IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i22p14903-d970924.html
   My bibliography  Save this article

A Flexible Approach for Assessing Heterogeneity of Causal Treatment Effects on Patient Survival Using Large Datasets with Clustered Observations

Author

Listed:
  • Liangyuan Hu

    (Department of Biostatistics and Epidemiology, Rutgers University, New Brunswick, NJ 07102, USA)

  • Jiayi Ji

    (Department of Biostatistics and Epidemiology, Rutgers University, New Brunswick, NJ 07102, USA)

  • Hao Liu

    (Department of Biostatistics and Epidemiology, Rutgers University, New Brunswick, NJ 07102, USA
    Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ 07102, USA)

  • Ronald Ennis

    (Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ 07102, USA
    Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ 07102, USA)

Abstract

Personalized medicine requires an understanding of treatment effect heterogeneity. Evolving toward causal evidence for scenarios not studied in randomized trials necessitates a methodology using real-world evidence. Herein, we demonstrate a methodology that generates causal effects, assesses the heterogeneity of the effects and adjusts for the clustered nature of the data. This study uses a state-of-the-art machine learning survival model, riAFT-BART, to draw causal inferences about individual survival treatment effects, while accounting for the variability in institutional effects; further, it proposes a data-driven approach to agnostically (as opposed to a priori hypotheses) ascertain which subgroups exhibit an enhanced treatment effect from which intervention, relative to global evidence—average treatment effects measured at the population level. Comprehensive simulations show the advantages of the proposed method in terms of bias, efficiency and precision in estimating heterogeneous causal effects. The empirically validated method was then used to analyze the National Cancer Database.

Suggested Citation

  • Liangyuan Hu & Jiayi Ji & Hao Liu & Ronald Ennis, 2022. "A Flexible Approach for Assessing Heterogeneity of Causal Treatment Effects on Patient Survival Using Large Datasets with Clustered Observations," IJERPH, MDPI, vol. 19(22), pages 1-6, November.
  • Handle: RePEc:gam:jijerp:v:19:y:2022:i:22:p:14903-:d:970924
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/22/14903/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/19/22/14903/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Liangyuan Hu & Joseph W. Hogan, 2019. "Causal comparative effectiveness analysis of dynamic continuous‐time treatment initiation rules with sparsely measured outcomes and death," Biometrics, The International Biometric Society, vol. 75(2), pages 695-707, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Liangyuan Hu & Lihua Li, 2022. "Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series," IJERPH, MDPI, vol. 19(23), pages 1-13, December.

    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. Liangyuan Hu & Lihua Li, 2022. "Using Tree-Based Machine Learning for Health Studies: Literature Review and Case Series," IJERPH, MDPI, vol. 19(23), pages 1-13, December.

    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:gam:jijerp:v:19:y:2022:i:22:p:14903-:d:970924. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.