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.^{[citation needed]}

See also

• Personalized medicine
• Reinforcement learning
• Q learning
• Optimal control
• Multi-armed bandit

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, doi:10.1080/10543406.2010.547264, PMID 22416835
• 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, doi:10.2174/1875692111201010022, PMC 3580802, PMID 23467392
• Banerjee, A.; Tsiatis, A. A. (2006), "Adaptive two-stage designs in phase II clinical trials", Statistics in Medicine, 25 (19): 3382–3395, doi:10.1002/sim.2501, PMID 16479547
• Collins, L. M.; Murphy, S. A.; Nair, V.; Strecher, V. (2005), "A strategy for optimizing and evaluating behavioral interventions", Annals of Behavioral Medicine, 30 (1): 65–73, doi:10.1207/s15324796abm3001_8, PMID 16097907
• 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, PMID 16611197
• 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, doi:10.1016/s0006-3223(01)01223-9, PMID 11720698
• Lavori, P. W.; Dawson, R (2003), "Dynamic treatment regimes: practical design considerations", Clinical Trials, 1 (1): 9–20, doi:10.1191/1740774s04cn002oa, PMID 16281458
• 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, doi:10.1111/j.0006-341x.2002.00048.x, PMID 11890326
• Moodie, E. E. M.; Richardson, T. S.; Stephens, D. A. (2007), "Demystifying optimal dynamic treatment regimes", Biometrics, 63 (2): 447–455, doi:10.1111/j.1541-0420.2006.00686.x, PMID 17688497
• 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, doi:10.1198/016214501753382327, PMC 2794446, PMID 20019887
• 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, hdl:2027.42/74095
• Murphy, Susan A. (2005), "An Experimental Design for the Development of Adaptive Treatment Strategies" (PDF), Statistics in Medicine, 24 (10): 1455–1481, doi:10.1002/sim.2022, hdl:2027.42/39201, PMID 15586395
• 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, PMC 2446451, PMID 18556602
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• Robins, James M. (2004), "Optimal structural nested models for optimal sequential decisions", in Lin, D. Y.; Heagerty, P. J. (eds.), 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
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This page was last edited on 26 March 2024, at 01:21