A Contextual Ranking and Selection Method for Personalized Medicine

Published Online:https://doi.org/10.1287/msom.2022.0232

References

  • Alonso D, McKane AJ, Pascual M (2007) Stochastic amplification in epidemics. J. R. Soc. Interface 4(14):575–582.CrossrefGoogle Scholar
  • Audibert JY, Bubeck S, Munos R (2010) Best arm identification in multi-armed bandits. Proceedings of the 23rd Annual Conference on Learning Theory (COLT), 41–53.Google Scholar
  • Bertsimas D, Brown DB, Caramanis C (2011) Theory and applications of robust optimization. SIAM Rev. 53:464–501.CrossrefGoogle Scholar
  • Boyd S, Vandenberghe L (2004) Convex Optimization (Cambridge University Press, New York).CrossrefGoogle Scholar
  • Branke J, Chick SE, Schmidt C (2007) Selecting a selection procedure. Management Sci. 53(11):1916–1932.LinkGoogle Scholar
  • Brennan A, Chick SE, Davies R (2006) A taxonomy of model structures for economic evaluation of health technologies. Health Econom. 15(12):1295–1310.CrossrefGoogle Scholar
  • Cai T, Tian L, Wong PH, Wei LJ (2011) Analysis of randomized comparative clinical trial data for personalized treatment selections. Biostatistics 12(2):270–282.CrossrefGoogle Scholar
  • Carpentier A, Locatelli A (2016) Tight (lower) bounds for the fixed budget best arm identification bandit problem. Conference on Learning Theory, 590–604 (PMLR, New York).Google Scholar
  • Chen CH, Lin J, Yücesan E, Chick SE (2000) Simulation budget allocation for further enhancing the efficiency of ordinal optimization. Discrete Event Dyn. Syst. 10:251–270.CrossrefGoogle Scholar
  • Chick SE, Branke J, Schmidt C (2010) Sequential sampling to myopically maximize the expected value of information. INFORMS J. Comput. 22(1):71–80.LinkGoogle Scholar
  • Chick SE, Mamani H, Simchi-Levi D (2008) Supply chain coordination and influenza vaccination. Oper. Res. 56(6):1493–1506.LinkGoogle Scholar
  • Chick SE, Koopman JS, Soorapanth S, Brown ME (2001) Infection transmission system models for microbial risk assessment. Sci. Total Environ. 274(1):197–207.CrossrefGoogle Scholar
  • Corro Ramos I, Hoogendoorn M, Rutten-van Mölken MP (2020) How to address uncertainty in health economic discrete-event simulation models: An illustration for chronic obstructive pulmonary disease. Medical Decis. Making 40(5):619–632.CrossrefGoogle Scholar
  • Dembo A, Zeitouni O (1998) Large Deviations Techniques and Applications, 2nd ed. (Springer, New York).CrossrefGoogle Scholar
  • Ding L, Hong LJ, Shen H, Zhang X (2022) Knowledge gradient for selection with covariates: Consistency and computation. Naval Res. Logist. 69(3):496–507.CrossrefGoogle Scholar
  • Fan W, Hong LJ, Zhang X (2020) Distributionally robust selection of the best. Management Sci. 66:190–208.LinkGoogle Scholar
  • Frazier PI, Powell WB, Dayanik S (2008) A knowledge-gradient policy for sequential information collection. SIAM J. Control Optim. 47(5):2410–2439.CrossrefGoogle Scholar
  • Gabillon V, Ghavamzadeh M, Lazaric A (2012) Best arm identification: A unified approach to fixed budget and fixed confidence. Adv. Neural Inf. Process. Syst. 25:1–9.Google Scholar
  • Gao S, Chen W, Shi L (2017a) A new budget allocation framework for the expected opportunity cost. Oper. Res. 65:787–803.LinkGoogle Scholar
  • Gao S, Du J, Chen CH (2019) Selecting the optimal system design under covariates. IEEE 15th International Conference on Automation Science and Engineering (CASE) (IEEE, Piscataway, NJ), 547–552.Google Scholar
  • Gao S, Xiao H, Zhou E, Chen W (2017b) Robust ranking and selection with optimal computing budget allocation. Automatica J. IFAC. 81:30–36.CrossrefGoogle Scholar
  • Garnett GP, Cousens S, Hallett TB, Steketee R, Walker N (2011) Mathematical models in the evaluation of health programmes. Lancet 378(9790):515–525.CrossrefGoogle Scholar
  • GLOBOCAN (2019) Cancer over Time. Accessed June 2019, https://gco.iarc.fr/.Google Scholar
  • Goodwin T, Xu J, Celik N, Chen CH (2022) Real-time digital twin-based optimization with predictive simulation learning. J. Simul. 16(6):1–18.CrossrefGoogle Scholar
  • Hamburg MA, Collins FS (2010) The path to personalized medicine. N. Engl. J. Med. 363(4):301–304.CrossrefGoogle Scholar
  • Hao B, Lattimore T, Szepesvari C (2020) Adaptive exploration in linear contextual bandit. Proc. 23rd Internat. Conf. Artificial Intelligence Statist., vol. 108 (PMLR, New York), 3536–3545.Google Scholar
  • Hoogendoorn M, Ramos IC, Baldwin M, Guix NGR, Rutten-van Mölken MP (2019) Broadening the perspective of cost-effectiveness modeling in chronic obstructive pulmonary disease: A new patient-level simulation model suitable to evaluate stratified medicine. Value Health 22(3):313–321.CrossrefGoogle Scholar
  • Hu R, Ludkovski M (2017) Sequential design for ranking response surfaces. SIAM/ASA J. Uncertain. Quantif. 5(1):212–239.CrossrefGoogle Scholar
  • James G, Witten D, Hastie T, Tibshirani R (2013) An Introduction to Statistical Learning, vol. 112 (Springer, New York).CrossrefGoogle Scholar
  • Jedra Y, Proutiere A (2020) Optimal best-arm identification in linear bandits. Adv. Neural Inform. Process. Syst. 33:10007–10017.Google Scholar
  • Kaufmann E, Cappé O, Garivier A (2016) On the complexity of best-arm identification in multi-armed bandit models. J. Machine Learning Res. 17(1):1–42.Google Scholar
  • Kim SH, Nelson BL (2001) A fully sequential procedure for indifference-zone selection in simulation. ACM Trans. Model. Comput. Simul. 11(3):251–273.CrossrefGoogle Scholar
  • Law AM, Kelton WD (2000) Simulation Modeling and Analysis, 3rd ed. (McGraw-Hill, New York).Google Scholar
  • Lee E, Lavieri M, Volk M (2019) Optimal screening for hepatocellular carcinoma: A restless bandit model. Manufacturing. Service Oper. Management 21(1):198–212.LinkGoogle Scholar
  • Levin CE, Sharma M, Olson Z, Verguet S, Shi JF, Wang SM, Qiao YL, Jamison DT, Kim JJ (2015) An extended cost-effectiveness analysis of publicly financed hpv vaccination to prevent cervical cancer in China. Vaccine 33(24):2830–2841.CrossrefGoogle Scholar
  • Li X, Zhang X, Zheng Z (2018) Data-driven ranking and selection: High-dimensional covariates and general dependence. 2018 Winter Simulation Conference (WSC), 1933–1944 (IEEE, Piscataway, NJ).Google Scholar
  • Li Z, Ratliff L, Nassif H, Jamieson K, Jain L (2022) Instance-optimal PAC algorithms for contextual bandits. Adv. Neural Inf. Processing Systems 37590–37603.Google Scholar
  • McLay LA, Foufoulides C, Merrick JRW (2010) Using simulation-optimization to construct screening strategies for cervical cancer. Health Care Management Sci. 13(4):294–318.CrossrefGoogle Scholar
  • Mok TSK (2011) Personalized medicine in lung cancer: What we need to know. Nat. Rev. Clin. Oncol. 8:661–668.CrossrefGoogle Scholar
  • Negoescu D, Bimpikis K, Brandeau M, Iancu D (2018) Dynamic learning of patient response types: An application to treating chronic diseases. Management Sci. 64(8):3469–3488.LinkGoogle Scholar
  • Nelson BL, Swann J, Goldsman D, Song W (2001) Simple procedures for selecting the best simulated system when the number of alternatives is large. Oper. Res. 49(6):950–963.LinkGoogle Scholar
  • Nocedal J, Wright S (2006) Numerical Optimization (Springer Science & Business Media, New York).Google Scholar
  • Pearce M, Branke J (2017) Efficient expected improvement estimation for continuous multiple ranking and selection. 2017 Winter Simulation Conference (WSC) (IEEE, Piscataway, NJ), 2161–2172.Google Scholar
  • Russo D (2020) Simple Bayesian algorithms for best-arm identification. Oper. Res. 68(6):1625–1647.LinkGoogle Scholar
  • Ryzhov IO (2016) On the convergence rates of expected improvement methods. Oper. Res. 64(6):1515–1528.LinkGoogle Scholar
  • Schork NJ (2015) Personalized medicine: Time for one-person trials. Nature 520(7549):609–611.CrossrefGoogle Scholar
  • Shen H, Hong LJ, Zhang X (2021) Ranking and selection with covariates for personalized decision making. INFORMS J. Comput. 33(4):1500–1519.AbstractGoogle Scholar
  • Soare M, Lazaric A, Munos R (2014) Best-arm identification in linear bandits. Adv. Neural Infrom. Process. Syst. 27:828–836.Google Scholar
  • Tan WY (2012) Stochastic Models with Applications to Genetics, Cancers, AIDS and Other Biomedical Systems, volume 19 of Series on Concrete and Applicable Mathematics (World Scientific, Singapore).Google Scholar
  • Tewari A, Murphy SA (2017) From ads to interventions: Contextual bandits in mobile health. Rehg JM, Murphy SA, Kumar S, eds. Mobile Health (Springer, Cham, Switzerland), 495–517.CrossrefGoogle Scholar
  • Thompson M (1982) Regression methods in the comparison of accuracy. Analyst (Lond.). 107(1279):1169–1180.CrossrefGoogle Scholar
  • Westra TA, Rozenbaum MH, Rogoza RM, Nijman HW, Daemen T, Postma MJ, Wilschut JC (2011) Until which age should women be vaccinated against HPV infection? Recommendation based on cost-effectiveness analyses. J. Infect. Dis. 204(3):377–384.CrossrefGoogle Scholar
  • World Health Organization (2003) Making Choices in Health: WHO Guide to Cost-Effectiveness Analysis (World Health Organization, Geneva).Google Scholar
  • World Health Organization (2010) A healthy lifestyle – WHO recommendations. https://www.who.int/europe/news-room/fact-sheets/item/a-healthy-lifestyle---who-recommendations.Google Scholar
  • Zhou Y, Fu MC, Ryzhov IO (2023) Sequential learning with a similarity selection index. Oper. Res., ePub ahead of print May 17, https://doi.org/10.1287/opre.2023.2478.LinkGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.