IDEAS home Printed from https://ideas.repec.org/a/spr/stabio/v15y2023i3d10.1007_s12561-021-09310-w.html
   My bibliography  Save this article

Estimating Heterogeneous Treatment Effect on Multivariate Responses Using Random Forests

Author

Listed:
  • Boyi Guo

    (University of Alabama at Birmingham)

  • Hannah D. Holscher

    (University of Illinois at Urbana-Champaign)

  • Loretta S. Auvil

    (University of Illinois at Urbana-Champaign)

  • Michael E. Welge

    (University of Illinois at Urbana-Champaign)

  • Colleen B. Bushell

    (University of Illinois at Urbana-Champaign)

  • Janet A. Novotny

    (USDA, ARS, Beltsville Human Nutrition Research Center)

  • David J. Baer

    (USDA, ARS, Beltsville Human Nutrition Research Center)

  • Nicholas A. Burd

    (University of Illinois at Urbana-Champaign)

  • Naiman A. Khan

    (University of Illinois at Urbana-Champaign)

  • Ruoqing Zhu

    (University of Illinois at Urbana-Champaign)

Abstract

Estimating the individualized treatment effect has become one of the most popular topics in statistics and machine learning communities in recent years. Most existing methods focus on modeling the heterogeneous treatment effects for univariate outcomes. However, many biomedical studies are interested in studying multiple highly correlated endpoints at the same time. We propose a random forest model that simultaneously estimates individualized treatment effects of multivariate outcomes. We consider a popular study design where covariates and outcomes are measured both before and after the intervention. The proposed model uses oblique splitting rules to partition population space to the neighborhood that experiences distinct treatment effects. An extensive simulation study suggests that the proposed method outperforms existing methods in various nonlinear settings. We further apply the proposed method to two nutrition studies investigating the effects of food consumption on gastrointestinal microbiota composition and clinical biomarkers. The method has been implemented in a freely available R package MOTE.RF at https://github.com/boyiguo1/MOTE.RF .

Suggested Citation

  • Boyi Guo & Hannah D. Holscher & Loretta S. Auvil & Michael E. Welge & Colleen B. Bushell & Janet A. Novotny & David J. Baer & Nicholas A. Burd & Naiman A. Khan & Ruoqing Zhu, 2023. "Estimating Heterogeneous Treatment Effect on Multivariate Responses Using Random Forests," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(3), pages 545-561, December.
  • Handle: RePEc:spr:stabio:v:15:y:2023:i:3:d:10.1007_s12561-021-09310-w
    DOI: 10.1007/s12561-021-09310-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12561-021-09310-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s12561-021-09310-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. E. B. Laber & Y. Q. Zhao, 2015. "Tree-based methods for individualized treatment regimes," Biometrika, Biometrika Trust, vol. 102(3), pages 501-514.
    2. Jason Brinkley & Anastasios Tsiatis & Kevin J. Anstrom, 2010. "A Generalized Estimator of the Attributable Benefit of an Optimal Treatment Regime," Biometrics, The International Biometric Society, vol. 66(2), pages 512-522, June.
    3. Michel Tenenhaus & Arthur Tenenhaus & Patrick J. F. Groenen, 2017. "Regularized Generalized Canonical Correlation Analysis: A Framework for Sequential Multiblock Component Methods," Psychometrika, Springer;The Psychometric Society, vol. 82(3), pages 737-777, September.
    4. Lu Tian & Ash A. Alizadeh & Andrew J. Gentles & Robert Tibshirani, 2014. "A Simple Method for Estimating Interactions Between a Treatment and a Large Number of Covariates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1517-1532, December.
    5. Yingqi Zhao & Donglin Zeng & A. John Rush & Michael R. Kosorok, 2012. "Estimating Individualized Treatment Rules Using Outcome Weighted Learning," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1106-1118, September.
    6. Michel Tenenhaus, 2011. "Regularized generalized canonical correlation analysis," Post-Print hal-00578321, HAL.
    7. Karen K. Ryan & Valentina Tremaroli & Christoffer Clemmensen & Petia Kovatcheva-Datchary & Andriy Myronovych & Rebekah Karns & Hilary E. Wilson-Pérez & Darleen A. Sandoval & Rohit Kohli & Fredrik Bäck, 2014. "FXR is a molecular target for the effects of vertical sleeve gastrectomy," Nature, Nature, vol. 509(7499), pages 183-188, May.
    8. Ruoqing Zhu & Michael R. Kosorok, 2012. "Recursively Imputed Survival Trees," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(497), pages 331-340, March.
    9. Arthur Tenenhaus & Michel Tenenhaus, 2011. "Regularized Generalized Canonical Correlation Analysis," Psychometrika, Springer;The Psychometric Society, vol. 76(2), pages 257-284, April.
    10. Ruoqing Zhu & Ying-Qi Zhao & Guanhua Chen & Shuangge Ma & Hongyu Zhao, 2017. "Greedy outcome weighted tree learning of optimal personalized treatment rules," Biometrics, The International Biometric Society, vol. 73(2), pages 391-400, June.
    11. Lihui Zhao & Lu Tian & Tianxi Cai & Brian Claggett & L. J. Wei, 2013. "Effectively Selecting a Target Population for a Future Comparative Study," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 527-539, June.
    12. Michel Tenenhaus & Arthur Tenenhaus, 2011. "Regularized Generalized Canonical Correlation Analysis," Post-Print hal-00609220, HAL.
    13. Baqun Zhang & Anastasios A. Tsiatis & Eric B. Laber & Marie Davidian, 2013. "Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions," Biometrika, Biometrika Trust, vol. 100(3), pages 681-694.
    14. Simon, Noah & Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2011. "Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 39(i05).
    15. repec:bla:biomet:v:71:y:2015:i:4:p:895-904 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sera Şanlı, 2023. "Untapped potentials on a well‐endowed plate: A sustainable future catalogue for the harmony of renewable technologies with the water‐energy‐climate‐SDGs nexus," Natural Resources Forum, Blackwell Publishing, vol. 47(4), pages 672-698, November.
    2. Emily L. Butler & Eric B. Laber & Sonia M. Davis & Michael R. Kosorok, 2018. "Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules," Biometrics, The International Biometric Society, vol. 74(1), pages 18-26, March.
    3. Xiuli Du & Xiaohu Jiang & Jinguan Lin, 2023. "Multinomial Logistic Factor Regression for Multi-source Functional Block-wise Missing Data," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 975-1001, September.
    4. Heungsun Hwang & Gyeongcheol Cho, 2020. "Global Least Squares Path Modeling: A Full-Information Alternative to Partial Least Squares Path Modeling," Psychometrika, Springer;The Psychometric Society, vol. 85(4), pages 947-972, December.
    5. Wang, Wenjia & Zhou, Yi-Hui, 2021. "Eigenvector-based sparse canonical correlation analysis: Fast computation for estimation of multiple canonical vectors," Journal of Multivariate Analysis, Elsevier, vol. 185(C).
    6. Ruoqing Zhu & Ying-Qi Zhao & Guanhua Chen & Shuangge Ma & Hongyu Zhao, 2017. "Greedy outcome weighted tree learning of optimal personalized treatment rules," Biometrics, The International Biometric Society, vol. 73(2), pages 391-400, June.
    7. Baqun Zhang & Min Zhang, 2018. "C‐learning: A new classification framework to estimate optimal dynamic treatment regimes," Biometrics, The International Biometric Society, vol. 74(3), pages 891-899, September.
    8. Joseph F. Hair & G. Tomas M. Hult & Christian M. Ringle & Marko Sarstedt & Kai Oliver Thiele, 2017. "Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods," Journal of the Academy of Marketing Science, Springer, vol. 45(5), pages 616-632, September.
    9. Lukáš Malec & Vladimír Janovský, 2020. "Connecting the multivariate partial least squares with canonical analysis: a path-following approach," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(3), pages 589-609, September.
    10. Ying Huang & Juhee Cho & Youyi Fong, 2021. "Threshold‐based subgroup testing in logistic regression models in two‐phase sampling designs," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 291-311, March.
    11. Stéphanie Bougeard & Hervé Abdi & Gilbert Saporta & Ndèye Niang, 2018. "Clusterwise analysis for multiblock component methods," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(2), pages 285-313, June.
    12. Cristina Davino & Pasquale Dolce & Stefania Taralli & Domenico Vistocco, 2022. "Composite-Based Path Modeling for Conditional Quantiles Prediction. An Application to Assess Health Differences at Local Level in a Well-Being Perspective," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 161(2), pages 907-936, June.
    13. Caiyun Fan & Wenbin Lu & Rui Song & Yong Zhou, 2017. "Concordance-assisted learning for estimating optimal individualized treatment regimes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1565-1582, November.
    14. Dana Johnson & Wenbin Lu & Marie Davidian, 2023. "A general framework for subgroup detection via one‐step value difference estimation," Biometrics, The International Biometric Society, vol. 79(3), pages 2116-2126, September.
    15. Runchao Jiang & Wenbin Lu & Rui Song & Marie Davidian, 2017. "On estimation of optimal treatment regimes for maximizing t-year survival probability," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1165-1185, September.
    16. Hyung G. Park & Danni Wu & Eva Petkova & Thaddeus Tarpey & R. Todd Ogden, 2023. "Bayesian Index Models for Heterogeneous Treatment Effects on a Binary Outcome," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(2), pages 397-418, July.
    17. Zhen Li & Jie Chen & Eric Laber & Fang Liu & Richard Baumgartner, 2023. "Optimal Treatment Regimes: A Review and Empirical Comparison," International Statistical Review, International Statistical Institute, vol. 91(3), pages 427-463, December.
    18. Marcela Guachamín & Diana Ramírez‐Cifuentes & Olga Delgado, 2020. "An Uncertainty Thermometer to Measure the Macroeconomic‐Financial Risk in South American Countries," Journal of International Development, John Wiley & Sons, Ltd., vol. 32(6), pages 854-890, August.
    19. Pasquale Dolce & Vincenzo Esposito Vinzi & Natale Carlo Lauro, 2018. "Non-symmetrical composite-based path modeling," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 759-784, September.
    20. Mayr, Kathrin & Teller, Christoph, 2023. "Customer deviance in retailing: Managers’ emotional support and employees’ affective wellbeing," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:stabio:v:15:y:2023:i:3:d:10.1007_s12561-021-09310-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.