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Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

Titel:	Assessing the properties of patient-specific treatment effect estimates from causal forest algorithms under essential heterogeneity.
Autor/in / Beteiligte Person:	Brooks, JM ; Chapman, CG ; Chen, BK ; Floyd, SB ; Hikmet, N
Link:	Full Text Finder (Volltext)
Zeitschrift:	BMC medical research methodology, Jg. 24 (2024-03-13), Heft 1, S. 66
Veröffentlichung:	London : BioMed Central, [2001-, 2024
Medientyp:	academicJournal
ISSN:	1471-2288 (electronic)
DOI:	10.1186/s12874-024-02187-5
Schlagwort:	Humans Treatment Effect Heterogeneity Causality Patient Selection Computer Simulation Algorithms Patients
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article Language: English [BMC Med Res Methodol] 2024 Mar 13; Vol. 24 (1), pp. 66. <i>Date of Electronic Publication: </i>2024 Mar 13. MeSH Terms: Algorithms* ; Patients* ; Humans ; Treatment Effect Heterogeneity ; Causality ; Patient Selection ; Computer Simulation References: Lancet Digit Health. 2020 Dec;2(12):e677-e680. (PMID: 33328030) ; Eur J Epidemiol. 2020 Dec;35(12):1115-1121. (PMID: 33247797) ; Health Promot J Austr. 2016 Feb;27(3):204-207. (PMID: 29241482) ; Perspect Biol Med. 2005 Autumn;48(4):477-89. (PMID: 16227661) ; Fam Process. 2014 Jun;53(2):336-47. (PMID: 24611528) ; Med Care. 2017 Feb;55(2):94-96. (PMID: 27547948) ; Soc Sci Med. 2018 Aug;210:32-36. (PMID: 29685451) ; BMC Med Res Methodol. 2022 Jul 11;22(1):190. (PMID: 35818028) ; J Hosp Med. 2022 Nov;17(11):912-916. (PMID: 35934981) ; Ann Fam Med. 2006 Mar-Apr;4(2):101-3. (PMID: 16569711) ; J Bone Joint Surg Am. 2021 Mar 17;103(6):549-559. (PMID: 33470590) ; Stat Med. 2018 Oct 15;37(23):3309-3324. (PMID: 29862536) ; Bone Joint J. 2019 Jun;101-B(6):715-723. (PMID: 31154836) ; Int J Epidemiol. 2020 Oct 1;49(5):1495-1497. (PMID: 32743656) ; Eval Rev. 2022 Oct;46(5):652-677. (PMID: 35635044) ; Prev Sci. 2020 Feb;21(2):222-233. (PMID: 31960259) ; J Shoulder Elbow Surg. 2024 Feb;33(2):417-424. (PMID: 37774829) ; Lancet. 2005 Jan 8-14;365(9454):176-86. (PMID: 15639301) ; Am J Epidemiol. 2023 Jul 7;192(7):1155-1165. (PMID: 36843042) ; Am J Drug Alcohol Abuse. 2009;35(4):253-9. (PMID: 20180678) ; Health Aff (Millwood). 2005 Jan-Feb;24(1):80-92. (PMID: 15647218) ; Health Policy. 1998 Oct;46(1):1-19. (PMID: 10187652) ; BMJ. 2018 Dec 10;363:k4245. (PMID: 30530757) ; BMC Musculoskelet Disord. 2014 Jan 14;15:17. (PMID: 24423146) ; Behav Res Methods. 2016 Jun;48(2):650-63. (PMID: 26092391) ; Am J Epidemiol. 2021 Aug 1;190(8):1659-1670. (PMID: 33615349) ; Ann Intern Med. 2020 Jan 7;172(1):W1-W25. (PMID: 31711094) ; JAMA Netw Open. 2020 Jan 3;3(1):e1918663. (PMID: 31922556) ; Qual Life Res. 2016 Apr;25(4):1007-15. (PMID: 26475139) ; Proc Natl Acad Sci U S A. 2016 Jul 5;113(27):7353-60. (PMID: 27382149) ; Med Care. 2007 Oct;45(10 Supl 2):S123-30. (PMID: 17909370) ; Appl Health Econ Health Policy. 2018 Jun;16(3):381-393. (PMID: 29589296) ; Epidemiology. 2007 Sep;18(5):569-72. (PMID: 17700243) ; Psychiatry Res. 2019 Oct;280:112513. (PMID: 31434011) ; Milbank Q. 2004;82(4):661-87. (PMID: 15595946) ; Perspect Biol Med. 2014 Spring;57(2):224-48. (PMID: 25544326) ; Health Econ. 2007 Nov;16(11):1133-57. (PMID: 17910109) ; Clin Ther. 2009 Apr;31(4):902-19. (PMID: 19446162) ; Front Health Serv. 2022 Dec 08;2:1053496. (PMID: 36925811) ; Med Decis Making. 2022 Oct;42(7):923-936. (PMID: 35607982) ; Med Care. 2012 Mar;50(3):217-26. (PMID: 22310560) ; Contemp Clin Trials Commun. 2018 Jun;10:1-8. (PMID: 29682627) ; Int J Epidemiol. 2022 Aug 10;51(4):1047-1053. (PMID: 35696125) ; Science. 2017 Feb 3;355(6324):483-485. (PMID: 28154050) ; Ann Intern Med. 2020 Jan 7;172(1):35-45. (PMID: 31711134) ; Annu Rev Public Health. 2012 Apr;33:425-45. (PMID: 22224891) ; J Comp Eff Res. 2012 Mar;1(2):119-20. (PMID: 24237370) ; Multivariate Behav Res. 2011 May;46(3):399-424. (PMID: 21818162) ; J Pain Symptom Manage. 2016 Sep;52(3):446-52. (PMID: 27220944) ; Proc Natl Acad Sci U S A. 1999 Apr 13;96(8):4730-4. (PMID: 10200330) ; Annu Rev Econom. 2022;14(1):893-923. (PMID: 36741238) ; Med Care. 2017 Feb;55(2):91-93. (PMID: 27547941) ; Health Econ. 2023 Sep;32(9):2113-2126. (PMID: 37303265) ; J Sch Psychol. 2017 Feb;60:7-24. (PMID: 28164801) ; J Clin Epidemiol. 2018 Aug;100:22-31. (PMID: 29654822) ; Stat Biosci. 2011 Sep;3(1):6-27. (PMID: 22010051) ; Am J Epidemiol. 2010 Oct 1;172(7):843-54. (PMID: 20716704) ; Annu Rev Public Health. 2020 Apr 2;41:21-36. (PMID: 31577910) ; Value Health. 2019 May;22(5):587-592. (PMID: 31104739) ; J Econ Lit. 2010 Jun 1;48(2):356-398. (PMID: 21743749) ; Am J Epidemiol. 2014 May 1;179(9):1134-42. (PMID: 24705812) ; Adm Policy Ment Health. 2017 Jul;44(4):558-571. (PMID: 27207372) ; Med Care. 2015 Feb;53(2):e9-e15. (PMID: 23269112) ; Soc Sci Med. 2018 Aug;210:2-21. (PMID: 29331519) ; Health Serv Res. 1998 Dec;33(5 Pt 1):1337-60. (PMID: 9865223) ; JAMA. 2012 Apr 18;307(15):1583-4. (PMID: 22511682) ; Health Serv Res. 2016 Dec;51(6):2375-2394. (PMID: 26891780) ; Int J Epidemiol. 2016 Dec 1;45(6):2184-2193. (PMID: 27864403) ; Health Serv Res. 2019 Dec;54(6):1273-1282. (PMID: 31602641) ; BMC Med Res Methodol. 2020 Oct 23;20(1):264. (PMID: 33096986) Contributed Indexing: Keywords: Causal Forest Algorithm (CFA); Linear probability estimators; Machine learning; Simulation modeling; Treatment effect estimation Entry Date(s): Date Created: 20240314 Date Completed: 20240315 Latest Revision: 20240316 Update Code: 20240316 PubMed Central ID: PMC10935905

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Assessing the properties of patient-specific treatment effect estimates from causal forest algorithms under essential heterogeneity.