Published

2022-01-01

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.v45n1.94553

Keywords:

Mediation, Causal inference, Survival Analysis, Generalized Linear Models (en)
Mediación, Inferencia causal, Análisis de supervivencia, Modelos lineales generalizados (es)

Downloads

Authors

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.

References

Aalen, O. O., Borgan, O., and Gjessing, H. K. (2008). Survival and Event History Analysis: A Process Point of View. New York: Springer. DOI: https://doi.org/10.1007/978-0-387-68560-1

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. DOI: https://doi.org/10.1111/j.1467-985X.2011.01030.x

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. DOI: https://doi.org/10.1002/bimj.201800263

Albert, J.M., and Nelson S. (2011). Generalized Causal Mediation Analysis. Biometrics. 67, 1028-1038. DOI: https://doi.org/10.1111/j.1541-0420.2010.01547.x

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. DOI: https://doi.org/10.1111/biom.12248

Didelez, V. (2019). Defining causal mediation with a longitudinal mediator and a survival outcome. Lifetime Data Analysis. 25, 593-610. DOI: https://doi.org/10.1007/s10985-018-9449-0

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. DOI: https://doi.org/10.1093/aje/kwz016

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. DOI: https://doi.org/10.1097/EDE.0000000000000687

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. DOI: https://doi.org/10.1200/JCO.2011.36.2335

Huang, Y-T and Yang, H-I. (2017) Causal mediation analysis of survival outcomes with multiple mediators. Epidemiology, 28(3), 370-378. DOI: https://doi.org/10.1097/EDE.0000000000000651

Iacobucci, D. (2012) Mediation analysis and categorical variables: the final frontier. Journal of Consumer Psychology, 22, 582-594. DOI: https://doi.org/10.1016/j.jcps.2012.03.006

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

Kalbfleisch, J. D. and Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data. Hoboken, N.J.: Wiley, 2nd edition. DOI: https://doi.org/10.1002/9781118032985

Lange. T. and Hansen, J. V. (2011). Direct and indirect effects in a survival context. Epidemiology, 22, 575-581. DOI: https://doi.org/10.1097/EDE.0b013e31821c680c

Lange T, Vansteelandt S, Bekaert M. (2012). A simple unified approach for estimating natural direct and indirect effects. Am J Epidemiol., 176, 190-195. DOI: https://doi.org/10.1093/aje/kwr525

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. DOI: https://doi.org/10.1002/sim.7426

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. DOI: https://doi.org/10.1080/00273171.2013.832132

MacKinnon, D. P. and Dwyer, J. H. (1993). Estimating mediated effects in prevention studies. Evaluation Review, 17, 144-158. DOI: https://doi.org/10.1177/0193841X9301700202

MacKinnon, D. P., Warsi, G. and Dwyer, J. H. (1995). A simulation study of mediated effect measures. Multivariate Behavioral Research, 30, 41-62. DOI: https://doi.org/10.1207/s15327906mbr3001_3

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. DOI: https://doi.org/10.1093/biomet/82.4.669

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. DOI: https://doi.org/10.1186/s12874-016-0130-6

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. DOI: https://doi.org/10.1097/00001648-200009000-00011

Rubin, D. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66, 688-701. DOI: https://doi.org/10.1037/h0037350

Tchetgen Tchetgen EJ. (2013). Inverse odds ratio-weighted estimation for causal mediation analysis. Statistics in Medicine, 32, 4567-4580. DOI: https://doi.org/10.1002/sim.5864

VanderWeele, T. J. (2011). Causal mediation analysis with survival data. Epidemiology, 22(4), 582-585. DOI: https://doi.org/10.1097/EDE.0b013e31821db37e

VanderWeele, T. J. (2015). Explanation in Causal Inference: Methods for Mediation and Interaction. New York: Oxford University Press. DOI: https://doi.org/10.1093/ije/dyw277

VanderWeele, T. J. (2016). Mediation Analysis: a Practitioner's Guide. Annual Review of Public Health, 37, 17-32. DOI: https://doi.org/10.1146/annurev-publhealth-032315-021402

VanderWeele, T. J. and Vansteelandt, S. (2009). Conceptual issues concerningmediation, interventions and composition. Statistics and its Interface, 2, 457-468. DOI: https://doi.org/10.4310/SII.2009.v2.n4.a7

VanderWeele, T. J. and Vansteelandt, S. (2010). Odds ratios for mediation analysis with a dichotomous outcome. American Journal of Epidemiology, 172, 1339-1348. DOI: https://doi.org/10.1093/aje/kwq332

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. DOI: https://doi.org/10.1002/sim.8336

How to Cite

APA

Taddeo, M. M. and Amorim, L. D. (2022). Causal Mediation for Survival Data: A Unifying Approach via GLM. Revista Colombiana de Estadística, 45(1), 161–191. https://doi.org/10.15446/rce.v45n1.94553

ACM

[1]
Taddeo, M.M. and Amorim, L.D. 2022. Causal Mediation for Survival Data: A Unifying Approach via GLM. Revista Colombiana de Estadística. 45, 1 (Jan. 2022), 161–191. DOI:https://doi.org/10.15446/rce.v45n1.94553.

ACS

(1)
Taddeo, M. M.; Amorim, L. D. Causal Mediation for Survival Data: A Unifying Approach via GLM. Rev. colomb. estad. 2022, 45, 161-191.

ABNT

TADDEO, M. M.; AMORIM, L. D. Causal Mediation for Survival Data: A Unifying Approach via GLM. Revista Colombiana de Estadística, [S. l.], v. 45, n. 1, p. 161–191, 2022. DOI: 10.15446/rce.v45n1.94553. Disponível em: https://revistas.unal.edu.co/index.php/estad/article/view/94553. Acesso em: 29 mar. 2024.

Chicago

Taddeo, Marcelo M., and Leila D. Amorim. 2022. “Causal Mediation for Survival Data: A Unifying Approach via GLM”. Revista Colombiana De Estadística 45 (1):161-91. https://doi.org/10.15446/rce.v45n1.94553.

Harvard

Taddeo, M. M. and Amorim, L. D. (2022) “Causal Mediation for Survival Data: A Unifying Approach via GLM”, Revista Colombiana de Estadística, 45(1), pp. 161–191. doi: 10.15446/rce.v45n1.94553.

IEEE

[1]
M. M. Taddeo and L. D. Amorim, “Causal Mediation for Survival Data: A Unifying Approach via GLM”, Rev. colomb. estad., vol. 45, no. 1, pp. 161–191, Jan. 2022.

MLA

Taddeo, M. M., and L. D. Amorim. “Causal Mediation for Survival Data: A Unifying Approach via GLM”. Revista Colombiana de Estadística, vol. 45, no. 1, Jan. 2022, pp. 161-9, doi:10.15446/rce.v45n1.94553.

Turabian

Taddeo, Marcelo M., and Leila D. Amorim. “Causal Mediation for Survival Data: A Unifying Approach via GLM”. Revista Colombiana de Estadística 45, no. 1 (January 19, 2022): 161–191. Accessed March 29, 2024. https://revistas.unal.edu.co/index.php/estad/article/view/94553.

Vancouver

1.
Taddeo MM, Amorim LD. Causal Mediation for Survival Data: A Unifying Approach via GLM. Rev. colomb. estad. [Internet]. 2022 Jan. 19 [cited 2024 Mar. 29];45(1):161-9. Available from: https://revistas.unal.edu.co/index.php/estad/article/view/94553

Download Citation

CrossRef Cited-by

CrossRef citations0

Dimensions

PlumX

Article abstract page views

268

Downloads

Download data is not yet available.