causalMed

Causal Mediation analysis for time fixed and time-varying mediator.

• IORW estimation method was implemented for time-fixed mediation anlaysis.
• G-formula and IPTW was implemented for time-varying treatment, mediator, confounders.

This package is currently in development, please use with caution.

Note

This package was developed for my thesis, titled as Trajectory Modelling Based Mediation Analysis in Heterogeneous Longitudinal Survival Data

该包是本人博士论文《基于轨迹模型的异质性样本重复测量生存资料中介分析方法研究》的研究成果

Installation

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

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)

## Call:
## iorw(fitY = coxph(Surv(os, cvd) ~ bmi + age0 + smoke, data = dtbase), 
##     exposure = "smoke", mediator = c("hdl", "ldl", "tg"), family = "binomial")
## 
## Outcome Model Call:
## coxph(formula = Surv(os, cvd) ~ bmi + age0 + smoke, data = dtbase)
## 
## Exposure Model Call:
## glm(formula = smoke ~ bmi + age0 + smoke + hdl + ldl + tg, family = "binomial", 
##     data = dtbase)
## ------
## Natural effect model
## with standard errors based on the non-parametric bootstrap
## ---
## Exposure: smoke 
## Mediator(s): c, hdl, ldl, tg 
## ------
## Parameter estimates:
##                         Estimate    Bias Std.error conf.low conf.high
## Total effect              0.3136 -0.0177    0.4038  -0.4602    1.1228
## Natural Direct effect    -0.2574  0.0026    0.4639  -1.1692    0.6492
## Natural Indirect effect   0.5710 -0.0203    0.2561   0.0894    1.0932
## ------
## Proportion Mediated: 182.0809%

For time-varying mediator:

Data structure must be in longitudinal format. Need to add the usage of the time-varying mediation analysis usage in the document.

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