View Item 

Show simple item record

Restricted sub‐tree learning to estimate an optimal dynamic treatment regime using observational data
dc.contributor.authorSpeth, Kelly
dc.contributor.authorWang, Lu
dc.date.accessioned2021-11-02T00:47:11Z
dc.date.available2022-12-01 20:47:10en
dc.date.available2021-11-02T00:47:11Z
dc.date.issued2021-11-20
dc.identifier.citationSpeth, Kelly; Wang, Lu (2021). "Restricted sub‐tree learning to estimate an optimal dynamic treatment regime using observational data." Statistics in Medicine 40(26): 5796-5812.
dc.identifier.issn0277-6715
dc.identifier.issn1097-0258
dc.identifier.urihttps://hdl.handle.net/2027.42/170872
dc.publisherWiley Periodicals, Inc.
dc.publisherNew York, NY
dc.subject.othertree‐based statistical learning
dc.subject.otheradaptive interventions
dc.subject.otherpersonalized medicine
dc.subject.otherrestricted optimization
dc.subject.othertailoring variables
dc.titleRestricted sub‐tree learning to estimate an optimal dynamic treatment regime using observational data
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelStatistics and Numeric Data
dc.subject.hlbsecondlevelPublic Health
dc.subject.hlbsecondlevelMedicine (General)
dc.subject.hlbtoplevelHealth Sciences
dc.subject.hlbtoplevelScience
dc.subject.hlbtoplevelSocial Sciences
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/170872/1/sim9155-sup-0001-supinfo.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/170872/2/sim9155.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/170872/3/sim9155_am.pdf
dc.identifier.doi10.1002/sim.9155
dc.identifier.sourceStatistics in Medicine
dc.identifier.citedreferenceMantovani R, Horvath T, Cerri R, Junior S, Vanschoren J, de Carvalho A. An empirical study on hyperparameter tuning of decision trees. arXiv; 2019:2.
dc.identifier.citedreferenceTsiatis A, Davidian M, Holloway S, Laber E. Dynamic Treatment Regimes: Statistical Methods for Precision Medicine. Monographs on Statistics and Applied Probability. Boca Raton, FL: CRC Press; 2020.
dc.identifier.citedreferenceLaber E, Zhao Y. Tree‐based methods for individualized treatment regimes. Biometrika. 2015; 102 ( 3 ): 501 ‐ 514.
dc.identifier.citedreferenceZhao Y, Zeng D, Rush J, Kosorok M. Estimating individualized treatment rules using outcome weighted learning. J Am Stat Assoc. 2012; 107 ( 449 ): 1106 ‐ 1118.
dc.identifier.citedreferenceZhao Y, Zeng D, Laber E, Kosorok M. New statistical learning methods for estimating optimal dynamic treatment regimes. J Am Stat Assoc. 2015; 110 ( 510 ): 583 ‐ 598.
dc.identifier.citedreferenceZhang B, Tsiatis A, Davidian M, Zhang M, Laber E. Estimating optimal treatment regimes from a classification perspective. Stat. 2012; 1: 103 ‐ 114.
dc.identifier.citedreferenceZhang B, Tsiatis A, Laber E, Davidian M. A robust method for estimating optimal treatment regimes. Biometrics. 2012; 68 ( 4 ): 1010 ‐ 1018.
dc.identifier.citedreferenceZhang Y, Laber E, Davidian M, Tsiatis A. Interpretable dynamic treatment regimes. J Am Stat Assoc. 2018; 113 ( 524 ): 1541 ‐ 1549.
dc.identifier.citedreferenceTao Y, Wang L, Almirall D. Tree‐based Reinforcement Learning for Estimating Optimal Dynamic Treatment Regimes. Ann Appl Stat. 2018; 12 ( 3 ): 1914 ‐ 1938.
dc.identifier.citedreferenceBreiman L, Freidman J, Olshen R, Stone C. Classification and Regression Trees. Belmont, CA: Wadsworth; 1984.
dc.identifier.citedreferenceMarino P. Marino’s the ICU Book. 4th ed. Philadelphia, PA: Wolters Kluver Health/Lippincott Williams and Wilkins; 2014.
dc.identifier.citedreferenceRhodes A, Evans L, Waleed A, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock: 2016. Intensive Care Med. 2017; 45 ( 3 ): 486 ‐ 552.
dc.identifier.citedreferenceRubin D. Estimating causal effects of treatments in randomized and nonrandomized studies. J Educ Psychol. 1974; 66: 688 ‐ 701.
dc.identifier.citedreferenceTao Y, Wang L. Adaptive contrast weighted learning for multi‐stage multi‐treatment decision‐making. Biometrics. 2017; 73: 145 ‐ 155.
dc.identifier.citedreferenceHastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed. New York, NY: Springer; 2009.
dc.identifier.citedreferenceBather J. Decision Theory: An Introduction to Dynamnic Programming and Sequential Decisions. New York, NY: Wiley; 2000.
dc.identifier.citedreferenceBoehmke B, Greenwell B. Decision trees. Hands‐on Machine Learning with R. Boca Raton, FL: CRC Press; 2020.
dc.identifier.citedreferenceTherneau T, Atkinson E. Mayo Foundation. An introduction to recursive partitioning using the RPART routines. CRAN R Netw. 2019; 1 ‐ 60. Accessed June 13 2020. https://cran.r‐project.org/web/packages/rpart/vignettes/longintro.pdf.
dc.identifier.citedreferenceR Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2018.
dc.identifier.citedreferenceJohnson A, Pollard T, Shen L, et al. MIMIC‐III, a freely accessible critical care database. Sci Data. 2016; 3: 160035.
dc.identifier.citedreferenceJohnson A, Stone D, Celi L, Pollard T. The MIMIC Code Repository: enabling reproducibility in critical care research. J Am Med Inform Assoc. 2017; 25 ( 1 ): 32 ‐ 39.
dc.identifier.citedreferencePollard TJ, Johnson AE. The MIMIC‐III clinical database; 2016. https://doi.org/10.13026/C2XW26.
dc.identifier.citedreferenceGoldberger A, Amaral L, Glass L, et al. PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation. 2000; 101 ( 23 ): e215 ‐ e220.
dc.identifier.citedreferenceElixhauser A, Steiner C, Harris D, et al. Comorbidity measures for use with administrative data. Med Care. 1998; 36: 8 ‐ 27.
dc.identifier.citedreferencevan Walraven C, Austin P, Jennings A, et al. A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data. Med Care. 2009; 47: 626 ‐ 633.
dc.identifier.citedreferenceVincent J, Moreno R, Takala J, et al. The SOFA (Sequential Organ Failure Assessment) score to describe organ dysfunction/failure. Intensive Care Med. 1996; 22 ( 7 ): 707 ‐ 710.
dc.identifier.citedreferenceChakraborty B, Moodie E. Statistical Methods for Dynamic Treatment Regimes: Reinforcement Learning, Causal Inference, and Personalized Medicine. Statistics for Biology and Health. Springer.: New York, NY; 2013.
dc.identifier.citedreferenceMurphy S. Optimal dynamic treatment regimes. J R Stat Soc Ser B Stat Methodol. 2003; 65 ( 2 ): 331 ‐ 355.
dc.working.doiNOen
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Name:
sim9155-sup-0001-supinfo.pdf
Size:
268.9KB
Format:
PDF
View/Open
Name:
sim9155.pdf
Size:
863.4KB
Format:
PDF
View/Open
Name:
sim9155_am.pdf
Size:
443.4KB
Format:
PDF
View/Open

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe library materials in a way that respects the people and communities who create, use, and are represented in our collections. Report harmful or offensive language in catalog records, finding aids, or elsewhere in our collections anonymously through our metadata feedback form. More information at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.