In adayim/causalMed: Causal Mediation Analysis in Survival settings

causalMed

Causal Mediation analysis for time fixed and time-varying mediator. This package is currently in development, please use with caution.

Installation

# Install the development version from GitHub:
# install.packages("devtools")
devtools::install_github("adayim/causalMed")

devtools::load_all()

Usage

For time fixed mediation analysis:

library(causalMed)

library(survival)
data(lipdat)
dtbase <- lipdat[lipdat$time == 0, ]   # Select the first row
out <- iorw(coxph(Surv(os, cvd) ~ bmi + age0 + smoke, data = dtbase),
            exposure   = "smoke",
            mediator   = c("hdl", "ldl", "tg"),
            family     = "binomial")
summary(out)

For time-varying mediator:

Data structure must be in longitudinal format, and only mediator is time-varying.

TODO

• [ ] Time-varying treatment and covariates.
• [ ] More documentations.
• [ ] More tests is needed.

References

1. Tchetgen Tchetgen, E. J. (2013). Inverse odds ratio‐weighted estimation for causal mediation analysis. Statistics in medicine, 32(26), 4567-4580. DOI:10.1002/sim.5864
2. Lin, S. H., Young, J. G., Logan, R., & VanderWeele, T. J. (2017). Mediation analysis for a survival outcome with time‐varying exposures, mediators, and confounders. Statistics in medicine, 36(26), 4153-4166. DOI:10.1002/sim.7426
3. 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). DOI:10.1515/jci-2016-0006

adayim/causalMed documentation built on June 2, 2020, 4:11 p.m.