Univariate Conditional Average Treatment Effect Estimation
Author: Philippe Boileau
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..
If you are looking to apply similar methods to observational study data,
check out the unihtee R
package.
This package can be installed directly from GitHub using
remotes:
remotes::install_github("insightsengineering/uniCATE")unicate() should be used when the outcome is continuous or binary. For
right-censored time-to-event outcomes, use sunicate().
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.
# load the required libraries
library(uniCATE)
library(dplyr)
library(sl3)# 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
)
#> # A tibble: 4 × 7
#> biomarker coef se z p_value p_value_bh p_value_holm
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 bio1 0.876 0.0851 10.3 7.52e-25 3.01e-24 3.01e-24
#> 2 bio2 0.841 0.108 7.80 6.00e-15 1.20e-14 1.80e-14
#> 3 bio3 0.117 0.252 0.464 6.43e- 1 6.72e- 1 1 e+ 0
#> 4 bio4 0.113 0.266 0.423 6.72e- 1 6.72e- 1 1 e+ 0If you encounter any bugs or have any specific feature requests, please file an issue.
Contributions are very welcome. Interested contributors should consult our contribution guidelines prior to submitting a pull request.
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}
}
The contents of this repository are distributed under the Apache 2.0
license. See the
LICENSE.md
and
LICENSE
files for details.
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