Estimating optimal treatment regimes via subgroup identification in randomized control trials and observational studies

Haoda Fu, Jin Zhou, Douglas E. Faries

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

With new treatments and novel technology available, personalized medicine has become an important piece in the new era of medical product development. Traditional statistics methods for personalized medicine and subgroup identification primarily focus on single treatment or two arm randomized control trials. Motivated by the recent development of outcome weighted learning framework, we propose an alternative algorithm to search treatment assignments which has a connection with subgroup identification problems. Our method focuses on applications from clinical trials to generate easy to interpret results. This framework is able to handle two or more than two treatments from both randomized control trials and observational studies. We implement our algorithm in C++ and connect it with R. Its performance is evaluated by simulations, and we apply our method to a dataset from a diabetes study.

Original languageEnglish (US)
Pages (from-to)3285-3302
Number of pages18
JournalStatistics in Medicine
Volume35
Issue number19
DOIs
StatePublished - Aug 30 2016

Fingerprint

Observational Study
Observational Studies
Subgroup
Precision Medicine
Medicine
Diabetes
Identification Problem
Therapeutics
Product Development
C++
Clinical Trials
Assignment
Learning
Statistics
Technology
Alternatives
Simulation
Framework

Keywords

  • multiple treatments
  • observational studies
  • personalized medicine
  • randomized control trials
  • subgroup identification
  • value function

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

Cite this

Estimating optimal treatment regimes via subgroup identification in randomized control trials and observational studies. / Fu, Haoda; Zhou, Jin; Faries, Douglas E.

In: Statistics in Medicine, Vol. 35, No. 19, 30.08.2016, p. 3285-3302.

Research output: Contribution to journalArticle

@article{1f728d2e4177490a98a275ce4add9e09,
title = "Estimating optimal treatment regimes via subgroup identification in randomized control trials and observational studies",
abstract = "With new treatments and novel technology available, personalized medicine has become an important piece in the new era of medical product development. Traditional statistics methods for personalized medicine and subgroup identification primarily focus on single treatment or two arm randomized control trials. Motivated by the recent development of outcome weighted learning framework, we propose an alternative algorithm to search treatment assignments which has a connection with subgroup identification problems. Our method focuses on applications from clinical trials to generate easy to interpret results. This framework is able to handle two or more than two treatments from both randomized control trials and observational studies. We implement our algorithm in C++ and connect it with R. Its performance is evaluated by simulations, and we apply our method to a dataset from a diabetes study.",
keywords = "multiple treatments, observational studies, personalized medicine, randomized control trials, subgroup identification, value function",
author = "Haoda Fu and Jin Zhou and Faries, {Douglas E.}",
year = "2016",
month = "8",
day = "30",
doi = "10.1002/sim.6920",
language = "English (US)",
volume = "35",
pages = "3285--3302",
journal = "Statistics in Medicine",
issn = "0277-6715",
publisher = "John Wiley and Sons Ltd",
number = "19",

}

TY - JOUR

T1 - Estimating optimal treatment regimes via subgroup identification in randomized control trials and observational studies

AU - Fu, Haoda

AU - Zhou, Jin

AU - Faries, Douglas E.

PY - 2016/8/30

Y1 - 2016/8/30

N2 - With new treatments and novel technology available, personalized medicine has become an important piece in the new era of medical product development. Traditional statistics methods for personalized medicine and subgroup identification primarily focus on single treatment or two arm randomized control trials. Motivated by the recent development of outcome weighted learning framework, we propose an alternative algorithm to search treatment assignments which has a connection with subgroup identification problems. Our method focuses on applications from clinical trials to generate easy to interpret results. This framework is able to handle two or more than two treatments from both randomized control trials and observational studies. We implement our algorithm in C++ and connect it with R. Its performance is evaluated by simulations, and we apply our method to a dataset from a diabetes study.

AB - With new treatments and novel technology available, personalized medicine has become an important piece in the new era of medical product development. Traditional statistics methods for personalized medicine and subgroup identification primarily focus on single treatment or two arm randomized control trials. Motivated by the recent development of outcome weighted learning framework, we propose an alternative algorithm to search treatment assignments which has a connection with subgroup identification problems. Our method focuses on applications from clinical trials to generate easy to interpret results. This framework is able to handle two or more than two treatments from both randomized control trials and observational studies. We implement our algorithm in C++ and connect it with R. Its performance is evaluated by simulations, and we apply our method to a dataset from a diabetes study.

KW - multiple treatments

KW - observational studies

KW - personalized medicine

KW - randomized control trials

KW - subgroup identification

KW - value function

UR - http://www.scopus.com/inward/record.url?scp=84977634231&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84977634231&partnerID=8YFLogxK

U2 - 10.1002/sim.6920

DO - 10.1002/sim.6920

M3 - Article

C2 - 26892174

AN - SCOPUS:84977634231

VL - 35

SP - 3285

EP - 3302

JO - Statistics in Medicine

JF - Statistics in Medicine

SN - 0277-6715

IS - 19

ER -