Estimation for Natural Mediation Effect in Longitudinal Data
Authors: Ivana Malenica, Wenjing Zheng and Mark van der Laan
medltmle
estimates the natural mediation effect for a longitudinal
setting with time-varying mediators. This R package implements several
estimators of the data dependent parameter (SE) and non-data dependent
parameter (NE) for direct and indirect mediation effects over multiple
time points, adjusting for measured time-varying confounding and
informative right-censoring. The theoretical justifications for using
either of the aforementioned parameters are outlined in the vignette.
Currently available estimators include
- TMLE for longitudinal data
- The longitudinal G-computation
- Inverse Probability of Treatment Weighted (IPTW)
Future releases will support longitudinal data in long format and will
integrate with the stremr
package
in order to handle more elaborate longitudinal data structures.
You can install the most recent stable release from GitHub via
devtools
with:
devtools::install_github("podTockom/medltmle")
If you encounter any bugs or have any specific feature requests, please file an issue.
To get an idea of how medltmle
works, let’s try using it with a simple
simulated data set:
# setup
suppressMessages(library(medltmle))
set.seed(67394)
# simulation parameters
end.time = 2
n <- 400
# simulate data
data <- GenerateData(n = n, end.time = end.time)
Next, we can generate simple models for conditional densities and iterative expectations, and define counterfactual exposures:
# define models
spec <- make.sim.spec(2)
# define counterfactuals
abar <- 1
abar.prime <- 0
Having gone through the above steps, we can now obtain IPTW and TMLE estimates of the natural mediation effect:
# let's fit the longitudinal TMLE for the mediation parameter
result_10 <- suppressMessages(
medltmle(data = data,
Anodes = names(data)[grep("^A", names(data))],
Cnodes = names(data)[grep("^C", names(data))],
Znodes = names(data)[grep("^Z", names(data))],
Lnodes = names(data)[grep("^L", names(data))],
Ynodes = names(data)[grep("^Y", names(data))],
survivalOutcome = TRUE,
QLform = spec$QL.c,
QZform = spec$QZ.c,
gform = spec$g.c,
qzform = spec$qz.c,
qLform = spec$qL.c,
abar = rep(abar, end.time),
abar.prime = rep(abar.prime, end.time),
CSE = TRUE,
time.end = end.time
)
)
After using the medltmle R package, please cite the following:
@software{malenica2018medltmle,
author = {Malenica, Ivana and Zheng, Wenjing and {van der Laan}, Mark J},
title = {{medltmle}: Natural mediation effect for a longitudinal setting with time-varying mediators},
year = {2018},
doi = {},
url = {https://github.com/imalenica/medltmle},
note = {R package version 0.1.0}
}
© 2018 Ivana Malenica
The contents of this repository are distributed under the MIT license. See below for details:
The MIT License (MIT)
Copyright (c) 2018 Ivana Malenica
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.