Univariate Heterogeneous Treatment Effect Estimation • unihtee

Univariate Heterogeneous Treatment Effect Estimation

unihtee provides tools for uncovering treatment effect modifiers in high-dimensional data. Treatment effect modification is defined using variable importance parameters based on absolute and relative effects. Inference is performed about these variable importance measures using nonparametric estimators. Users may use one-step or targeted maximum likelihood estimators. Under general conditions, these estimators are unbiased and efficient.

Additional details about this methodology is provided in Boileau et al. (2022) and in the package’s vignette.

Installation

The package may be installed from GitHub using remotes:

remotes::install_github("insightsengineering/unihtee")

unihtee is under active development. Check back often for updates.

Usage

unihtee() is the only user-facing function. It can be used to perform inference about the treatment effect modification variable importance parameters. These parameters are defined for data-generating processes with continuous, binary and time-to-event outcomes with binary exposure variables. Variable importance parameters based on absolute and relative effects are available. Details are provided in the vignette.

Example

We simulate some observational study data that contains ten pre-treatment covariates, of which are two treatment effect modifiers. We then perform inference about the absolute treatment effect modifier variable importance parameter, which is inspired by the average treatment effect.

library(unihtee)
library(MASS)
library(data.table)
library(sl3)

set.seed(510)

## create the dataset
n_obs <- 500
w <- mvrnorm(n = n_obs, mu = rep(0, 10), Sigma = diag(10))
confounder_names <- paste0("w_", seq_len(10))
colnames(w) <- confounder_names
a <- rbinom(n = n_obs, size = 1, prob = plogis(w[, 1] + w[, 2]))
y <- rnorm(n = n_obs, mean = w[, 1] + w[, 2] + a * w[, 3] - a * w[, 4])
dt <- as.data.table(cbind(w, a, y))

## targeted maximum likelihood estimates and testing procedure
unihtee(
  data = dt,
  confounders = confounder_names,
  modifiers = confounder_names,
  exposure = "a",
  outcome = "y",
  outcome_type = "continuous",
  effect = "absolute",
  estimator = "tmle"
)
#>     modifier     estimate        se           z      p_value    ci_lower
#>  1:      w_3  1.044592804 0.1613285  6.47494319 9.484769e-11  0.72838896
#>  2:      w_4 -0.869002514 0.1492388 -5.82289742 5.783606e-09 -1.16151066
#>  3:      w_8  0.137803254 0.1137965  1.21096238 2.259098e-01 -0.08523784
#>  4:      w_1  0.115258422 0.1160997  0.99275414 3.208298e-01 -0.11229692
#>  5:      w_9  0.124150185 0.1300374  0.95472664 3.397160e-01 -0.13072315
#>  6:     w_10 -0.097928234 0.1356976 -0.72166517 4.705004e-01 -0.36389554
#>  7:      w_6  0.054845105 0.1159964  0.47281713 6.363437e-01 -0.17250792
#>  8:      w_2 -0.064478504 0.1767632 -0.36477331 7.152806e-01 -0.41093441
#>  9:      w_7 -0.014704981 0.1485331 -0.09900136 9.211372e-01 -0.30582989
#> 10:      w_5  0.001500152 0.1103752  0.01359138 9.891560e-01 -0.21483526
#>       ci_upper  p_value_fdr
#>  1:  1.3607966 9.484769e-10
#>  2: -0.5764944 2.891803e-08
#>  3:  0.3608444 6.794319e-01
#>  4:  0.3428138 6.794319e-01
#>  5:  0.3790235 6.794319e-01
#>  6:  0.1680391 7.841673e-01
#>  7:  0.2821981 8.941008e-01
#>  8:  0.2819774 8.941008e-01
#>  9:  0.2764199 9.891560e-01
#> 10:  0.2178356 9.891560e-01

Issues

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

Contributions

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

Citation

Cite unihtee using the following BibTeX entry:

@manual{unihtee,
  title = {unihtee: Univariate Heterogeneous Treatment Effect Estimation},
  author = {Philippe Boileau},
  note = {R package version 0.0.1}
}

License

The contents of this repository are distributed under the Apache 2.0 license. See the LICENSE.md and LICENSE files for details.

References

Boileau, Philippe, Nina Ting Qi, Mark J van der Laan, Sandrine Dudoit, and Ning Leng. 2022. “A flexible approach for predictive biomarker discovery.” Biostatistics, July. https://doi.org/10.1093/biostatistics/kxac029.

R/unihtee