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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# lmtp <img src="man/figures/lmtp.png" align="right" height="139"/></a>
<!-- badges: start -->
[](https://CRAN.R-project.org/package=lmtp)  [](https://github.com/nt-williams/lmtp/actions) [](https://app.codecov.io/gh/nt-williams/lmtp) [](https://www.gnu.org/licenses/gpl-3.0) [](https://www.repostatus.org/#active)
<!-- badges: end -->
> Non-parametric Causal Effects of Feasible Interventions Based on Modified Treatment Policies
Nick Williams and Ivan Diaz
------------------------------------------------------------------------
**lmtp** is an R package that provides an estimation framework for the casual effects of feasible interventions based on point-treatment and longitudinal modified treatment policies as described in Diaz, Williams, Hoffman, and Schenck (2020). Two primary estimators are supported, a targeted maximum likelihood (TML) estimator and a sequentially doubly robust (SDR) estimator. Binary, continuous, time-to-event (including competing risks), and censored outcomes are allowed. **lmtp** is built atop the [`SuperLearner`](https://CRAN.R-project.org/package=SuperLearner) package to utilize ensemble machine learning for estimation. The treatment mechanism is estimated via a density ratio classification procedure irrespective of treatment variable type providing decreased computation time when treatment is continuous. Dynamic treatment regimes and incremental propensity scores based on the risk ratio are also supported.
For an in-depth look at the package's functionality, please consult the accompanying technical paper in [Observational Studies](https://muse.jhu.edu/article/883479).
## Installation
**lmtp** can be installed from CRAN with:
``` r
install.packages("lmtp")
```
The stable, development version can be installed from GitHub with:
``` r
devtools::install_github("nt-williams/lmtp@devel")
```
## What even is a modified treatment policy?
Modified treatment policies (MTP) are interventions that can depend on the *natural* value of the treatment (the treatment value in the absence of intervention). A key assumption for causal inference is the *positivity assumption* which states that all observations have a non-zero probability of experiencing a treatment value. **When working with continuous or multivalued treatments, violations of the positivity assumption are likely to occur. MTPs offer a solution to this problem.**
## Can lmtp estimate other effects?
Yes! **lmtp** can estimate the effects of deterministic, static treatment effects (such as the ATE) and deterministic, dynamic treatment regimes for binary, continuous, and survival outcomes.
### Features
| Feature | Status |
|---------------------------------------------------------|:-------------:|
| Point treatment | ✓ |
| Longitudinal treatment | ✓ |
| Modified treatment intervention | ✓ |
| Incremental Propensity Score Intervention (Using the risk ratio) | ✓ |
| Static intervention | ✓ |
| Dynamic intervention | ✓ |
| Continuous treatment | ✓ |
| Binary treatment | ✓ |
| Categorical treatment | ✓ |
| Multivariate treatment | ✓ |
| Missingness in treatment | |
| Continuous outcome | ✓ |
| Binary outcome | ✓ |
| Censored outcome | ✓ |
| Competing risks | ✓ |
| Mediation | |
| Survey weights | ✓ |
| Super learner | ✓ |
| Clustered data | ✓ |
| Parallel processing | ✓ |
| Progress bars | ✓ |
## Example
```{r}
library(lmtp)
# the data: 4 treatment nodes with time varying covariates and a binary outcome
head(sim_t4)
```
We're interested in a treatment policy, `d`, where exposure is decreased by 1 only among subjects whose exposure won't go below 1 if intervened upon. The true population outcome under this policy is about 0.305.
```{r}
# a treatment policy function to be applied at all time points
policy <- function(data, trt) {
(data[[trt]] - 1) * (data[[trt]] - 1 >= 1) + data[[trt]] * (data[[trt]] - 1 < 1)
}
```
In addition to specifying a treatment policy, we need to specify our treatment variables and time-varying covariates.
```{r}
# treatment nodes, a character vector of length 4
A <- c("A_1", "A_2", "A_3", "A_4")
# time varying nodes, a list of length 4
L <- list(c("L_1"), c("L_2"), c("L_3"), c("L_4"))
```
We can now estimate the effect of our treatment policy, `d`. In this example, we'll use the cross-validated TML estimator with 10 folds.
```{r, eval = FALSE}
lmtp_tmle(sim_t4, A, "Y", time_vary = L, shift = policy, mtp = TRUE, folds = 10)
#> LMTP Estimator: TMLE
#> Trt. Policy: (policy)
#>
#> ── Population intervention estimate ──
#>
#> Estimate: 0.25
#> Std. error: 0.02
#> 95% Conf. int.: 0.2, 0.29
```
## Data structure
#### Single time point
<img src="man/figures/pointtrt.png" height="300"/>
#### Time-varying exposure and confounders, not survival outcome
<img src="man/figures/timevary.png" height="300"/>
#### Single exposure, survival outcome
<img src="man/figures/survival.png" height="300"/>
#### Time-varying exposure and confounders, survival outcome
<img src="man/figures/timevarysurvival.png" height="300"/>
## Similar Implementations
A variety of other R packages perform similar tasks as **lmtp**. However, **lmtp** is the only R package currently capable of estimating causal effects for binary, categorical, continuous, and mixture exposures in both the point treatment and longitudinal setting.
- [`txshift`](https://github.com/nhejazi/txshift)\
- [`tmle3`](https://github.com/tlverse/tmle3)\
- [`tmle3shift`](https://github.com/tlverse/tmle3shift)
- [`ltmle`](https://CRAN.R-project.org/package=ltmle)\
- [`tmle`](https://CRAN.R-project.org/package=tmle)
## Citation
Please cite the following when using **lmtp** in publications. Citation should include both the R package article and the paper establishing the statistical methodology.
```
@article{,
title = {lmtp: An R package for estimating the causal effects of modified treatment policies},
author = {Nicholas T Williams and Iván Díaz},
journal = {Observational Studies},
year = {2023},
url = {https://muse.jhu.edu/article/883479}
}
@article{
doi:10.1080/01621459.2021.1955691,
author = {Iván Díaz and Nicholas Williams and Katherine L. Hoffman and Edward J. Schenck},
title = {Non-parametric causal effects based on longitudinal modified treatment policies},
journal = {Journal of the American Statistical Association},
year = {2021},
doi = {10.1080/01621459.2021.1955691},
URL = {https://doi.org/10.1080/01621459.2021.1955691},
}
```