Combining biomarkers to optimize patient treatment recommendations. (English) Zbl 1299.62125
Summary: Markers that predict treatment effect have the potential to improve patient outcomes. For example, the Oncotype DX\(^\circledR\) RecurrenceScore\(^\circledR\) has some ability to predict the benefit of adjuvant chemotherapy over and above hormone therapy for the treatment of estrogen-receptor-positive breast cancer, facilitating the provision of chemotherapy to women most likely to benefit from it. Given that the score was originally developed for predicting outcome given hormone therapy alone, it is of interest to develop alternative combinations of the genes comprising the score that are optimized for treatment selection. However, most methodology for combining markers is useful when predicting outcome under a single treatment. We propose a method for combining markers for treatment selection which requires modeling the treatment effect as a function of markers. Multiple models of treatment effect are fit iteratively by upweighting or “boosting” subjects potentially misclassified according to treatment benefit at the previous stage. The boosting approach is compared to existing methods in a simulation study based on the change in expected outcome under marker-based treatment. The approach improves upon methods in some settings and has comparable performance in others. Our simulation study also provides insights as to the relative merits of the existing methods. Application of the boosting approach to the breast cancer data, using scaled versions of the original markers, produces marker combinations that may have improved performance for treatment selection.
MSC:
| 62P10 | Applications of statistics to biology and medical sciences; meta analysis |
References:
| [1] | Albain, K., Barlow, W., Ravdin, P., Farrar, W., Burton, G., Ketchel, S., Cobau, C., Levine, E., Ingle, J., Pritchard, K., Lichter, A., Schneider, D., Abeloff, M., Henderson, I., Muss, H., Green, S., Lew, D., Livingston, R., Martino, S., and Osborne, C. (2010a). Adjuvant chemotherapy and timing of tamoxifen in postmenopausal patients with endocrine‐responsive, node‐positive breast cancer: A phase 3, open‐label, randomised controlled trial. The Lancet374, 2055-2063. |
| [2] | Albain, K., Barlow, W., Shak, S., Hortobagyi, G., Livingston, R., Yeh, I., Ravdin, P., Bugarini, R., Baehner, F., Davidson, N., Sledge, G., Winer, E., Hudis, C., Ingle, J., Perez, E., Pritchard, K., Shepherd, L., Gralow, J., Yoshizawa, C., Allred, D., Osborne, C., and Hayes, D. (2010b). Prognostic and predictive value of the 21‐gene recurrence score assay in postmenopausal women with node‐positive, oestrogen‐receptor‐positive breast cancer on chemotherapy: A retrospective analysis of a randomised trial. The Lancet Oncology11, 55-65. |
| [3] | Breiman, L., Friedman, J., Stone, C., and Olshen, R. (1984). Classification and Regression Trees. Wadsworth, Belmont, CA: Chapman & Hall/CRC. · Zbl 0541.62042 |
| [4] | Brinkley, J., Tsiatis, A., and Anstrom, K. (2010). A generalized estimator of the attributable benefit of an optimal treatment regime. Biometrics66, 512-522. · Zbl 1192.62219 |
| [5] | Cai, T., Tian, L., Wong, P., and Wei, L. (2011). Analysis of randomized comparative clinical trial data for personalized treatment selections. Biostatistics12, 270-282. · Zbl 1437.62406 |
| [6] | Chu, N., Ma, L., Liu, P., Hu, Y., and Zhou, M. (2011). A comparative analysis of methods for probability estimation tree. WSEAS Transactions on Computers10, 71-80. |
| [7] | Claggett, B., Zhao, L., Tian, L., Castagno, D., and Wei, L. (2011). Estimating subject‐specific treatment differences for risk‐benefit assessment with competing risk event‐time data. Harvard University Biostatistics Working Paper Series, 125. |
| [8] | Clark, G. (1995). Prognostic and predictive factors for breast cancer. Breast Cancer2, 79-89. |
| [9] | Culp, M., Johnson, K., and Michailidis, G. (2012). ada: An R Package for Stochastic Boosting. R package version 2.0‐3. |
| [10] | Efron, B. and Tibshirani, R. (1993). An Introduction to the Bootstrap, Vol. 57. London: CRC Press. · Zbl 0835.62038 |
| [11] | Etzioni, R., Kooperberg, C., Pepe, M., Smith, R., and Gann, P. (2003). Combining biomarkers to detect disease with application to prostate cancer. Biostatistics4, 523-538. · Zbl 1197.62145 |
| [12] | Foster, J. C., Taylor, J. M. G., and Ruberg, S. J. (2011). Subgroup identification from randomized clinical trial data. Statistics in Medicine30, 2867-2880. |
| [13] | Freund, Y. and Schapire, R. E. (1997). A decision‐theoretic generalization of on‐line learning and an application to boosting. Journal of Computer and System Sciences55, 119-139. · Zbl 0880.68103 |
| [14] | Friedman, J., Hastie, T., and Tibshirani, R. (2000). Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). Annals of Statistics28, 337-407. · Zbl 1106.62323 |
| [15] | Gunter, L., Zhu, J., and Murphy, S. (2007). Variable selection for optimal decision making. Artificial Intelligence in Medicine4594, 149-154. |
| [16] | Gunter, L., Zhu, J., and Murphy, S. (2011a). Variable selection for qualitative interactions. Statistical Methodology8, 42-55. |
| [17] | Gunter, L., Zhu, J., and Murphy, S. (2011b). Variable selection for qualitative interactions in personalized medicine while controlling the family‐wise error rate. Journal of Biopharmaceutical Statistics21, 1063-1078. |
| [18] | Hastie, T., Tibshirani, R., and Friedman, J. (2001). The Elements of Statistical Learning: Data Mining, Inference and Prediction, Vol. 1. New York: Springer Series in Statistics. · Zbl 0973.62007 |
| [19] | Henry, N. and Hayes, D. (2006). Uses and abuses of tumor markers in the diagnosis, monitoring, and treatment of primary and metastatic breast cancer. The Oncologist11, 541-552. |
| [20] | Janes, H., Brown, M., Pepe, M., and Huang, Y. (in press). An approach to evaluating and comparing biomarkers for patient treatment selection. International Journal of Biostatistics. |
| [21] | Janes, H., Pepe, M., and Bossuyt, P. (2014). A framework for evaluating markers used to select patient treatment. Medical Decision Making, 34, 159-167. |
| [22] | Janes, H., Pepe, M., Bossuyt, P., and Barlow, W. (2011). Measuring the performance of markers for guiding treatment decisions. Annals of Internal Medicine154, 253. |
| [23] | Lu, W., Zhang, H., and Zeng, D. (2013). Variable selection for optimal treatment decision. Statistical methods in medical research, 22, 493-504. |
| [24] | Mebane, Jr., W. R. and Sekhon, J. S. (2011). Genetic optimization using derivatives: The rgenoud package for R. Journal of Statistical Software42, 1-26. |
| [25] | Paik, S., Shak, S., Tang, G., Kim, C., Baker, J., Cronin, M., Baehner, F., Walker, M., Watson, D., Park, T., Hiller, W., Fisher, E., Wickerham, D., Bryant, J., and Wolmark, N. (2004). A multigene assay to predict recurrence of tamoxifen‐treated, node‐negative breast cancer. New England Journal of Medicine351, 2817-2826. |
| [26] | Paik, S., Tang, G., Shak, S., Kim, C., Baker, J., Kim, W., Cronin, M., Baehner, F., Watson, D., Bryant, J., Costantino, J., Geyer Jr, C., Wickerham, D., and Wolmark, N. (2006). Gene expression and benefit of chemotherapy in women with node‐negative, estrogen receptor-positive breast cancer. Journal of Clinical Oncology24, 3726-3734. |
| [27] | Pepe, M., Cai, T., and Longton, G. (2005). Combining predictors for classification using the area under the receiver operating characteristic curve. Biometrics62, 221-229. · Zbl 1091.62125 |
| [28] | Provost, F. and Domingos, P. (2003). Tree induction for probability‐based ranking. Machine Learning52, 199-215. · Zbl 1039.68105 |
| [29] | Qian, M. and Murphy, S. (2011). Performance guarantees for individualized treatment rules. Annals of Statistics39, 1180. · Zbl 1216.62178 |
| [30] | Sekhon, J. S. and Mebane, Jr., W. R. (1998). Genetic optimization using derivatives: Theory and application to nonlinear models. Political Analysis7, 189-213. |
| [31] | Song, X. and Pepe, M. (2004). Evaluating markers for selecting a patient’s treatment. Biometrics60, 874-883. · Zbl 1274.62877 |
| [32] | Therneau, T., Atkinson, B., and Ripley, B. (2012). rpart: Recursive Partitioning. R package version 4.0‐1. |
| [33] | Vickers, A., Kattan, M., and Sargent, D. (2007). Method for evaluating prediction models that apply the results of randomized trials to individual patients. Trials8, 14. |
| [34] | Zhang, B., Tsiatis, A., Laber, E., and Davidian, M. (2012). A robust method for estimating optimal treatment regimes. Biometrics68, 1010-1018. · Zbl 1258.62116 |
| [35] | Zhao, X., Dai, W., Li, Y., and Tian, L. (2011). AUC‐based biomarker ensemble with an application on gene scores predicting low bone mineral density. Bioinformatics27, 3050-3055. |
| [36] | Zhao, Y., Zeng, D., Rush, A. J., and Kosorok, M. R. (2012). Estimating individualized treatment rules using outcome weighted learning. Journal of the American Statistical Association107, 1106-1118. · Zbl 1443.62396 |
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