治療効果が顕著なサブグループを抽出するための境界内平均生存時間に基づく生存時間Bump Hunting法の開発
医学研究では, リアル・ワールド・データ (real world data) に代表されるビッグデータを用いて, 新規治療が既存治療に比べて顕著に有効な部分集団を抽出する方法, すなわち, サブグループ抽出法 (Subgroup identification method) が注目されている. これらの研究では, CART (Classification and Regression Trees, Breiman et al., 1984) などのプロダクション・ルールで解釈可能なモデルに基づいて提案されているものが多く, 生存時間データに対する方法も例外ではない. 例えば, Negassa...
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| Published in | 計算機統計学 Vol. 33; no. 1; pp. 1 - 28 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | Japanese |
| Published |
日本計算機統計学会
2020
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0914-8930 2189-9789 |
| DOI | 10.20551/jscswabun.33.1_1 |
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| Abstract | 医学研究では, リアル・ワールド・データ (real world data) に代表されるビッグデータを用いて, 新規治療が既存治療に比べて顕著に有効な部分集団を抽出する方法, すなわち, サブグループ抽出法 (Subgroup identification method) が注目されている. これらの研究では, CART (Classification and Regression Trees, Breiman et al., 1984) などのプロダクション・ルールで解釈可能なモデルに基づいて提案されているものが多く, 生存時間データに対する方法も例外ではない. 例えば, Negassa et al. (2005) は, 比例ハザード・モデルの治療×共変量の交互作用に対して樹木モデルを仮定し, RECPAM (Ciampi et al., 1988, 1991; Ciampi & Thiffault, 1988) のアルゴリズムのもとでモデルを構築している. また, Kehl & Ulm (2006) は, Bump Huntingの1つであるPRIM法 (Patient Rule Induction Method; Friedman & Fisher, 1999) に基づいて新治療に対するresponderを抽出している (以下, SPRIM法と呼ぶ). さらに, Lipkovich et al. (2011) は, ルール・ベースでの再帰的アルゴリズムを用いてサブグループを抽出する方法, すなわち, SIDES (Subgroup Identification based on Differential Effect Search) 法を提案している. ただし, これらの方法では, サブグループ内における新治療と既存治療の比例ハザード性を仮定しなければならない. 一方で, 免疫チェックポイント阻害剤などの臨床試験では, 治療群間の比例ハザード性の仮定を満たさないことは広く知られている. 本論文では, 比例ハザード性の仮定を満たさない場合にも適用可能なBump Hunting法を提案した. そこでは, 治療効果の評価基準にサブグループの境界内平均生存時間 (RMST : Restricted Mean Survival Time) の差を評価基準に用いた. RMSTに基づく生存時間PRIM法の有用性を文献事例により提示し, 性能を数値検証により評価した. その結果, RMSTに基づく生存時間PRIM法はSPRIM法およびSIDES法に比べてRMSTが高いサブグループを適切に捉えることが示された. |
|---|---|
| AbstractList | 医学研究では, リアル・ワールド・データ (real world data) に代表されるビッグデータを用いて, 新規治療が既存治療に比べて顕著に有効な部分集団を抽出する方法, すなわち, サブグループ抽出法 (Subgroup identification method) が注目されている. これらの研究では, CART (Classification and Regression Trees, Breiman et al., 1984) などのプロダクション・ルールで解釈可能なモデルに基づいて提案されているものが多く, 生存時間データに対する方法も例外ではない. 例えば, Negassa et al. (2005) は, 比例ハザード・モデルの治療×共変量の交互作用に対して樹木モデルを仮定し, RECPAM (Ciampi et al., 1988, 1991; Ciampi & Thiffault, 1988) のアルゴリズムのもとでモデルを構築している. また, Kehl & Ulm (2006) は, Bump Huntingの1つであるPRIM法 (Patient Rule Induction Method; Friedman & Fisher, 1999) に基づいて新治療に対するresponderを抽出している (以下, SPRIM法と呼ぶ). さらに, Lipkovich et al. (2011) は, ルール・ベースでの再帰的アルゴリズムを用いてサブグループを抽出する方法, すなわち, SIDES (Subgroup Identification based on Differential Effect Search) 法を提案している. ただし, これらの方法では, サブグループ内における新治療と既存治療の比例ハザード性を仮定しなければならない. 一方で, 免疫チェックポイント阻害剤などの臨床試験では, 治療群間の比例ハザード性の仮定を満たさないことは広く知られている. 本論文では, 比例ハザード性の仮定を満たさない場合にも適用可能なBump Hunting法を提案した. そこでは, 治療効果の評価基準にサブグループの境界内平均生存時間 (RMST : Restricted Mean Survival Time) の差を評価基準に用いた. RMSTに基づく生存時間PRIM法の有用性を文献事例により提示し, 性能を数値検証により評価した. その結果, RMSTに基づく生存時間PRIM法はSPRIM法およびSIDES法に比べてRMSTが高いサブグループを適切に捉えることが示された. |
| Author | 下川, 敏雄 万, 可 南, 弘征 水田, 正弘 谷岡, 健資 |
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| References | Schumacher, M., Bastert, G., Bojar, H., Hübner, K., Olschewski, M., Sauerbrei, W., Schmoor, C., Beyerle, C., Neumann, R. L. & Rauschecker, H. F. (1994). Randomized 2 × 2 trial evaluating hormonal treatment and the duration of chemotherapy in node-positive breast cancer patients. German Breast Cancer Study Group. Journal of Clinical Oncology, 12, 2086-2093. Su, X., Tsai, C.-L., Wang, H., Nickerson, D. M. & Li, B. (2009). Subgroup analysis via recursive partitioning. Journal of Machine Learning Research, 10, 141-158. Friedman, J. H. & Fisher, N. I. (1999). Bump hunting for high-dimensional data. Statistics and Computing, 9, 123-143. 下川敏雄・杉本知之・後藤昌司 (2013). 樹木構造接近法. 共立出版. Ciampi, A. & Thiffault, J. (1988). Recursive partition and amalgamation (RECPAM) for censored survival data : Criteria for tree selection. Statistical Software Newsletter, 14, 78-81. Tian, L., Zhao, L. & Wei, L. J. (2014). Predicting the restricted mean event time with the subject's baseline covariates in survival analysis. Biostatistics, 15, 222-233. Zhang, P., Ma, J., Chen, X. & Shentu, Y. (2020). A nonparametric method for value function guided subgroup identification via gradient tree boosting for censored survival data. Statistics in Medicine, 39, 4133-4146. Negassa, A., Ciampi, A., Abrahamowicz, M., Shapiro, S. & Boivin, J.-F. (2005). Tree-structured subgroup analysis for censored survival data : Validation of computationally inexpensive model selection criteria. Statistics and Computing, 15, 231-239. Xu, Y., Yu, M., Zhao, Y.-Q., Li, Q., Wang, S. & Shao, J. (2015). Regularized outcome weighted subgroup identification for differential treatment effects. Biometrics, 71, 645-653. Li, J., Zhao, L., Tian, L., Cai, T., Claggett, B., Callegaro, A., Dizier, B., Spiessens, B., Ulloa-Montoya, F. & Wei, L.-J. (2016). A predictive enrichment procedure to identify potential responders to a new therapy for randomized, comparative controlled clinical studies. Biometrics, 72, 877-887. Chen, S., Tian, L., Cai, T. & Yu, M. (2017). A general statistical framework for subgroup identification and comparative treatment scoring. Biometrics, 73, 1199-1209. Lee, E. T. (1992). Statistical Methods for Survival Data Analysis, Second Edition. Wiley. LeBlanc, M. & Crowley, J. (1993). Survival trees by goodness of split. Journal of the American Statistical Association, 88, 457-467. Ciampi, A., Hogg, S., McKinney, S. & Thiffault, J. (1988). RECPAM : A computer program for recursive partition and amalgamation for censored survival data and other situations frequently occurring in biostatistics. I. Methods and program features. Computer Methods and Programs in Biomedicine, 26, 239-256. Breiman, L., Friedman, J. H., Olshen, R. A. & Stone, C. J. (1984). Classification and Regression Trees. Wadsworth. LeBlanc, M., Jacobson, J. & Crowley, J. (2002). Partitioning and peeling for constructing prognostic groups. Statistical Methods in Medical Research, 11, 247-274. Freidlin, B., Mcshane, L. M. & Korn, E. L. (2010). Randomized clinical trials with biomarkers : Design issues. Journal of the National Cancer Institute, 102, 152-160. Kehl, V. & Ulm, K. (2006). Responder identification in clinical trials with censored data. Computational Statistics & Data Analysis, 50, 1338-1355. Dusseldorp, E. & van Mechelen, I. (2014). Qualitative interaction trees : a tool to identify qualitative treatment-subgroup interactions. Statistics in Medicine, 33, 219-237. Dazard, J.-E., Choe, M., LeBlanc, M. & Rao, J. S. (2016). Cross-validation and peeling strategies for survival bump hunting using recursive peeling methods. Statistical Analysis and Data Mining, 9, 12-42. Foster, J. C., Taylor, J. M. G. & Ruberg, S. J. (2011). Subgroup identification from randomized clinical trial data. Statistics in Medicine, 30, 2867-2880. Lipkovich, I. & Dmitrienko, A. (2014). Biomarker identification in clinical trials. Clinical and Statistical Considerations in Personalized Medicine (Carini, C., Menon, S. M. & Chang, M. (Eds.)), Chapman and Hall/CRC, 211-264. Schnell, P. M., Tang, Q., Offen, W. W. & Carlin, B. P. (2016). A Bayesian credible subgroups approach to identifying patient subgroups with positive treatment effects. Biometrics, 72, 1026-1036. Imai, K. & Ratkovic, M. (2013). Estimating treatment effect heterogeneity in randomized program evaluation. Annals of Applied Statistics, 7, 443-470. Ngo, D., Baumgartner, R., Mt-lsa, S., Feng, D., Chen, J. & Schnell, P. (2020). Bayesian credible subgroup identification for treatment effectiveness in time-to-event data. PLoS ONE, https://doi.org/10.1371/journal.pone.0229336 (閲覧日 : 2020年3月6日) Lipkovich, I., Dmitrienko, A., Denne, J. & Enas, G. (2011). Subgroup identification based on differential effect search—A recursive partitioning method for establishing response to treatment in patient subpopulations. Statistics in Medicine, 30, 2601-2621. Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32. Ciampi, A., Lou, Z., Lin, Q. & Negassa, A. (1991). Recursive partition and amalgamation with the exponential family : Theory and applications. Applied Stochastic Models and Data Analysis, 7, 121-137. Delmar, P., Irl, C. & Tian, L. (2017). Innovative methods for the identification of predictive biomarker signatures in oncology : Application to bevacizumab. Contemporary Clinical Trials Communications, 5, 107-115. Hothorn, T., Hornik, K. & Zeileis, A. (2006). Unbiased recursive partitioning : A conditional inference framework. Journal of Computational and Graphical Statistics, 15, 651-674. Qian, M. & Murphy, S. A. (2011). Performance guarantees for individualized treatment rules. Annals of Statistics, 39, 1180-1210. 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, 1106-1118. |
| References_xml | – reference: LeBlanc, M., Jacobson, J. & Crowley, J. (2002). Partitioning and peeling for constructing prognostic groups. Statistical Methods in Medical Research, 11, 247-274. – reference: Su, X., Tsai, C.-L., Wang, H., Nickerson, D. M. & Li, B. (2009). Subgroup analysis via recursive partitioning. Journal of Machine Learning Research, 10, 141-158. – reference: Zhang, P., Ma, J., Chen, X. & Shentu, Y. (2020). A nonparametric method for value function guided subgroup identification via gradient tree boosting for censored survival data. Statistics in Medicine, 39, 4133-4146. – reference: Ciampi, A., Lou, Z., Lin, Q. & Negassa, A. (1991). Recursive partition and amalgamation with the exponential family : Theory and applications. Applied Stochastic Models and Data Analysis, 7, 121-137. – reference: Qian, M. & Murphy, S. A. (2011). Performance guarantees for individualized treatment rules. Annals of Statistics, 39, 1180-1210. – reference: Ciampi, A., Hogg, S., McKinney, S. & Thiffault, J. (1988). RECPAM : A computer program for recursive partition and amalgamation for censored survival data and other situations frequently occurring in biostatistics. I. Methods and program features. Computer Methods and Programs in Biomedicine, 26, 239-256. – reference: 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, 1106-1118. – reference: Hothorn, T., Hornik, K. & Zeileis, A. (2006). Unbiased recursive partitioning : A conditional inference framework. Journal of Computational and Graphical Statistics, 15, 651-674. – reference: Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32. – reference: Imai, K. & Ratkovic, M. (2013). Estimating treatment effect heterogeneity in randomized program evaluation. Annals of Applied Statistics, 7, 443-470. – reference: Schumacher, M., Bastert, G., Bojar, H., Hübner, K., Olschewski, M., Sauerbrei, W., Schmoor, C., Beyerle, C., Neumann, R. L. & Rauschecker, H. F. (1994). Randomized 2 × 2 trial evaluating hormonal treatment and the duration of chemotherapy in node-positive breast cancer patients. German Breast Cancer Study Group. Journal of Clinical Oncology, 12, 2086-2093. – reference: Li, J., Zhao, L., Tian, L., Cai, T., Claggett, B., Callegaro, A., Dizier, B., Spiessens, B., Ulloa-Montoya, F. & Wei, L.-J. (2016). A predictive enrichment procedure to identify potential responders to a new therapy for randomized, comparative controlled clinical studies. Biometrics, 72, 877-887. – reference: Ciampi, A. & Thiffault, J. (1988). Recursive partition and amalgamation (RECPAM) for censored survival data : Criteria for tree selection. Statistical Software Newsletter, 14, 78-81. – reference: Lipkovich, I., Dmitrienko, A., Denne, J. & Enas, G. (2011). Subgroup identification based on differential effect search—A recursive partitioning method for establishing response to treatment in patient subpopulations. Statistics in Medicine, 30, 2601-2621. – reference: Negassa, A., Ciampi, A., Abrahamowicz, M., Shapiro, S. & Boivin, J.-F. (2005). Tree-structured subgroup analysis for censored survival data : Validation of computationally inexpensive model selection criteria. Statistics and Computing, 15, 231-239. – reference: Schnell, P. M., Tang, Q., Offen, W. W. & Carlin, B. P. (2016). A Bayesian credible subgroups approach to identifying patient subgroups with positive treatment effects. Biometrics, 72, 1026-1036. – reference: Friedman, J. H. & Fisher, N. I. (1999). Bump hunting for high-dimensional data. Statistics and Computing, 9, 123-143. – reference: Dazard, J.-E., Choe, M., LeBlanc, M. & Rao, J. S. (2016). Cross-validation and peeling strategies for survival bump hunting using recursive peeling methods. Statistical Analysis and Data Mining, 9, 12-42. – reference: Freidlin, B., Mcshane, L. M. & Korn, E. L. (2010). Randomized clinical trials with biomarkers : Design issues. Journal of the National Cancer Institute, 102, 152-160. – reference: Lee, E. T. (1992). Statistical Methods for Survival Data Analysis, Second Edition. Wiley. – reference: Chen, S., Tian, L., Cai, T. & Yu, M. (2017). A general statistical framework for subgroup identification and comparative treatment scoring. Biometrics, 73, 1199-1209. – reference: Ngo, D., Baumgartner, R., Mt-lsa, S., Feng, D., Chen, J. & Schnell, P. (2020). Bayesian credible subgroup identification for treatment effectiveness in time-to-event data. PLoS ONE, https://doi.org/10.1371/journal.pone.0229336 (閲覧日 : 2020年3月6日) – reference: Kehl, V. & Ulm, K. (2006). Responder identification in clinical trials with censored data. Computational Statistics & Data Analysis, 50, 1338-1355. – reference: Lipkovich, I. & Dmitrienko, A. (2014). Biomarker identification in clinical trials. Clinical and Statistical Considerations in Personalized Medicine (Carini, C., Menon, S. M. & Chang, M. (Eds.)), Chapman and Hall/CRC, 211-264. – reference: 下川敏雄・杉本知之・後藤昌司 (2013). 樹木構造接近法. 共立出版. – reference: Breiman, L., Friedman, J. H., Olshen, R. A. & Stone, C. J. (1984). Classification and Regression Trees. Wadsworth. – reference: Foster, J. C., Taylor, J. M. G. & Ruberg, S. J. (2011). Subgroup identification from randomized clinical trial data. Statistics in Medicine, 30, 2867-2880. – reference: Dusseldorp, E. & van Mechelen, I. (2014). Qualitative interaction trees : a tool to identify qualitative treatment-subgroup interactions. Statistics in Medicine, 33, 219-237. – reference: LeBlanc, M. & Crowley, J. (1993). Survival trees by goodness of split. Journal of the American Statistical Association, 88, 457-467. – reference: Xu, Y., Yu, M., Zhao, Y.-Q., Li, Q., Wang, S. & Shao, J. (2015). Regularized outcome weighted subgroup identification for differential treatment effects. Biometrics, 71, 645-653. – reference: Tian, L., Zhao, L. & Wei, L. J. (2014). Predicting the restricted mean event time with the subject's baseline covariates in survival analysis. Biostatistics, 15, 222-233. – reference: Delmar, P., Irl, C. & Tian, L. (2017). Innovative methods for the identification of predictive biomarker signatures in oncology : Application to bevacizumab. Contemporary Clinical Trials Communications, 5, 107-115. |
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| Title | 治療効果が顕著なサブグループを抽出するための境界内平均生存時間に基づく生存時間Bump Hunting法の開発 |
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