An Algorithm for Generating Individualized Treatment Decision Trees and Random Forests
With new treatments and novel technology available, precision medicine has become a key topic in the new era of healthcare. Traditional statistical methods for precision medicine focus on subgroup discovery through identifying interactions between a few markers and treatment regimes. However, given the large scale and high dimensionality of modern data sets, it is difficult to detect the interactions between treatment and high dimensional covariates. Recently, novel approaches have emerged that seek to directly estimate individualized treatment rules (ITR) via maximizing the expected clinical reward by using, for example, support vector machines (SVM) or decision trees. The latter enjoys great popularity in clinical practice due to its interpretability. In this paper, we propose a new reward function and a novel decision tree algorithm to directly maximize rewards. We further improve a single tree decision rule by an ensemble decision tree algorithm, ITR random forests. Our final decision rule is an average over single decision trees and it is a soft probability rather than a hard choice. Depending on how strong the treatment recommendation is, physicians can make decisions based on our model along with their own judgment and experience. Performance of ITR forest and tree methods is assessed through simulations along with applications to a randomized controlled trial (RCT) of 1385 patients with diabetes and an EMR cohort of 5177 patients with diabetes. ITR forest and tree methods are implemented using statistical software R (https://github.com/kdoub5ha/ITR.Forest).
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2018 |
|---|---|
| Erschienen: |
2018 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:27 |
|---|---|
| Enthalten in: |
Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America - 27(2018), 4 vom: 28., Seite 849-860 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Doubleday, Kevin [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Journal Article |
|---|
| Anmerkungen: |
Date Revised 11.11.2023 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.1080/10618600.2018.1451337 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM311026001 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM311026001 | ||
| 003 | DE-627 | ||
| 005 | 20231225141352.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2018 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1080/10618600.2018.1451337 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1036.xml |
| 035 | |a (DE-627)NLM311026001 | ||
| 035 | |a (NLM)32523325 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Doubleday, Kevin |e verfasserin |4 aut | |
| 245 | 1 | 3 | |a An Algorithm for Generating Individualized Treatment Decision Trees and Random Forests |
| 264 | 1 | |c 2018 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 11.11.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a With new treatments and novel technology available, precision medicine has become a key topic in the new era of healthcare. Traditional statistical methods for precision medicine focus on subgroup discovery through identifying interactions between a few markers and treatment regimes. However, given the large scale and high dimensionality of modern data sets, it is difficult to detect the interactions between treatment and high dimensional covariates. Recently, novel approaches have emerged that seek to directly estimate individualized treatment rules (ITR) via maximizing the expected clinical reward by using, for example, support vector machines (SVM) or decision trees. The latter enjoys great popularity in clinical practice due to its interpretability. In this paper, we propose a new reward function and a novel decision tree algorithm to directly maximize rewards. We further improve a single tree decision rule by an ensemble decision tree algorithm, ITR random forests. Our final decision rule is an average over single decision trees and it is a soft probability rather than a hard choice. Depending on how strong the treatment recommendation is, physicians can make decisions based on our model along with their own judgment and experience. Performance of ITR forest and tree methods is assessed through simulations along with applications to a randomized controlled trial (RCT) of 1385 patients with diabetes and an EMR cohort of 5177 patients with diabetes. ITR forest and tree methods are implemented using statistical software R (https://github.com/kdoub5ha/ITR.Forest) | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Optimization | |
| 650 | 4 | |a Precision Medicine | |
| 650 | 4 | |a Recursive Partitioning | |
| 650 | 4 | |a Subgroup Identification | |
| 650 | 4 | |a Value Function | |
| 650 | 4 | |a Variable Importance | |
| 700 | 1 | |a Zhou, Hua |e verfasserin |4 aut | |
| 700 | 1 | |a Fu, Haoda |e verfasserin |4 aut | |
| 700 | 1 | |a Zhou, Jin |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America |d 1998 |g 27(2018), 4 vom: 28., Seite 849-860 |w (DE-627)NLM180962736 |x 1061-8600 |7 nnns |
| 773 | 1 | 8 | |g volume:27 |g year:2018 |g number:4 |g day:28 |g pages:849-860 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1080/10618600.2018.1451337 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 27 |j 2018 |e 4 |b 28 |h 849-860 | ||