysis: Modeling direct and indirect effects with the additive hazards model. Biometrical Journal. 62, 532-549.
Albert, J.M., and Nelson S. (2011). Generalized Causal Mediation Analysis. Biometrics. 67, 1028-1038.
Baron, R. M., and 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.
Daniel, R.M., Stavola B.L., Cousens S.N., Vansteelandt S. (2015). Causal mediation analysis with multiple mediators. Biometrics. 71, 1-14.
Didelez, V. (2019). Defining causal mediation with a longitudinal mediator and a survival outcome. Lifetime Data Analysis. 25, 593-610.
Fasanelli, F., Giraudo, M.T., Ricceri, F., Valeri, L., Zugna, D (2019) Marginal Time-Dependent Causal Effects in Mediation Analysis With Survival Data. American Journal of Epidemiology, 188(5), 967-974.
Fulcher, I.R., Tchetgen Tchetgen, E.J., Williams, P.L. (2017) Mediation Analysis for Censored Survival Data Under an Accelerated Failure Time Model. Epidemiology, 28(5), 660-666.
Huang, Y-T, Jen, C-Lm Yang H-I, Lee M-H, Lu S-N, Iloeje U.H, Chen C-J. (2011) Lifetime risk and sex difference of hepatocellular carcinoma among patients with chronic hepatitis B and C. Journal of Clinical Oncology, 29(27), 3643-3650.
Huang, Y-T and Yang, H-I. (2017) Causal mediation analysis of survival outcomes with multiple mediators. Epidemiology, 28(3), 370-378.
Iacobucci, D. (2012) Mediation analysis and categorical variables: the final frontier. Journal of Consumer Psychology, 22, 582-594.
Imai K., Keele L. and Tingley D. (2010). A general approach to causal mediation analysis. Psychological Methods, 15, 309-334.
Kalbfleisch, J. D. and Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data. Hoboken, N.J.: Wiley, 2nd edition.
Lange. T. and Hansen, J. V. (2011). Direct and indirect effects in a survival context. Epidemiology, 22, 575-581.
Lange T, Vansteelandt S, Bekaert M. (2012). A simple unified approach for estimating natural direct and indirect effects. Am J Epidemiol., 176, 190-195.
Lin, S-H., Young J.G., Logan, R. and VanderWeele, T.J. (2017). Mediation analysis with a survival outcome with time-varying exposures, mediators, and confounders. Statistics in Medicine, 36, 4153-4166.
Loyes, T., Moerkerke B., De Smet, O. Byusse, A., Steen, J. Vansteelandt, S. (2013) Flexible mediation analysis in the presence of nonlinear relations: beyond the mediation formula. Multivariate Behavioral Research, 48(6), 871-894.
MacKinnon, D. P. and Dwyer, J. H. (1993). Estimating mediated effects in prevention studies. Evaluation Review, 17, 144-158.
MacKinnon, D. P., Warsi, G. and Dwyer, J. H. (1995). A simulation study of mediated effect measures. Multivariate Behavioral Research, 30, 41-62.
MacKinnon, D. (2008). Introduction to statistical mediation analysis. New York: Taylor and Francis.
Pearl, J. (1995) Causal diagrams for empirical research. Biometrika, 82, 669-688.
Pratschke, J., Haase, T., Comber, H, Sharp L., Cancela, M.C. and Johnson H. (2016) Mechanisms and mediation in survival analysis: towards an integrated analytical framework. BMC Medical Research Methodology, 16:27.
R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
Robins, J.M, Hernan, M.A., Brumback, B. (2000). Marginal structural models and causal inference in epidemiology. Epidemiology, 11, 550-560.
Rubin, D. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688-701.
Tchetgen Tchetgen EJ. (2013). Inverse odds ratio-weighted estimation for causal mediation analysis. Statistics in Medicine, 32, 4567-4580.
VanderWeele, T. J. (2011). Causal mediation analysis with survival data. Epidemiology, 22(4), 582-585.
VanderWeele, T. J. (2015). Explanation in Causal Inference: Methods for Mediation and Interaction. New York: Oxford University Press.
VanderWeele, T. J. (2016). Mediation Analysis: a Practitioner's Guide. Annual Review of Public Health, 37, 17-32.
VanderWeele, T. J. and Vansteelandt, S. (2009). Conceptual issues concerningmediation, interventions and composition. Statistics and its Interface, 2, 457-468.
VanderWeele, T. J. and Vansteelandt, S. (2010). Odds ratios for mediation analysis with a dichotomous outcome. American Journal of Epidemiology, 172, 1339-1348.
Vansteelandt S, Linder M, Vandenberghe S, Steen J, Madsen J. (2019). Mediation analysis of time-to-event endpoints accounting for repeatedly measured mediators subject to time-varying confounding. Statistics in Medicine, 38, 4828-4840.
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