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 language | English (US) |
|---|---|
| Pages (from-to) | 3285-3302 |
| Number of pages | 18 |
| Journal | Statistics in Medicine |
| Volume | 35 |
| Issue number | 19 |
| DOIs | |
| State | Published - Aug 30 2016 |
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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 journal › Article
}
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
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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 -