Journal article
Assessing the properties of patient-specific treatment effect estimates from causal forest algorithms under essential heterogeneity
BMC medical research methodology, Vol.24(1), 66
03/13/2024
DOI: 10.1186/s12874-024-02187-5
PMCID: PMC10935905
PMID: 38481139
Abstract
Treatment variation from observational data has been used to estimate patient-specific treatment effects. Causal Forest Algorithms (CFAs) developed for this task have unknown properties when treatment effect heterogeneity from unmeasured patient factors influences treatment choice - essential heterogeneity.
We simulated eleven populations with identical treatment effect distributions based on patient factors. The populations varied in the extent that treatment effect heterogeneity influenced treatment choice. We used the generalized random forest application (CFA-GRF) to estimate patient-specific treatment effects for each population. Average differences between true and estimated effects for patient subsets were evaluated.
CFA-GRF performed well across the population when treatment effect heterogeneity did not influence treatment choice. Under essential heterogeneity, however, CFA-GRF yielded treatment effect estimates that reflected true treatment effects only for treated patients and were on average greater than true treatment effects for untreated patients.
Patient-specific estimates produced by CFAs are sensitive to why patients in real-world practice make different treatment choices. Researchers using CFAs should develop conceptual frameworks of treatment choice prior to estimation to guide estimate interpretation ex post.
Details
- Title: Subtitle
- Assessing the properties of patient-specific treatment effect estimates from causal forest algorithms under essential heterogeneity
- Creators
- John M Brooks - University of South CarolinaCole G Chapman - Center for Effectiveness Research in Orthopaedics, Greenville, SC, USABrian K Chen - Center for Effectiveness Research in Orthopaedics, Greenville, SC, USASarah B Floyd - Clemson UniversityNeset Hikmet - University of South Carolina
- Resource Type
- Journal article
- Publication Details
- BMC medical research methodology, Vol.24(1), 66
- DOI
- 10.1186/s12874-024-02187-5
- PMID
- 38481139
- PMCID
- PMC10935905
- ISSN
- 1471-2288
- eISSN
- 1471-2288
- Grant note
- name: University of South Carolina Big Data Science Center; name: University of South Caroline Center for Effectiveness Research in Orthopaedics
- Language
- English
- Date published
- 03/13/2024
- Academic Unit
- Pharmacy Practice and Science
- Record Identifier
- 9984573760202771
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