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

Liangyuan Hu (), Jiayi Ji, Hao Liu and Ronald Ennis
Additional contact information
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
Ronald Ennis: Cancer Institute of New Jersey, Rutgers University, New Brunswick, NJ 07102, USA

IJERPH, 2022, vol. 19, issue 22, 1-6

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.

Keywords: causal inference; survival data analysis; machine learning; treatment effect heterogeneity; clustering (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1660-4601/19/22/14903/pdf (application/pdf)
https://www.mdpi.com/1660-4601/19/22/14903/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jijerp:v:19:y:2022:i:22:p:14903-:d:970924

Access Statistics for this article

IJERPH is currently edited by Ms. Jenna Liu

More articles in IJERPH from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().