Adaptive treatment and robust control.
None
A-learning
anticoagulation
control
misspecification
personalized medicine
robustness
Journal
Biometrics
ISSN: 1541-0420
Titre abrégé: Biometrics
Pays: United States
ID NLM: 0370625
Informations de publication
Date de publication:
03 2021
03 2021
Historique:
received:
04
12
2018
revised:
23
01
2020
accepted:
24
03
2020
pubmed:
7
4
2020
medline:
26
10
2021
entrez:
7
4
2020
Statut:
ppublish
Résumé
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
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
223-236Subventions
Organisme : Engineering and Physical Sciences Research Council
ID : EP/M015637/1
Informations de copyright
© 2020 The International Biometric Society.
Références
Baglin, T.P., Keeling, D.M., Watson, H.G. and the British Committee for Standards in Haematology. (2006) Guidelines on oral anticoagulation (warfarin): third edition-2005 update. British Journal of Haematology, 132, 277-285.
Barrett, J., Henderson, R. and Rosthøj, S. (2014) Doubly robust estimation of optimal dynamic treatment regimes. Statistics in Biosciences, 6, 244-260.
Bekiroglu, K., Lagoa, C., Murphy, S.A. and Lanza, S.T. (2017) Control engineering methods for the design of robust behavioral treatments. IEEE Transactions on Control Systems Technology, 3, 979-990.
Chakrabarty, A., Zavitsanou, S., Doyle, F.J. and Dassau, E. (2018) Event triggered model predictive control for embedded artificial pancreas systems. IEEE Transactions on Biomedical Engineering, 65(3), 575-586 (2018).
Chakraborty, B., Laber, E. and Zhao, Y. (2013) Inference for optimal dynamic treatment regimes using an adaptive m-out-of-n bootstrap scheme. Biometrics, 69, 714-723.
Chakraborty, B. and Moodie, E. (2013) Statistical Methods for Dynamic Treatment Regimes. New York: Springer.
Chen, G., Zeng, D. and Kosorok, M. (2016) Personalized dose finding using outcome weighted learning. Journal of the American Statistical Association, 111, 1509-547.
Deshpande, S., Nandola, N.N., Rivera, D.E. and Younger, J.W. (2014) Optimized treatment of fibbromyalgia using system identification and hybrid model predictive control. Control Engineering Practice, 33, 161-173.
Doyle, J., Glover, K., Khargonekar, P. and Francis, B. (1989) State-space solutions to standard h2 and h-infinity control problems. IEEE Transactions on Automatic Control, 34, 831-847.
El Ghaoui, L. and Lebret, H. (1997) Robust solutions to least-squares problems with uncertain data. SIAM Journal on matrix analysis and applications, 18, 1035-1064.
Glover, K. and Doyle, J. (1988) State-space formulae for all stabilizing controllers that satisfy an h-infinity-norm bound and relations to risk sensitivity. Systems and Control Letters, 11, 167-172.
Henderson, R., Ansell, P. and Alsibani, D. (2010) Regret-regression for optimal dynamic treatment regimes. Biometrics, 66, 1192-1201.
Henderson, R., Ansell, P. and Alsibani, D. (2011) Optimal dynamic treatment methods. Revstat Statistical Journal, 9, 19-36.
Hooker, G., Lin, K. and Rogers, B. (2015) Control theory and experimental design in diffusion processes. SIAM, 3, 234-164.
Laber, E.B., Linn, K.A. and Stefanski, L.A. (2014) Interactive model building for Q-learning. Biometrika, 101, 831-847.
Laber, E. and Zhao, Y. (2015) Tree-based methods for individualized treatment regimes. Biometrika, 102, 501-514.
Linn, K., Laber, E. and Stefanski, L. (2017) Interactive Q-learning for quantiles. Journal of the American Statistical Association, 112, 638-649.
Moodie, E., Dean, N. and Sun, Y. (2014) Q-learning: flexible learning about useful utilities. Statistics in Biosciences, 6, 223-243.
Moodie, E., Richardson, T.S. and Stephens, D. (2007) Demystifying optimal dynamic treatment regimes. Biometrics, 63, 447-455.
Murphy, S.A. (2003) Optimal dynamic treatment regimes. Journal of the Royal Statistical Society Series B, 65, 331-355.
Orellana, J. (2010) Optimal drug scheduling for HIV therapy efficiency improvement. Biomedical signal Processing and Control, 6, 376-386.
Orellana, L., Rotnitzky, A. and Robins, J. (2010) Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes. Part I: main content. International Journal of Biosciences, 6, article 8.
Petersen, M., Deeks, S., Martin, J. and van der Laan, M. (2007) History-adjusted marginal structural models for estimating time-varying effect modification. American Journal of Epidemiology, 166, 985-993.
Pronzato, L. (2008) Optimal experimental design and some related control problems. Automatica, 44, 303-325.
Qian, M. and Murphy, S.A. (2008) Performance guarantees for individualized treatment rules. Annals of Statistics, 27, 1180-1210.
Rich, B., Moodie, E. and Stephens, D. (2014) Simulating sequential multiple assignment randomized trials to generate optimal personalized warfarin dosing strategies. Clinical Trials, 11, 435-444.
Robins, J. (1986) A new approach to causal inference in mortality studies with a sustained exposure period-application to control of the healthy worker survivor effect. Mathematical Modelling, 7, 1393-1512.
Robins, J.M. (2004) Optimal structural nested models for optimal sequential decisions. In: Lin, D.Y. and Heagerty, P. (Eds.) Proceedings of the Second Symposium on Biostatistics. New York: Springer, pp. 189-326.
Rosthøj, S., Fullwood, C., Henderson, R. and Stewart, S. (2006) Estimation of optimal dynamic anticoagulation regimes from observational data: a regret-based approach. Statistics in Medicine, 25, 4197-4215.
Rosthøj, S., Keiding, N. and Schmiegelow, K. (2012) Estimation of dynamic treatment strategies for maintenance therapy of children with acute lymphoblastic leukemia: an application of history-adjusted marginal structural models. Statistics in Medicine, 31, 470-488.
Schulte, P., Tsiatis, A., Laber, E. and Davidian, M. (2014) Q- and A-learning methods for estimating optimal dynamic treatment regimes. Statistical Science, 69, 640-661.
Song, R., Wang, W., Zeng, D. and Kosorok, M. (2015) Penalized Q-learning for dynamic treatment regimens. Statistica Sinica, 25, 901-920.
Sontag, E. (1998) Mathematical Control Theory: Deterministic Finite-Dimensional Systems. New York: Springer.
Sra, S., Nowozin, S. and Wright, S.J. (2012) Optimization for Machine Learning. Cambridge, MA: MIT Press.
Taylor, J. (2004) Environmental test chamber for the support of learning and teaching in intelligent control. International Journal of Electrical Engineering Education, 41, 375-387.
Taylor, J., Young, P. and Chotai, A. (2013) True Digital Control: Statistical Modelling and Non-Minimal State Space Design. Chichester: John Wiley and Sons.
van der Laan, M., Petersen, M. and Joffe, M. (2005) History-adjusted marginal structural models and statically-optimal dynamic treatment regimens. International Journal of Biostatistics, 1. Available at: https://doi.org/10.2202/1557-4679.1003.
Wallace, M. and Moodie, E. (2015) Doubly-robust dynamic treatment regimen estimation via weighted least squares. Biometrics, 71, 636-644.
Wallace, M., Moodie, E. and Stephens, D. (2016) Model assessment in dynamic treatment regimen estimation via double robustness. Biometrics, 72, 855-864.
Wilson, E., Clairon, Q., Henderson, R. and Taylor, J. (2018) Dealing with observational data in control. Annual Reviews in Control, 46, 94-106.
Xu, Y., Müller, P., Wahed, A. and Thall, P. (2016) Bayesian nonparametric estimation for dynamic treatment regimes with sequential transition times. Journal of the American Statistical Association, 111, 921-950.
Zhang, B., Tsiatis, A., Laber, E. and Davidian, M. (2012) A robust method for estimating optimal treatment regimes. Biometrics, 68, 1010-1018.
Zhang, B., Tsiatis, A., Laber, E. and Davidian, M. (2013) Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions. Biometrika, 100, 681-694.
Zhang, S. and Xu, X. (2016) Dynamic analysis and optimal control for a model of hepatitis C with treatment. Commun Nonlinear Sci Numer Simulat, 46, 14-25.
Zhao, Y., Zeng, D., Laber, E., Song, R., Yuan, M. and Kosorok, M. (2015) Doubly robust learning for estimating individualized treatment with censored data. Biometrika, 102, 151-168.
Zhao, Y., Zeng, D., Rush, A. and Kosorok, M. (2012) Estimating individualized treatment rules using outcome weighted learning. Journal of the American Statistical Association, 107, 1106-1118.
Zhou, X., Mayer-Hamblett, N., Khan, U. and Kosorok, M. (2017) Residual weighted learning for estimating individualized treatment rules. Journal of the American Statistical Association, 112, 169-187.