Dynamic Learning of Patient Response Types: An Application to Treating Chronic Diseases

Published Online:https://doi.org/10.1287/mnsc.2017.2793

References

  • Ahuja V, Birge J (2016) Response-adaptive designs for clinical trials: Simultaneous learning from multiple patients. Eur. J. Oper. Res. 248(2):619–633.CrossrefGoogle Scholar
  • Almirall D, Compton S, Gunlicks-Stoessel M, Duan N, Murphy S (2012) Designing a pilot sequential multiple assignment randomized trial for developing an adaptive treatment strategy. Statist. Medicine 31(17):1887–1902.CrossrefGoogle Scholar
  • Arias E (2014) United States life tables, 2010. Natl. Vital Statist. Rep. 63(7), http://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_07.pdf.Google Scholar
  • Bank P, Küchler C (2007) On Gittins’ index theorem in continuous time. Stochastic Processes Their Appl. 117(9):1357–1371.CrossrefGoogle Scholar
  • Berry D (1978) Modified two-armed bandit strategies for certain clinical trials. J. Amer. Statist. Assoc. 73(362):339–345.CrossrefGoogle Scholar
  • Berry D, Fristedt B (1985) Bandit Problems: Sequential Allocation of Experiments (Chapman and Hall, London).CrossrefGoogle Scholar
  • Berry D, Pearson L (1985) Optimal designs for clinical trials with dichotomous responses. Statist. Medicine 4(4):497–450.CrossrefGoogle Scholar
  • Bertsimas D, O’Hair A, Relyea S, Silberholz J (2016) An analytics approach to designing combination chemotherapy regimens for cancer. Management Sci. 62(5):1511–1531.LinkGoogle Scholar
  • Boggild M, Palace J, Barton P, Ben-Shlomo Y, Bregenzer T, Dobson C, Gray R (2009) Multiple sclerosis risk sharing scheme: Two year results of clinical cohort study with historical comparator. BMJ 339:b4677.CrossrefGoogle Scholar
  • Bolton P, Harris C (1999) Strategic experimentation. Econometrica 67(2):349–374.CrossrefGoogle Scholar
  • Carroll W (2010) Oral therapy for multiple sclerosis—Sea change or incremental step? New England J. Medicine 362(5):456–458.CrossrefGoogle Scholar
  • Chen C (2015) Google, Biogen seek reasons for advance of multiple sclerosis. Bloomberg (January 27), http://www.bloomberg.com/news/articles/2015-01-27/google-biogen-seek-reasons-for-advance-of-multiple-sclerosis.Google Scholar
  • Cohen A, Solan E (2013) Bandit problems with Lévy processes. Math. Oper. Res. 38(1):92–107.LinkGoogle Scholar
  • Cohen BA, Khan O, Jeffery DR, Bashir K, Rizvi SA, Fox EJ, Agius Met al. (2004) Identifying and treating patients with suboptimal responses. Neurology 63(12, Suppl. 6):S33–S40.CrossrefGoogle Scholar
  • Cohen J, Barkhof F, Comi G, Hartung H, Khatri B, Montalban X, Pelletier Jet al. (2010) Oral fingolimod or intramuscular interferon for relapsing multiple sclerosis. New England J. Medicine 362(5):402–415.CrossrefGoogle Scholar
  • Denton B, Kurt M, Shah N, Bryant S, Smith S (2009) Optimizing the start time of statin therapy for patients with diabetes. Medical Decision Making 29(3):351–367.CrossrefGoogle Scholar
  • Derfuss T (2012) Personalized medicine in multiple sclerosis: Hope or reality? BMC Medicine 10(1):116.CrossrefGoogle Scholar
  • Drummond M (2005) Methods for the Economic Evaluation of Health Care Programmes (Oxford University Press, Oxford, UK).Google Scholar
  • El Karoui N, Karatzas I (1994) Dynamic allocation problems in continuous time. Ann. Appl. Probab. 4(2):255–286.CrossrefGoogle Scholar
  • Gold M (1996) Cost-Effectiveness in Health and Medicine (Oxford University Press, New York).CrossrefGoogle Scholar
  • Harrison JM, Sunar N (2015) Investment timing with incomplete information and multiple means of learning. Oper. Res. 62(2):442–457.LinkGoogle Scholar
  • Hartung DM, Bourdette DN, Ahmed SM, Whitham RH (2015) The cost of multiple sclerosis drugs in the us and the pharmaceutical industry too big to fail? Neurology 84(21):2185–2192.CrossrefGoogle Scholar
  • Helm JE, Lavieri MS, Van Oyen MP, Stein JD, Musch DC (2015) Dynamic forecasting and control algorithms of glaucoma progression for clinician decision support. Oper. Res. 63(5):979–999.LinkGoogle Scholar
  • Horakova D, Kalincik T, Dolezal O, Krasensky J, Vaneckova M, Seidl Z, Havrdova E (2012) Early predictors of non-response to interferon in multiple sclerosis. Acta Neurologica Scandinavica 126(6):390–397.CrossrefGoogle Scholar
  • Hunink M, Weinstein M, Wittenberg E, Drummond M, Pliskin J, Wong J, Glasziou P (2014) Decision Making in Health and Medicine: Integrating Evidence and Values (Cambridge University Press, Cambridge, UK).CrossrefGoogle Scholar
  • Keller G, Rady S (2010) Strategic experimentation with Poisson bandits. Theor. Econom. 5(2):275–311.CrossrefGoogle Scholar
  • Keller G, Rady S (2015) Breakdowns. Theor. Econom. 10(1):175–202.CrossrefGoogle Scholar
  • Keller G, Rady S, Cripps M (2005) Strategic experimentation with exponential bandits. Econometrica 73(1):39–68.CrossrefGoogle Scholar
  • Kremenchutzky M, Rice GPA, Baskerville J, Wingerchuk DM, Ebers GC (2006) The natural history of multiple sclerosis: A geographically based study—9: Observations on the progressive phase of the disease. Brain 129(3):584–594.CrossrefGoogle Scholar
  • Lee S, Baxter D, Limone B, Roberts M, Coleman C (2012) Cost-effectiveness of fingolimod versus interferon beta-1a for relapsing remitting multiple sclerosis in the United States. J. Medical Econom. 15(6):1088–1096.CrossrefGoogle Scholar
  • Mandelbaum A (1987) Continuous multi-armed bandits and multiparameter processes. Ann. Probab. 15(4):1527–1556.CrossrefGoogle Scholar
  • Mariette X, Matucci-Cerinic M, Pavelka K, Taylor P, van Vollenhoven R, Heatley R, Walsh C, Lawson R, Reynolds A, Emery P (2011) Malignancies associated with tumour necrosis factor inhibitors in registries and prospective observational studies: A systematic review and meta-analysis. Ann. Rheumatic Diseases 70(11):1895–1904.CrossrefGoogle Scholar
  • Mason J, Denton B, Shah N, Smith S (2014) Optimizing the simultaneous management of blood pressure and cholesterol for type 2 diabetes patients. Eur. J. Oper. Res. 233(3):727–738.CrossrefGoogle Scholar
  • McIninch J, Datta S, DasMahapatra P, Chiauzzi E, Bhalerao R, Spector A, Goldstein S, Morgan L, Relton J (2015) Remote tracking of walking activity in MS patients in a real-world setting. Neurology 84(14, Suppl. P3.209), https://patientslikeme_posters.s3.amazonaws.com/2015_Remote%20Tracking%20of%20Walking%20Activity% 20in%20MS%20Patients%20in%20Real%20World.pdf.Google Scholar
  • Murphy S (2005) An experimental design for the development of adaptive treatment strategies. Statist. Medicine 24(10):1455–1481.CrossrefGoogle Scholar
  • Murphy SA (2003) Optimal dynamic treatment regimes. J. Roy. Statist. Soc. Ser. B Statist. Methodol. 65(2):331–355.CrossrefGoogle Scholar
  • Murphy SA, Collins LM, Rush AJ (2007) Customizing treatment to the patient: Adaptive treatment strategies. Drug Alcohol Dependence 88(Suppl. 2):S1–S3.CrossrefGoogle Scholar
  • National Multiple Sclerosis Society (2004) Changing therapy in relapsing multiple sclerosis: Considerations and recommendations of a task force of the National Multiple Sclerosis Society. Expert opinion paper, http://www.nationalmssociety.org/NationalMSSociety/media/MSNationalFiles/Brochures/Clinical_Bulletin_Changing-Therapy-in-Relapsing-MS.pdf.Google Scholar
  • National Multiple Sclerosis Society (2008) Disease management consensus statement. Expert opinion paper, http://www.nationalmssociety.org/NationalMSSociety/media/MSNationalFiles/Brochures/ExpOp_Consensus.pdf.Google Scholar
  • National Multiple Sclerosis Society (2017) Disease-modifying therapies for MS. http://stage.nationalmssociety.org/NationalMSSociety/media/MSNationalFiles/Brochures/Brochure-The-MS-Disease-Modifying-Medications.pdf.Google Scholar
  • Noyes K, Bajorska A, Chappel A, Schwid S, Mehta L, Weinstock-Guttman B, Holloway R, Dick A (2011) Cost-effectiveness of disease-modifying therapy for multiple sclerosis: A population-based study. Neurology 77(4):355–363.CrossrefGoogle Scholar
  • Once Weekly Interferon for MS Study Group (OWIMS) (1999) Evidence of interferon β-1a dose response in relapsing-remitting MS: The OWIMS study. Neurology 53(4):679–686.CrossrefGoogle Scholar
  • O’Rourke KE, Hutchinson M (2005) Stopping beta-interferon therapy in multiple sclerosis: An analysis of stopping patterns. Multiple Sclerosis 11(1):46–50.CrossrefGoogle Scholar
  • Phillips CJ (2004) The cost of multiple sclerosis and the cost effectiveness of disease-modifying agents in its treatment. CNS Drugs 18(9):561–574.CrossrefGoogle Scholar
  • Pincus T, Callahan L, Sale W, Brooks A, Payne L, Vaughn W (1984) Severe functional declines, work disability, and increased mortality in seventy-five rheumatoid arthritis patients studied over nine years. Arthritis Rheumatism 27(8):864–872.CrossrefGoogle Scholar
  • Pineau J, Bellemare MG, Rush AJ, Ghizaru A, Murphy SA (2007) Constructing evidence-based treatment strategies using methods from computer science. Drug Alcohol Dependence 88(Suppl. 2):S52–S60.CrossrefGoogle Scholar
  • Powell WB, Ryzhov IO (2012) Optimal Learning (John Wiley & Sons, Hoboken, NJ).CrossrefGoogle Scholar
  • Prosser L, Kuntz K, Bar-Or A, Weinstein M (2003) Patient and community preferences for treatments and health states in multiple sclerosis. Multiple Sclerosis 9(3):311–319.CrossrefGoogle Scholar
  • Prosser L, Kuntz K, Bar-Or A, Weinstein M (2004) Cost-effectiveness of interferon beta-1a, interferon beta-1b, and glatiramer acetate in newly diagnosed non-primary progressive multiple sclerosis. Value Health 7(5):554–568.CrossrefGoogle Scholar
  • Raftery J (2010) Multiple sclerosis risk sharing scheme: A costly failure. BMJ 340:c1672.CrossrefGoogle Scholar
  • Río J, Comabella M, Montalban X (2011) Multiple sclerosis: Current treatment algorithms. Current Opinion Neurol. 24(3):230–237.CrossrefGoogle Scholar
  • Romeo M, Martinelli-Boneschi F, Rodegher M, Esposito F, Martinelli V, Comi G, San Raffaele Multiple Sclerosis Clinical Group (2013) Clinical and MRI predictors of response to interferon-beta and glatiramer acetate in relapsing–remitting multiple sclerosis patients. Eur. J. Neurol. 20(7):1060–1067.CrossrefGoogle Scholar
  • Rovaris M, Comi G, Rocca M, Wolinsky J, Filippi M, European/Canadian Glatiramer Acetate Study Group (2001) Short-term brain volume change in relapsing–remitting multiple sclerosis: Effect of glatiramer acetate and implications. Brain 124(9): 1803–1812.CrossrefGoogle Scholar
  • Rudick R, Stuart W, Calabresi P, Confavreux C, Galetta S, Radue E, Lublin Fet al. (2006) Natalizumab plus interferon beta-1a for relapsing multiple sclerosis. New England J. Medicine 354(9):911–923.CrossrefGoogle Scholar
  • Scalfari A, Neuhaus A, Degenhardt A, Rice G, Muraro P, Daumer M, Ebers G (2010) The natural history of multiple sclerosis, a geographically based study 10: Relapses and long-term disability. Brain 133(7):1914–1929.CrossrefGoogle Scholar
  • Sudlow C, Counsell C (2003) Problems with UK government’s risk sharing scheme for assessing drugs for multiple sclerosis. British Medical J. 326(7385):388–392.CrossrefGoogle Scholar
  • Tappenden P, McCabe C, Chilcott J, Simpson E, Nixon R, Madan J, Fisk JD, Brown M (2009) Cost-effectiveness of disease-modifying therapies in the management of multiple sclerosis for the Medicare population. Value Health 12(5):657–665.CrossrefGoogle Scholar
  • Young P, Olsen L (2010) The Healthcare Imperative: Lowering Costs and Improving Outcomes: Workshop Series Summary (National Academies Press, Washington, DC).Google Scholar
  • Zhang J, Denton B, Balasubramanian H, Shah N, Inman B (2012) Optimization of prostate biopsy referral decisions. Manufacturing Service Oper. Management 14(4):529–547.LinkGoogle Scholar
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