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https://github.com/insightsengineering/unicate

Univariate conditional average treatment effect estimation for predictive biomarker discovery
https://github.com/insightsengineering/unicate

biomarkers clinical-trials high-dimensional-data nonparametrics r treatment-effects

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Univariate conditional average treatment effect estimation for predictive biomarker discovery

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README 

        
---

output: github_document

---



```{r, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```



# R/`uniCATE`



> Univariate Conditional Average Treatment Effect Estimation



__Author:__ [Philippe Boileau](https://pboileau.ca/)



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---



`uniCATE` implements statistical inference procedures for variable importance

measures that assess the treatment effect modification capabilities of

individual pre-treatment biomarkers in high-dimensional randomized control

trials. This variable importance measure is defined as the vector of simple

linear regression slope coefficients obtained by regressing the difference in

potential outcomes on each biomarker. This parameter, which we dub the

*univariate conditional average treatment effect*, is a reasonable indicator of

treatment effect modification in all but pathological biomarker-outcome

relationships, and can therefore be used to identify predictive biomarkers.

Assumption-lean estimation and testing procedures based on semiparametric theory

are made available for continuous, binary, and right-censored time-to-event

outcomes. Additional details are provided in [*A Flexible Approach for

Predictive Biomarker Discovery* by Boileau et

al.](https://academic.oup.com/biostatistics/advance-article/doi/10.1093/biostatistics/kxac029/6647929).



If you are looking to apply similar methods to observational study data, check

out the [`unihtee` R package](https://github.com/insightsengineering/unihtee).



## Installation



This package can be installed directly from GitHub using

[`remotes`](https://CRAN.R-project.org/package=remotes):



```{r gh-master-installation, eval=FALSE}

remotes::install_github("insightsengineering/uniCATE")

```



## Usage



`unicate()` should be used when the outcome is continuous or binary. For

right-censored time-to-event outcomes, use `sunicate()`.



## Example



We simulate a randomized control trial in which there is a heterogeneous

treatment effect for biomarkers 1 and 2. `unicate()` successfully identifies

these biomarkers as effect modifiers.



```{r setup, include=FALSE}

library(uniCATE)

library(dplyr)

library(sl3)

```

```{r fake-setup, eval=FALSE}

# load the required libraries

library(uniCATE)

library(dplyr)

library(sl3)

```

```{r example}

# set the seed for reproducibility

set.seed(514)



# simulate some randomized control data

n <- 100

data <- tibble("treatment" = rbinom(n, 1, 0.5)) %>%

  mutate(

    bio1 = rnorm(n, mean = 2, sd = 0.2),

    bio2 = rnorm(n, mean = -2, sd = 0.2),

    bio3 = rnorm(n, mean = 0, sd = 0.1),

    bio4 = rnorm(n, mean = 0, sd = 0.1),

    covar = 0.2 * rbinom(n, 1, 0.4),

    response = covar + bio1 * treatment + bio2 * treatment

  )



# define the required arguments

covariates <- c("bio1", "bio2", "bio3", "bio4", "covar")

biomarkers <- c("bio1", "bio2", "bio3", "bio4")

propensity_score_ls <- list("1" = 0.5, "0" = 0.5)



# create a simple SuperLearner using a linear model and a random forest

interactions <- lapply(biomarkers, function(b) c(b, "treatment"))

lrnr_interactions <- sl3::Lrnr_define_interactions$new(interactions)

lrnr_glm <- sl3::make_learner(

  sl3::Pipeline, lrnr_interactions, sl3::Lrnr_glm$new()

)

lrnr_sl <- Lrnr_sl$new(

  learners = make_learner(

    Stack, Lrnr_ranger$new(), lrnr_glm

  ),

  metalearner = make_learner(Lrnr_nnls)

)



# apply uniCATE to the simulated data

unicate(

  data,

  outcome = "response",

  treatment = "treatment",

  covariates = covariates,

  biomarkers = biomarkers,

  propensity_score_ls = propensity_score_ls,

  super_learner = lrnr_sl,

  v_folds = 2L

)

```



## Issues



If you encounter any bugs or have any specific feature requests, please [file

an issue](https://github.com/insightsengineering/uniCATE/issues).



## Contributions



Contributions are very welcome. Interested contributors should consult our

[contribution

guidelines](https://github.com/insightsengineering/uniCATE/blob/master/.github/CONTRIBUTING.md)

prior to submitting a pull request.



## Citation



You can cite this package and the methodology used therein with the following

BibTeX entries:



```

@manual{uniCATE,

  title = {uniCATE: Univariate Conditional Average Treatment Effect Estimation},

  author = {Philippe Boileau},

  year = {2023},

  note = {R package version 0.4.0},

}

  

@article{boileau2022,

  author = {Boileau, Philippe and Qi, Nina Ting and van der Laan, Mark J and Dudoit, Sandrine and Leng, Ning},

  title = "{A flexible approach for predictive biomarker discovery}",

  journal = {Biostatistics},

  year = {2022},

  month = {07},

  issn = {1465-4644},

  doi = {10.1093/biostatistics/kxac029},

  url = {https://doi.org/10.1093/biostatistics/kxac029}

}

```



## License



The contents of this repository are distributed under the Apache 2.0 license.

See the

[`LICENSE.md`](https://github.com/insightsengineering/uniCATE/blob/main/LICENSE.md)

and [`LICENSE`](https://github.com/insightsengineering/uniCATE/blob/main/LICENSE)

files for details.