Adaptive treatment and robust control
CLAIRON, Quentin
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
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Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
CLAIRON, Quentin
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
< Reduce
Statistics In System biology and Translational Medicine [SISTM]
Bordeaux population health [BPH]
Language
EN
Article de revue
This item was published in
Biometrics. 2020
English Abstract
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 to ...Read more >
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 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 modeling and inference per se. We propose that modeling and estimation should be based on standard statistical techniques but subsequent treatment policy should be obtained from robust control. To bring focus, we concentrate on A‐learning methodology as developed in the biostatistical literature and 𝐻∞‐synthesis from control theory. Simulations and two applications demonstrate robustness of the 𝐻∞ strategy compared to standard A‐learning in the presence of model misspecification or measurement error.Read less <
English Keywords
A‐learning
Anticoagulation
Control
𝐻∞ ‐synthesis
Misspecification
Personalized medicine
Robustness