Dynamic treatment regime

In medical research, a dynamic treatment regime (DTR), adaptive intervention, or adaptive treatment strategy is a set of rules for choosing effective treatments for individual patients.^[1] Historically, medical research and the practice of medicine tended to rely on an acute care model for the treatment of all medical problems, including chronic illness.^[2] Treatment choices made for a particular patient under a dynamic regime are based on that individual's characteristics and history, with the goal of optimizing his or her long-term clinical outcome. A dynamic treatment regime is analogous to a policy in the field of reinforcement learning, and analogous to a controller in control theory. While most work on dynamic treatment regimes has been done in the context of medicine, the same ideas apply to time-varying policies in other fields, such as education, marketing, and economics.

See also

• Personalized medicine
• Reinforcement learning
• Q learning
• Optimal control
• Multi-armed bandit
• Susan Murphy, University of Michigan, The Methodology Center

References

Further reading

• Diaz, Francisco J.; Cogollo, Myladis R.; Spina, Edoardo; Santoro, Vincenza; Rendon, Diego M.; Leon, jose de (2012), "Drug Dosage Individualization Based on a Random-Effects Linear Model", Journal of Biopharmaceutical Statistics, 22 (3): 463–484, PMID 22416835, doi:10.1080/10543406.2010.547264 

• Diaz, Francisco J.; Yeh, Hung-Weh; Leon, Jose de (2012), "Role of Statistical Random-Effects Linear Models in Personalized Medicine", Current Pharmacogenomics and Personalized Medicine, 10 (1): 22–32, PMC 3580802 , PMID 23467392, doi:10.2174/1875692111201010022 

• Banerjee, A.; Tsiatis, A. A. (2006), "Adaptive two-stage designs in phase II clinical trials", Statistics in Medicine, 25 (19): 3382–3395, PMID 16479547, doi:10.1002/sim.2501 

• Collins, L. M.; Murphy, S. A.; Nair, V.; Strecher, V. (2005), "A strategy for optimizing and evaluating behavioral interventions", Annals of Behavioral Medicine, 30: 65–73, doi:10.1207/s15324796abm3001_8 

• Guo, X.; Tsiatis, A. A. (2005), "Estimation of survival distributions in two-stage randomization designs with censored data", International Journal of Biostatistics, 1 (1), doi:10.2202/1557-4679.1000 

• Hernán, Miguel A.; Lanoy, Emilie; Costagliola, Dominique; Robins, James M. (2006), "Comparison of Dynamic Treatment Regimes via Inverse Probability Weighting", Basic & Clinical Pharmacology & Toxicology, 98 (3): 237–242, doi:10.1111/j.1742-7843.2006.pto_329.x 

• Lavori, P. W.; Dawson, R. (2000), "A design for testing clinical strategies: biased adaptive within-subject randomization", Journal of the Royal Statistical Society, Series A, 163: 29–38, doi:10.1111/1467-985x.00154 

• Lavori, P.W.; Rush, A.J.; Wisniewski,, S.R.; Alpert, J.; Fava, M.; Kupfer, D.J.; Nierenberg, A.; Quitkin, F.M.; Sacheim, H.A.; Thase, M.E.; Trivedi, M (2001), "Strengthening clinical effectiveness trials: Equipoise-stratified randomization", Biological Psychiatry, 50 (10): 792–801, PMID 11720698, doi:10.1016/s0006-3223(01)01223-9 

• Lavori, P. W.; Dawson, R (2003), "Dynamic treatment regimes: practical design considerations", Clinical Trials, 1 (1): 9–20, PMID 16281458, doi:10.1191/1740774s04cn002oa 

• Lizotte, D. L.; Bowling, M.; Murphy, S. A. (2010), "Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Clinical Trial Analysis" (PDF), Twenty-Seventh Annual International Conference on Machine Learning 

• Lokhnygina, Y; Tsiatis, A. A. (2008), "Optimal two-stage group sequential designs", Journal of Statistical Planning and Inference, 138 (2): 489–499, doi:10.1016/j.jspi.2007.06.011 

• Lunceford, J. K.; Davidian, M.; Tsiatis, A. A. (2002), "Estimation of survival distributions of treatment policies in two-stage randomization designs in clinical trials", Biometrics, 58 (1): 48–57, PMID 11890326, doi:10.1111/j.0006-341x.2002.00048.x 

• Moodie, E. E. M.; Richardson, T. S.; Stephens, D. A. (2007), "Demystifying optimal dynamic treatment regimes", Biometrics, 63 (2): 447–455, PMID 17688497, doi:10.1111/j.1541-0420.2006.00686.x 

• Murphy, Susan A.; van der Laan, M. J.; Robins, James M.; CPPRG; (2001), "Marginal Mean Models for Dynamic Regimes", Journal of the American Statistical Association, 96 (456): 1410–1423, PMC 2794446 , PMID 20019887, doi:10.1198/016214501753382327 

• Murphy, Susan A. (2003), "Optimal Dynamic Treatment Regimes", Journal of the Royal Statistical Society, Series B, 65 (2): 331–366, doi:10.1111/1467-9868.00389 

• Murphy, Susan A. (2005), "An Experimental Design for the Development of Adaptive Treatment Strategies", Statistics in Medicine, 24 (10): 1455–1481, PMID 15586395, doi:10.1002/sim.2022 

• Murphy, Susan A.; Daniel Almiral (2008), "Dynamic Treatment Regimes", Encyclopedia of Medical Decision Making: #–# 

• Nair, V.; Strecher, V.; Fagerlin, A.; Ubel, P.; Resnicow, K.; Murphy, S.; Little, R.; Chakraborty, B.; Zhang, A. (2008), "Screening Experiments and Fractional Factorial Designs in Behavioral Intervention Research", American Journal of Public Health, 98 (8): 1354–1359, doi:10.2105/ajph.2007.127563 

• Orellana, Liliana; Rotnitzky, Andrea; Robins, James M. (2010), "Dynamic Regime Marginal Structural Mean Models for Estimation of Optimal Dynamic Treatment Regimes, Part I: Main Content", The International Journal of Biostatistics, 6 (2), doi:10.2202/1557-4679.1200 

• Orellana, Liliana; Rotnitzky, Andrea; Robins, James M. (2010), "Dynamic Regime Marginal Structural Mean Models for Estimation of Optimal Dynamic Treatment Regimes, Part II: Proofs of Results", The International Journal of Biostatistics, 6 (2), doi:10.2202/1557-4679.1242 

• Robins, James M. (2004), "Optimal structural nested models for optimal sequential decisions", in Lin, D. Y.; Heagerty, P. J., Proceedings of the Second Seattle Symposium on Biostatistics, Springer, New York, pp. 189–326 

• Robins, James M. (1986), "A new approach to causal inference in mortality studies with sustained exposure periods-application to control of the healthy worker survivor effect", Computers and Mathematics with Applications, 14: 1393–1512 

• Robins, James M. (1987), "Addendum to 'A new approach to causal inference in mortality studies with sustained exposure periods-application to control of the healthy worker survivor effect'", Computers and Mathematics with Applications, 14 (9–12): 923–945, doi:10.1016/0898-1221(87)90238-0 

• Rush, A.J.; Trivedi, M.; Fava, M. (2003), "Depression IV: STAR*D treatment trial for depression", American Journal of Psychiatry, 160 (2): 237, PMID 12562566, doi:10.1176/appi.ajp.160.2.237 

• Schneider, L.S.; Tariot, P.N.; Lyketsos, C.G.; Dagerman, K.S.; Davis, K.L.; Davis, S.; Hsiao, J.K.; Jeste, D.V.; Katz, I.R.; Olin, J.T.; Pollock, B.G.; Rabins, P.V.; Rosenheck, R.A.; Small, G.W.; Lebowitz, B.; Lieberman, J.A. (2001), "National Institute of Mental Health clinical antipsychotic trials of intervention effectiveness (CATIE) Alzheimer disease trial methodology", American Journal of Geriatric Psychiatry, 9 (4): 346–360, PMID 11739062 

• Sutton, R. S.; Barto, A. G. (1998), Reinforcement Learning: An Introduction, MIT Press, ISBN 0-262-19398-1 

• van der Laan, M. J.; Robins, James M. (2003), Unified Methods for Censored Longitudinal Data and Causality, Springer-Verlag, ISBN 0-387-95556-9 

• van der Laan, M. J.; Petersen, M. L. (2004), History-Adjusted Marginal Structural Models and Statically-Optimal Dynamic Treatment Regimes 

• Wagner, E. H.; Austin, B. T.; Davis, C.; Hindmarsh, M.; Schaefer, J.; Bonomi, A. (2001), "Improving Chronic Illness Care: Translating Evidence Into Action", Health Affairs, 20 (6): 64–78, PMID 11816692, doi:10.1377/hlthaff.20.6.64 

• Wahed, A.. S.; Tsiatis, A. A. (2004), "Optimal estimator for the survival distribution and related quantities for treatment policies in two-stage randomization designs in clinical trials", Biometrics, 60 (1): 124–133, PMID 15032782, doi:10.1111/j.0006-341X.2004.00160.x 

• Watkins, C. J. C. H. (1989), "Learning from Delayed Rewards", PhD thesis, Cambridge University, Cambridge, England 

• Zhao, Y.; Kosorok, M. R.; Zeng, D. (2009), "Reinforcement learning design for cancer clinical trials", Statistics in Medicine, 28 (26): 3294–3315, PMC 2767418 , PMID 19750510, doi:10.1002/sim.3720 

• Zajonc, T. (2010), Bayesian Inference for Dynamic Treatment Regimes: Mobility, Equity, and Efficiency in Student Tracking, SSRN 1689707