{"id":18733562,"url":"https://github.com/insightsengineering/unihtee","last_synced_at":"2025-07-05T18:07:21.331Z","repository":{"id":103218197,"uuid":"545657085","full_name":"insightsengineering/unihtee","owner":"insightsengineering","description":"Tools for uncovering treatment effect modifiers in high-dimensional data.","archived":false,"fork":false,"pushed_at":"2025-07-04T13:16:50.000Z","size":805,"stargazers_count":4,"open_issues_count":1,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-07-04T14:54:41.346Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://insightsengineering.github.io/unihtee/","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/insightsengineering.png","metadata":{"files":{"readme":"README.Rmd","changelog":"NEWS.md","contributing":".github/CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":".github/CODEOWNERS","security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null},"funding":{"custom":["https://pharmaverse.org"]}},"created_at":"2022-10-04T18:58:02.000Z","updated_at":"2024-11-08T13:33:37.000Z","dependencies_parsed_at":null,"dependency_job_id":"30e43515-3ac8-406d-869a-d30904b545b2","html_url":"https://github.com/insightsengineering/unihtee","commit_stats":null,"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"purl":"pkg:github/insightsengineering/unihtee","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/insightsengineering%2Funihtee","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/insightsengineering%2Funihtee/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/insightsengineering%2Funihtee/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/insightsengineering%2Funihtee/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/insightsengineering","download_url":"https://codeload.github.com/insightsengineering/unihtee/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/insightsengineering%2Funihtee/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":263781180,"owners_count":23510420,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["heterogeneous-treatment-effects","high-dimensional-data","nonparametrics","targeted-learning","variable-importance"],"created_at":"2024-11-07T15:10:25.689Z","updated_at":"2025-07-05T18:07:21.324Z","avatar_url":"https://github.com/insightsengineering.png","language":"R","funding_links":["https://pharmaverse.org"],"categories":[],"sub_categories":[],"readme":"---\noutput: github_document\nbibliography: inst/references.bib \n---\n\n\u003c!-- README.md is generated from README.Rmd. Please edit that file --\u003e\n\n```{r, include = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"man/figures/README-\",\n  out.width = \"100%\"\n)\n```\n\n# R/`unihtee`\n\n\u003e Univariate Heterogeneous Treatment Effect Estimation\n\n__Author:__ [Philippe Boileau](https://pboileau.ca/)\n\n\u003c!-- badges: start --\u003e\n[![Project Status: Active – The project has reached a stable, usable state and is being actively developed.](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)\n\u003c!-- badges: end --\u003e\n\n---\n\n`unihtee` provides tools for uncovering treatment effect modifiers in\nhigh-dimensional data. Treatment effect modification is defined using variable\nimportance parameters based on absolute and relative effects. Inference is\nperformed about these variable importance measures using nonparametric\nestimators. Users may use one-step or targeted maximum likelihood estimators.\nUnder general conditions, these estimators are unbiased and efficient.\n\nAdditional details about this methodology is provided in @boileau2022\nand in the package's\n[vignette](https://insightsengineering.github.io/unihtee/main/articles/using-unihtee.html).\n\n## Installation\n\nThe package may be installed from GitHub using\n[`remotes`](https://CRAN.R-project.org/package=remotes):\n\n```{r gh-master-installation, eval=FALSE}\nremotes::install_github(\"insightsengineering/unihtee\")\n```\n\n`unihtee` is under active development. Check back often for updates.\n\n## Usage\n\n`unihtee()` is the only user-facing function. It can be used to perform\ninference about the treatment effect modification variable importance\nparameters. These parameters are defined for data-generating processes with\ncontinuous, binary and time-to-event outcomes with binary exposure variables.\nVariable importance parameters based on absolute and relative effects are\navailable. Details are provided in the vignette.\n\n## Example\n\nWe simulate some observational study data that contains ten pre-treatment\ncovariates, of which are two treatment effect modifiers. We then perform\ninference about the absolute treatment effect modifier variable importance\nparameter, which is inspired by the average treatment effect.\n\n```{r example}\nlibrary(unihtee)\nlibrary(MASS)\nlibrary(data.table)\nlibrary(sl3)\n\nset.seed(510)\n\n## create the dataset\nn_obs \u003c- 500\nw \u003c- mvrnorm(n = n_obs, mu = rep(0, 10), Sigma = diag(10))\nconfounder_names \u003c- paste0(\"w_\", seq_len(10))\ncolnames(w) \u003c- confounder_names\na \u003c- rbinom(n = n_obs, size = 1, prob = plogis(w[, 1] + w[, 2]))\ny \u003c- rnorm(n = n_obs, mean = w[, 1] + w[, 2] + a * w[, 3] - a * w[, 4])\ndt \u003c- as.data.table(cbind(w, a, y))\n\n## targeted maximum likelihood estimates and testing procedure\nunihtee(\n  data = dt,\n  confounders = confounder_names,\n  modifiers = confounder_names,\n  exposure = \"a\",\n  outcome = \"y\",\n  outcome_type = \"continuous\",\n  effect = \"absolute\",\n  estimator = \"tmle\"\n)\n```\n\n## Issues\n\nIf you encounter any bugs or have any specific feature requests, please [file\nan issue](https://github.com/insightsengineering/unihtee/issues).\n\n## Contributions\n\nContributions are very welcome. Interested contributors should consult our\n[contribution\nguidelines](https://github.com/insightsengineering/unihtee/blob/master/.github/CONTRIBUTING.md)\nprior to submitting a pull request.\n\n## Citation\n\nTo cite `unihtee` and the papers introducing the underlying framework, use the\nfollowing BibTeX entries:\n\n```\n@manual{unihtee,\n  title = {unihtee: Univariate Heterogeneous Treatment Effect Estimation},\n  author = {Philippe Boileau},\n  note = {R package version 0.0.1}\n}\n\n@article{boileau2025,\n  title = {A Nonparametric Framework for Treatment Effect Modifier Discovery in High Dimensions},\n  author = {Boileau, Philippe and Leng, Ning and Hejazi, Nima S and {van der Laan}, Mark and Dudoit, Sandrine},\n  year = {2025},\n  journal = {Journal of the Royal Statistical Society Series B: Statistical Methodology},\n  volume = {87},\n  number = {1},\n  pages = {157--185},\n  issn = {1369-7412},\n  doi = {10.1093/jrsssb/qkae084}\n}\n\n@article{boileau2022,\n  author = {Boileau, Philippe and Qi, Nina Ting and van der Laan, Mark J and Dudoit, Sandrine and Leng, Ning},\n  title = {A flexible approach for predictive biomarker discovery},\n  journal = {Biostatistics},\n  year = {2022},\n  month = {07},\n  issn = {1465-4644},\n  doi = {10.1093/biostatistics/kxac029},\n  url = {https://doi.org/10.1093/biostatistics/kxac029}\n}\n```\n\n## License\n\nThe contents of this repository are distributed under the Apache 2.0 license.\nSee the\n[`LICENSE.md`](https://github.com/insightsengineering/unihtee/blob/main/LICENSE.md)\nand\n[`LICENSE`](https://github.com/insightsengineering/unihtee/blob/main/LICENSE)\nfiles for details.\n\n## References\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Finsightsengineering%2Funihtee","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Finsightsengineering%2Funihtee","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Finsightsengineering%2Funihtee/lists"}