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Statistical Methods for Dynamic Treatment Regimes Reinforcement Learning Causal Inference and Person
Statistical Methods for Dynamic Treatment Regimes Reinforcement Learning Causal Inference and Person
Published: 2016/09/28
Channel: D. Almunda
Dynamic Treatment Regimes (Bibhas Chakraborty)
Dynamic Treatment Regimes (Bibhas Chakraborty)
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Statistical Methods for Dynamic Treatment Regimes Reinforcement Learning, Causal Inference, and Pers
Statistical Methods for Dynamic Treatment Regimes Reinforcement Learning, Causal Inference, and Pers
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Statistical Methods for Dynamic Treatment Regimes Reinforcement Learning, Causal Inference, and Pers
Statistical Methods for Dynamic Treatment Regimes Reinforcement Learning, Causal Inference, and Pers
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Estimating the Comparative Effectiveness of Longitudinal Treatment Regimes
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Channel: UCSF Epi & Biostats
ICAPS 2015: "Improving the Design and Discovery of Dynamic Treatment Strategies Using Recent ..."
ICAPS 2015: "Improving the Design and Discovery of Dynamic Treatment Strategies Using Recent ..."
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Channel: ICAPS
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Statistics Done Wrong The Woefully Complete Guide 1st Edition
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TOP 5 Hair Loss Treatment Options On The Market | Hair Loss Tips
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DREAM 9 Hackathon Presentations - Thall and Xu
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Markov-switching models in Stata
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WIKIPEDIA ARTICLE

From Wikipedia, the free encyclopedia
  (Redirected from Dynamic treatment regimes)
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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[edit]

References[edit]

  1. ^ Lei, H.; Nahum-Shani, I.; Lynch, K.; Oslin, D.; Murphy, S. A. (2012), "A "SMART" design for building individualized treatment sequences", Annual Review of Clinical Psychology, 8: 21–48, PMC 3887122Freely accessible, PMID 22224838, doi:10.1146/annurev-clinpsy-032511-143152 
  2. ^ 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. 

Further reading[edit]

  • 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 3580802Freely accessible, 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 
  • 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.; 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 
  • 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 
  • 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 
  • 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 
  • 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 
  • 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 2767418Freely accessible, PMID 19750510, doi:10.1002/sim.3720 
  • Zajonc, T. (2010), Bayesian Inference for Dynamic Treatment Regimes: Mobility, Equity, and Efficiency in Student Tracking, SSRN 1689707Freely accessible 

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