Rights statement: This is the peer reviewed version of the following article: Clairon, Q, Henderson, R, Young, NJ, Wilson, ED, Taylor, CJ. Adaptive treatment and robust control. Biometrics. 2020; 1– 14. https://doi.org/10.1111/biom.13268 which has been published in final form at https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13268 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - Adaptive treatment and robust control
AU - Clairon, Quentin
AU - Henderson, Robin
AU - Young, N
AU - Wilson, Emma
AU - Taylor, C. James
N1 - This is the peer reviewed version of the following article: Clairon, Q, Henderson, R, Young, NJ, Wilson, ED, Taylor, CJ. Adaptive treatment and robust control. Biometrics. 2020; 1– 14. https://doi.org/10.1111/biom.13268 which has been published in final form at https://onlinelibrary.wiley.com/doi/abs/10.1111/biom.13268 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
PY - 2021/3/30
Y1 - 2021/3/30
N2 - A control theory perspective on determination of optimal dynamic treatment regimes is considered. The aim is to adapt statistical methodology that has been developed for medical or other biostatistical applications so as to incorporate powerful control techniques that have been designed for engineering or other technological problems. Data tend to be sparse and noisy in the biostatistical area and interest has tended to be in statistical inference for treatment effects. In engineering fields, experimental data can be more easily obtained and reproduced and interest is more often in performance and stability of proposed controllers rather than modelling and inference per se. We propose that modelling and estimation be based on standard statistical techniques but subsequent treatment policy be obtained from robust control. To bring focus, we concentrate on A-learning methodology as developed in the biostatistical literature and h-infinity synthesis from control theory. Simulations and two applications demonstrate robustness of the h-infinity strategy compared to standard A-learning in the presence of model misspecification or measurement error.
AB - A control theory perspective on determination of optimal dynamic treatment regimes is considered. The aim is to adapt statistical methodology that has been developed for medical or other biostatistical applications so as to incorporate powerful control techniques that have been designed for engineering or other technological problems. Data tend to be sparse and noisy in the biostatistical area and interest has tended to be in statistical inference for treatment effects. In engineering fields, experimental data can be more easily obtained and reproduced and interest is more often in performance and stability of proposed controllers rather than modelling and inference per se. We propose that modelling and estimation be based on standard statistical techniques but subsequent treatment policy be obtained from robust control. To bring focus, we concentrate on A-learning methodology as developed in the biostatistical literature and h-infinity synthesis from control theory. Simulations and two applications demonstrate robustness of the h-infinity strategy compared to standard A-learning in the presence of model misspecification or measurement error.
KW - A-learning
KW - anticoagulation
KW - control
KW - h-infinity synthesis
KW - misspecification
KW - personalized medicine
KW - robustness
U2 - 10.1111/biom.13268
DO - 10.1111/biom.13268
M3 - Journal article
VL - 77
SP - 223
EP - 236
JO - Biometrics
JF - Biometrics
SN - 0006-341X
IS - 1
ER -