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Original Articles

Estimating the Optimal Personalized Treatment Strategy Based on Selected Variables to Prolong Survival via Random Survival Forest with Weighted Bootstrap

ORCID Icon, , , , &
Pages 362-381 | Received 11 Dec 2016, Accepted 07 Aug 2017, Published online: 25 Oct 2017

References

  • Barbe, P., Bertail, P. (2012). The Weighted Bootstrap, volume 98. New York, NY: Springer Science & Business Media.
  • Biau, G., Devroye, L. (2010). On the layered nearest neighbour estimate, the bagged nearest neighbour estimate and the random forest method in regression and classification. Journal of Multivariate Analysis 101(10):2499–2518.
  • Biau, G., Devroye, L., Lugosi, G. (2008). Consistency of random forests and other averaging classifiers. The Journal of Machine Learning Research 9:2015–2033.
  • Bou-Hamad, I., Larocque, D., Ben-Ameur, H., et al. (2011). A review of survival trees. Statistics Surveys 5:44–71.
  • Breiman, L. (2001). Random forests. Machine Learning 45(1):5–32.
  • Cox, D. R. (1972). Regression models and life tables (with discussion). Journal of the Royal Statistical Society 34:187–220.
  • Dietterich, T. G. (2000). Ensemble methods in machine learning. In J. Kittler and F. Roli (eds) International Workshop on Multiple Classifier Systems, pages 1–15. Springer-Verlag Berlin Heidelberg.
  • Doove, L., Dusseldorp, E., Van Deun, K., Van Mechelen, I. (2014). A comparison of five recursive partitioning methods to find person subgroups involved in meaningful treatment–subgroup interactions. Advances in Data Analysis and Classification 8(4):403–425.
  • Ellsworth, R. E., Decewicz, D. J., Shriver, C. D., Ellsworth, D. L. (2010). Breast cancer in the personal genomics era. Current Genomics 11(3):146–161.
  • Foster, J. C., Taylor, J. M. G., Ruberg, S. J. (2011). Subgroup identification from randomized clinical trial data. Statistics in Medicine 30(24):2867–2880.
  • Genuer, R., Poggi, J.-M., Tuleau-Malot, C. (2010). Variable selection using random forests. Pattern Recognition Letters 31(14):2225–2236.
  • Gordon, L., Olshen, R. A. (1984). Almost surely consistent nonparametric regression from recursive partitioning schemes. Journal of Multivariate Analysis 15(2):147–163.
  • Hothorn, T., Hornik, K., Zeileis, A. (2006). Unbiased recursive partitioning: A conditional inference framework. Journal of Computational and Graphical Statistics 15(3):651–674.
  • Ishigooka, J., Murasaki, M., Miura, S., the Olanzapine Late-Phase II Study Group (2000). Olanzapine optimal dose: Results of an open-label multicenter study in schizophrenic patients. Psychiatry and Clinical Neurosciences 54(4):467–478.
  • Ishwaran, H., Kogalur, U. B., Blackstone, E. H., Lauer, M. S. (2008). Random survival forests. The Annals of Applied Statistics 2(3):841–860.
  • Laber, E. B., Zhao, Y. (2015). Tree-based methods for individualized treatment regimes. Biometrika 102(3):501–514.
  • LeBlanc, M., Crowley, J. (1995). A review of tree-based prognostic models. In Recent Advances in Clinical Trial Design and Analysis, pages 113–124. Boston, MA: Springer.
  • Loh, W.-Y. (2002). Regression tress with unbiased variable selection and interaction detection. Statistica Sinica 12:361–386.
  • Lu, M., Sadiq, S., Feaster, D. J., Ishwaran, H. (2017). Estimating individual treatment effect in observational data using random forest methods. arXiv preprint arXiv:1701.05306.
  • Makarenkov, V., Boc, A., Xie, J., Peres-Neto, P., Lapointe, F.-J., Legendre, P. (2010). Weighted bootstrapping: a correction method for assessing the robustness of phylogenetic trees. BMC Evolutionary Biology 10(1):1.
  • Mogensen, U. B., Ishwaran, H., Gerds, T. A. (2012). Evaluating random forests for survival analysis using prediction error curves. Journal of Statistical Software 50(11):1.
  • Nahorniak, M., Larsen, D. P., Volk, C., Jordan, C. E. (2015). Using inverse probability bootstrap sampling to eliminate sample induced bias in model based analysis of unequal probability samples. PLoS One 10(6):e0131765.
  • Norazan, M., Habshah, M., Imon, A., Chen, S. (2009). Weighted bootstrap with probability in regression. In Proceedings of the 8th WSEAS International Conference on Applied Computer and Applied Computational Science, pages 113–41. Hangzhou, China.
  • Piquette-Miller, M., Grant, D. (2007). The art and science of personalized medicine. Clinical Pharmacology and Therapeutics 81(3):311–315.
  • Qian, M., Murphy, S. A. (2011). Performance guarantees for individualized treatment rules. Annals of Statistics 39(2):1180.
  • Rothwell, P. M. (2005). Subgroup analysis in randomised controlled trials: importance, indications, and interpretation. The Lancet 365(9454):176–186.
  • Scornet, E., Biau, G., Vert, J.-P. (2015). Consistency of random forests. Annals of Statistics 43(4):1716–1741.
  • Shen, J., Wang, L., Taylor, J. M. G. (2017). Estimation of the optimal regime in treatment of prostate cancer recurrence from observational data using flexible weighting models. Biometrics 73(2):635–645.
  • Strobl, C., Malley, J., Tutz, G. (2009). An introduction to recursive partitioning: rationale, application, and characteristics of classification and regression trees, bagging, and random forests. Psychological Methods 14(4):323.
  • Wager, S., Athey, S. (2017). Estimation and inference of heterogeneous treatment effects using random forests. Journal of the American Statistical Association (just-accepted).
  • Wager, S., Hastie, T., Efron, B. (2014). Confidence intervals for random forests: the jackknife and the infinitesimal jackknife. Journal of Machine Learning Research 15(1):1625–1651.
  • Xu, R., Nettleton, D., Nordman, D. J. (2016). Case-specific random forests. Journal of Computational and Graphical Statistics 25(1):49–65.
  • Zhang, B., Tsiatis, A. A., Laber, E. B., Davidian, M. (2012). A robust method for estimating optimal treatment regimes. Biometrics 68(4):1010–1018.
  • Zhao, Y., Zeng, D., Laber, E. B., Song, R., Yuan, M., Kosorok, M. R. (2015). Doubly robust learning for estimating individualized treatment with censored data. Biometrika 102(1):151.
  • Zhao, Y., Zeng, D., Rush, A. J., Kosorok, M. R. (2012). Estimating individualized treatment rules using outcome weighted learning. Journal of the American Statistical Association 107(499):1106–1118.
  • Zhu, R., Kosorok, M. R. (2012). Recursively imputed survival trees. Journal of the American Statistical Association 107(497):331–340.

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