References
- Butler, E. L., E. B. Laber, S. M. Davis, and M. R. Kosorok. 2017. Incorporating patient preferences into estimation of optimal individualized treatment rules. Biometrics 74 (1):18–26. doi:https://doi.org/10.1111/biom.12743.
- Cai, T., L. Tian, P. H. Wong, and L. J. Wei. 2011. Analysis of randomized comparative clinical trial data for personalized treatment selections. Biostatistics (Oxford, England) 12 (2):270–82. doi:https://doi.org/10.1093/biostatistics/kxq060.
- Ertefaie, A., T. Wu, K. G. Lynch, and I. Nahum-Shani. 2016. Identifying a set that contains the best dynamic treatment regimes. Biostatistics (Oxford, England) 17 (1):135–48. doi:https://doi.org/10.1093/biostatistics/kxv025.
- Ganesan, A., N. Crum-Cianflone, J. Higgins, J. Qin, C. Rehm, J. Metcalf, C. Brandt, et al. 2011. High dose atorvastatin decreases cellular markers of immune activation without affecting HIV-1 RNA levels: results of a double-blind randomized placebo controlled clinical trial. The Journal of Infectious Diseases 203 (6):756–64. doi:https://doi.org/10.1093/infdis/jiq115.
- Genz, A., F. Bretz, T. Miwa., X. Mi., F. Leisch., F. Scheipl., B. Bornkamp., et al. 2017. mvtnorm: Multivariate Normal and t Distributions, R package version 1.0-3. Accessed on 29 September 2017. https://cran.r-project.org/web/packages/mvtnorm/
- Hristache, M., A. Juditsky, J. Polzehl., and V. Spokoiny. 2001. Structure adaptive approach for dimension reduction. The Annals of Statistics 29:1537–66. doi:https://doi.org/10.1214/aos/1015345954.
- Jones, B., and M. G. Kenward. 2015. Design and analysis of cross-over trials. 3rd ed. New York: Chapman & Hall CRC.
- Kelly, Z. L., A. Michael, S. Butler-Manuel, H. S. Pandha, and R. GL. Morgan. 2011. HOX genes in ovarian cancer. Journal of Ovarian Research 4: 16. doi:https://doi.org/10.1186/1757-2215-4-16.
- Laber, E. B., D. J. Lizotte, and B. Ferguson. 2014. Set-valued dynamic treatment regimes for competing outcomes. Biometrics 70 (1):53–61. doi:https://doi.org/10.1111/biom.12132.
- Liang, H., X. Liu, R. Li, and C.-L. Tsai. 2010 . Estimation and testing for partially linear single index models. The Annals of Statistics 38 (6):3811–36. doi:https://doi.org/10.1214/10-AOS835.
- Lizotte, D. J., M. Bowling, and S. Murphy. 2012. Linear fitted-Q iteration with multiple reward functions. Journal of Machine Learning Research 13 (Nov):3253–95.
- Lizotte, D. J., and E. B. Laber. 2016. Multi-objective Markov decision processes for data-driven decision support. Journal of Machine Learning Research 17:1–28.
- Pihur, V., S. Datta, and S. Datta. 2007. Weighted rank aggregation of cluster validation measures: a Monte Carlo cross-entropy approach. Bioinformatics 23 (13):1607–15. doi:https://doi.org/10.1093/bioinformatics/btm158.
- Pihur, V., S. Datta, and S. Datta. 2009. RankAggreg, an R package for weighted rank aggregation. BMC Bioinformatics 10:62. doi:https://doi.org/10.1186/1471-2105-10-62.
- Qian, M., and S. A. Murphy. 2011. Performance guarantees for individualized treatment rules. The Annals of Statistics 39 (2):1180–210. doi:https://doi.org/10.1214/10-AOS864.
- Schulte, P. J., A. A. Tsiatis, E. B. Laber, and M. Davidian. 2014. Q- and A-learning methods for estimating optimal dynamic treatment regimes. Statistical Science 29 (4):640–61. doi:https://doi.org/10.1214/13-STS450.
- Siriwardhana, C., M. Zhao, S. Datta., and K. B. Kulasekera. 2017a. Personalized plans with multiple treatments. to appear in statistical methods in medical research. Technical Report: http://louisville.edu/sphis/departments/bioinformatics-biostatistics/pdfs/BST2017-01.pdf, Department of Bioinformatics and Biostatistics, the University of Louisville.
- Siriwardhana, C., S. Datta, and K. B. Kulasekera. 2017b Personalized Plans with Multiple Treatments. Technical Report: http://louisville.edu/sphis/departments/bioinformatics-biostatistics/pdfs/BST2017-02.pdf, Department of Bioinformatics and Biostatistics, the University of Louisville.
- Statisticat, L. L. C. 2016. LaplacesDemon: Complete Environment for Bayesian Inference. Bayesian-Inference.com; R package version 16.0.1. Accessed on 29 September 2017. https://cran.r-project.org/web/packages/LaplacesDemon/.
- van’t Veer, L. J., and R. Bernards. 2008. Enabling personalized cancer medicine through analysis of gene-expression patterns. Nature 452:564–70. doi:https://doi.org/10.1038/nature06915.
- Vazquez, A. 2013. Optimization of personalized therapies for anticancer treatment. BMC Systems Biology 7:31. doi:https://doi.org/10.1186/1752-0509-7-31.
- Wand, M. P., and M. C. Jones. 1995. Kernel smoothing. London: Chapman and Hall.
- Yu, Y., and D. Ruppert. 2002. Penalized spline estimation for partially linear single-index models. Journal of the American Statistical Association 97 (460):1042–54. doi:https://doi.org/10.1198/016214502388618861.
- Zhao, Y., D. Zeng, A. J. Rush, and M. R. Kosorok. 2012. Estimating individualized treatment rules using outcome weighted learning. Journal of the American Statistical Association 107 (499):1106–18. doi:https://doi.org/10.1080/01621459.2012.695674.