Personalized Dose Finding Using Outcome Weighted Learning

References

• An, T. H., and Tao, P. D. (1997), “Solving a Class of Linearly Constrained Indefinite Quadratic Problems by D. C. Algorithms,” Journal of Global Optimization, 11, 253–285.
   Web of Science ®Google Scholar
• Boyd, S., and Vandenberghe, L. (2004), Convex Optimization (Vol. 25), Cambridge, UK: Cambridge University Press.
   Google Scholar
• Breiman, L. (2001), “Random Forests,” Machine Learning, 45, 5–32.
   Web of Science ®Google Scholar
• Breiman, L., Friedman, J., Stone, C. J., and Olshen, R. A. (1984), Classification and Regression Trees, Boca Raton, FL: CRC Press.
   Google Scholar
• Bühlmann, P., and Hothorn, T. (2007), “Boosting Algorithms: Regularization, Prediction And Model Fitting,” Statistical Science, 22, 477–505.
   Web of Science ®Google Scholar
• Cai, T., Tian, L., Uno, H., and Solomon, S. D. (2010), “Calibrating Parametric Subject-Specific Risk Estimation,” Biometrika, 97, 389–404.
   PubMed Web of Science ®Google Scholar
• Chakraborty, B., and Moodie, E. E. M. (2013), Statistical Methods for Dynamic Treatment Regimes, New York: Springer.
   Google Scholar
• Chevret, S. (2006), Statistical Methods for Dose Finding Experiments, New York: Wiley.
   Google Scholar
• Eagle, K. A., Lim, M. J., Dabbous, O. H., Pieper, K. S., Goldberg, R. J., de Werf, F. V., Goodman, S. G., Granger, C. B., Steg, P. G., Joel, M., Gore, M., Budaj, A., Avezum, A., Flather, M. D., Fox, K. A. A., and GRACE Investigators. (2004), “A Validated Prediction Model for All Forms of Acute Coronary Syndrome: Estimating the Risk of 6-Month Postdischarge Death in an International Registry,” Journal of the American Medical Association, 291, 2727–2733.
   PubMed Web of Science ®Google Scholar
• Eberts, M., and Steinwart, I. (2013), “Optimal Regression Rates for SVMs Using Gaussian Kernels,” Electronic Journal of Statistics, 7, 1–42.
   Web of Science ®Google Scholar
• Friedman, J., Hastie, H., and Tibshirani, R. (2010), “Regularization Paths for Generalized Linear Models via Coordinate Descent,” Journal of Statistical Software, 33, 1–22.
   PubMed Web of Science ®Google Scholar
• Henderson, R., Ansell, P., and Alshibani, D. (2010), “Regret-Regression for Optimal Dynamic Treatment Regimes,” Biometrics, 66, 1192–1201.
   PubMed Web of Science ®Google Scholar
• Holbrook, A. M., Pereira, J. A., Labiris, R., McDonald, H., Douketis, J. D., Crowther, M., and Wells, P. S. (2005), “Systematic Overview of Warfarin and Its Drug and Food Interactions,” Archives of Internal Medicine, 165, 1095–1106.
   PubMed Web of Science ®Google Scholar
• Hu, Y.-H., Wu, F., Lo, C.-L., and Tai, C.-T. (2012), “Predicting Warfarin Dosage From Clinical Data: A Supervised Learning Approach,” Artificial Intelligence in Medicine, 56, 27–34.
   PubMed Web of Science ®Google Scholar
• Hunter, D., and Lange, K. (2004), “A Tutorial on MM Algorithms,” American Statistician, 58, 30–37.
   Web of Science ®Google Scholar
• Imai, K., and Van Dyk, D. A. (2004), “Causal Inference With General Treatment Treatment Regimes: Generalizing the Propensity Score,” Journal of the American Statistical Association, 99, 854–866.
   Web of Science ®Google Scholar
• Karatzoglou, A., Smola, A., Hornik, K., and Zeileis, A. (2004), “Kernlab – An S4 Package for Kernel Methods in R,” Journal of Statistical Software, 11, 1–20.
   Google Scholar
• Kimeldorf, G., and Wahba, G. (1971), “Some Results on Tchebycheffian Spline Functions,” Journal of Mathematical Analysis and Applications, 33, 82–95.
   Web of Science ®Google Scholar
• Laber, E. B., Lizotte, D. J., and Ferguson, B. (2014), “Set-Valued Dynamic Treatment Regimes for Competing Outcomes,” Biometrics, 70, 53–61.
   PubMed Web of Science ®Google Scholar
• Marlowe, D. B., Festinger, D. S., Dugosh, K. L., Lee, P. A., and Benasutti, K. M. (2007), “Adapting Judicial Supervision to the Risk Level of Drug Offenders: Discharge and 6-Month Outcomes From a Prospective Matching Study,” Drug and Alcohol Dependence, 88, S4–S13.
   PubMedGoogle Scholar
• Moodie, E. E. M., Chakraborty, B., and Kramer, M. S. (2012), “Q-Learning for Estimating Optimal Dynamic Treatment Rules From Observational Data,” Canadian Journal of Statistics, 40, 629–645.
   PubMed Web of Science ®Google Scholar
• Moodie, E. E. M., Dean, N., and Sun, Y. R. (2014), “Q-Learning: Flexible Learning About Useful Utilities,” Statistics in Biosciences, 6, 223–243.
   Google Scholar
• Moodie, E. E. M., Platt, R. W., and Kramer, M. S. (2009), “Estimating Response-Maximized Decision Rules With Applications to Breastfeeding,” Journal of the American Statistical Association, 104, 155–165.
   Web of Science ®Google Scholar
• Murphy, S. A. (2003), “Optimal Dynamic Treatment Regimes,” Journal of the Royal Statistical Society, Series B, 65, 331–355.
   Google Scholar
• Qian, M., and Murphy, S. A. (2011), “Performance Guarantees for Individualized Treatment Rules,” The Annals of Statistics, 39, 1180–1210.
   PubMed Web of Science ®Google Scholar
• R Core Team (2013), R: A Language and Environment for Statistical Computing, Vienna: R Foundation for Statistical Computing.
   Google Scholar
• Rich, B., Moodie, E. E. M., and Stephens, D. A. (2014), “Simulating Sequential Multiple Assignment Randomized Trials to Generate Optimal Personalized Warfarin Dosing Strategies,” Clinical Trials, 11, 435–444.
   PubMed Web of Science ®Google Scholar
• Robins, J. M. (2004), “Optimal Structural Nested Models for Optimal Sequential Decisions,” in Proceedings of the Second Seattle Symposium in Biostatistics Analysis of Correlated Data, pp. 189–236.
   Google Scholar
• Rubin, D. B. (1978), “Bayesian Inference for Causal Effects: The Role of Randomization,” The Annals of Statistics, 6, 34–58.
   Web of Science ®Google Scholar
• Schulte, P. J., Tsiatis, A. A., Laber, E. B., and Davidian, M. (2014), “Q- and A-Learning Methods for Estimating Optimal Dynamic Treatment Regimes,” Statistical Science, 29, 640–661.
   PubMed Web of Science ®Google Scholar
• Smola, A. J., and Schölkopf, B. (2004), “A Tutorial on Support Vector Regression,” Statistics and Computing, 14, 199–222.
   Web of Science ®Google Scholar
• Steinwart, I., and Christmann, A. (2008), Support Vector Machines, New York: Springer-Verlag.
   Google Scholar
• Sutton, R. S., and Barto, A. G. (1998), Reinforcement Learning: An Introduction (Vol. 28), Cambridge, MA: MIT Press.
   Google Scholar
• Thall, P. F., and Russell, K. E. (1998), “A Strategy for Dose-Finding and Safety Monitoring Based on Efficacy and Adverse Outcomes in Phase I/II Clinical Trials,” Biometrics, 54, 251–264.
   PubMed Web of Science ®Google Scholar
• The International Warfarin Pharmacogenetics Consortium (2009), “Estimation of the Warfarin Dose With Clinical and Pharmacogenetic Data,” The New England Journal of Medicine, 360, 753–764.
   PubMed Web of Science ®Google Scholar
• Tibshirani, R. (1996), “Regression Shrinkage and Selection via the Lasso,” Journal of the Royal Statistical Society, Series B, 58, 267–288.
   Google Scholar
• Vapnik, V. N. (1995), The Nature of Statistical Learning Theory (Vol. 8), New York: Springer.
   Google Scholar
• Wallace, M. P., and Moodie, E. E. M. (2015), “Doubly-Robust Dynamic Treatment Regimen Estimation via Weighted Least Squares,” Biometrics, 71, 636–644.
   PubMed Web of Science ®Google Scholar
• Zhang, B., Tsiatis, A. A., Davidian, M., Zhang, M., and Laber, E. (2012a), “Estimating Optimal Treatment Regimes From a Classification Perspective,” Stat, 1, 103–114.
   PubMedGoogle Scholar
• Zhang, B., Tsiatis, A. A., Laber, E. B., and Davidian, M. (2012b), “A Robust Method for Estimating Optimal Treatment Regimes,” Biometrics, 68, 1010–1018.
   PubMed Web of Science ®Google Scholar
• Zhang, T. (2004), “Statistical Behavior and Consistency of Classification Methods Based on Convex Risk Minimization,” The Annals of Statistics, 32, 56–85.
   Web of Science ®Google Scholar
• Zhao, Y., Kosorok, M. R., and Zeng, D. (2009), “Reinforcement Learning Design for Cancer Clinical Trials,” Statistics in Medicine, 28, 3294–3315.
   PubMed Web of Science ®Google Scholar
• Zhao, Y., Zeng, D., Rush, J., and Kosorok, M. R. (2012), “Estimating Individualized Treatment Rules Using Outcome Weighted Learning,” Journal of the American Statistical Association, 107, 1106–1118.
   PubMed Web of Science ®Google Scholar
• Zhou, X., Mayer-Hamblett, N., Khan, U., and Kosorok, M. R. (2015), “Residual Weighted Learning for Estimating Individualized Treatment Rules,” Journal of the American Statistical Association, DOI:10.1080/01621459.2015.1093947.
   Web of Science ®Google Scholar
• Zhu, Y., Coffman, D. L., and Ghosh, D. (2015), “A Boosting Algorithm for Estimating Generalized Propensity Scores With Continuous Treatments,” Journal of Causal Inference, 3, 25–40.
   PubMed Web of Science ®Google Scholar