Targeted Minimum Loss-Based Estimation (TMLE) for Survival Analysis with Competing Risks

Authors: David Benkeser and Nima Hejazi

## Description

survtmle is an R package designed to use targeted minimum loss-based estimation (TMLE) to compute covariate-adjusted marginal cumulative incidence estimates in right-censored survival settings with and without competing risks. The estimates can leverage ensemble machine learning via the SuperLearner package.

## Installation

For standard use, we recommend installing the package from CRAN via

install.packages("survtmle")

You can install a stable release of survtmle from GitHub via devtools with:

devtools::install_github("benkeser/survtmle")

## Issues

If you encounter any bugs or have any specific feature requests, please file an issue.

## Example

This minimal example shows how to use survtmle to obtain cumulative incidence estimates with a very simple, simulated data set.

# load the package and set seed for reproducibility
library(survtmle)
#> survtmle: Targeted Learning for Survival Analysis
#> Version: 1.1.0
set.seed(341796)

# simulate data
n <- 100
t_0 <- 10
W <- data.frame(W1 = runif(n), W2 = rbinom(n, 1, 0.5))
A <- rbinom(n, 1, 0.5)
T <- rgeom(n,plogis(-4 + W$W1 * W$W2 - A)) + 1
C <- rgeom(n, plogis(-6 + W$W1)) + 1 ftime <- pmin(T, C) ftype <- as.numeric(ftime == T) # apply survtmle for estimation fit <- survtmle(ftime = ftime, ftype = ftype, trt = A, adjustVars = W, glm.trt = "1", glm.ftime = "I(W1*W2) + trt + t", glm.ctime = "W1 + t", method = "hazard", t0 = t_0) # extract cumulative incidence at each timepoint tpfit <- timepoints(fit, times = seq_len(t_0)) # examine output object produced by the timepoints function tpfit #>$est
#>              t1         t2         t3         t4         t5         t6
#> 0 1 0.032997470 0.06492788 0.09582530 0.12572293 0.15465313 0.18264737
#> 1 1 0.008014555 0.01603567 0.02406256 0.03209448 0.04013064 0.04817027
#>             t7         t8         t9        t10
#> 0 1 0.20973629 0.23594966 0.26131640 0.28586459
#> 1 1 0.05621257 0.06425675 0.07230203 0.08034761
#>
#> \$var
#>               t1           t2           t3           t4           t5
#> 0 1 4.565496e-04 0.0005099637 0.0004951975 0.0004852183 0.0005862762
#> 1 1 2.111604e-06 0.0003345475 0.0003208485 0.0003079889 0.0006317931
#>               t6           t7           t8           t9         t10
#> 0 1 0.0012883360 0.0013743743 0.0012847990 0.0020298371 0.003137739
#> 1 1 0.0006229943 0.0009761833 0.0009544762 0.0009534062 0.000956650

# examine plot of cumulative incidences
plot(tpfit)

## Contributions

Contributions are very welcome. Interested contributors can consult our contribution guidelines prior to submitting a pull request.

## Citation

After using the survtmle R package, please cite both of the following:

    @misc{benkeser2017survtmle,
author = {Benkeser, David C and Hejazi, Nima S},
title = {{survtmle}: Targeted Minimum Loss-Based Estimation for
Survival Analysis in {R}},
year  = {2017},
howpublished = {\url{https://github.com/benkeser/survtmle}},
url = {http://dx.doi.org/10.5281/zenodo.835868},
doi = {10.5281/zenodo.835868}
}

@article{benkeser2017improved,
author = {Benkeser, David C and Carone, Marco and Gilbert, Peter B},
title = {Improved estimation of the cumulative incidence of rare
outcomes},
journal = {Statistics in Medicine},
publisher = {Wiley-Blackwell},
year  = {2017},
doi = {10.1002/sim.7337}
}

The MIT License (MIT)

Copyright (c) 2016-2018 David C. Benkeser

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