生存時間変数に対する代替性評価 ─メタアナリシスアプローチ
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| Published in | 計量生物学 Vol. 45; no. 1; pp. 67 - 85 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | Japanese |
| Published |
日本計量生物学会
30.07.2024
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| Online Access | Get full text |
| ISSN | 0918-4430 2185-6494 |
| DOI | 10.5691/jjb.45.67 |
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| Author | 江村, 剛志 大庭, 幸治 |
|---|---|
| Author_xml | – sequence: 1 fullname: 大庭, 幸治 organization: 東京大学大学院医学系研究科公共健康医学専攻生物統計学分野 – sequence: 1 fullname: 江村, 剛志 organization: 久留米大学バイオ統計センター |
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| ContentType | Journal Article |
| Copyright | 2023 日本計量生物学会 |
| Copyright_xml | – notice: 2023 日本計量生物学会 |
| DOI | 10.5691/jjb.45.67 |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 2185-6494 |
| EndPage | 85 |
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| ISSN | 0918-4430 |
| IngestDate | Wed Sep 03 06:30:37 EDT 2025 |
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| PageCount | 19 |
| ParticipantIDs | jstage_primary_article_jjb_45_1_45_67_article_char_ja |
| PublicationCentury | 2000 |
| PublicationDate | 2024/07/30 |
| PublicationDateYYYYMMDD | 2024-07-30 |
| PublicationDate_xml | – month: 07 year: 2024 text: 2024/07/30 day: 30 |
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| PublicationTitle | 計量生物学 |
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| Publisher | 日本計量生物学会 |
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| References | Buyse, M., Burzykowski, T., Michiels, S. and Carroll, K. (2008). Individual-and trial-level surrogacy in colorectal cancer. Statistical Methods in Medical Research 17(5), 467-475. Sofeu, C. L., Emura, T. and Rondeau, V. (2019). One-step validation method for surrogate endpoints using data from multiple randomized cancer clinical trials with failure-time endpoints. Statistics in Medicine, 38(16), 2928-2942. GASTRIC Group, Oba, K., Paoletti, X. et al. (2013). Role of chemotherapy for advanced/recurrent gastric cancer: an individual-patient-data meta-analysis. European Journal of Cancer, 49(7), 1565-1577. Scannell, J.W., Blanckley, A., Boldon, H. and Warrington, B. (2012). Diagnosing the decline in pharmaceutical R &D efficiency. Nature Reviews Drug Discovery, 11(3), 191-200. Belaroussi, Y., Bouteiller, F., Bellera, C. et al. (2023). Survival outcomes of patients with metastatic non-small cell lung cancer receiving chemotherapy or immunotherapy as first-line in a real-life setting. Scientific Reports, 13(1), 9584. Sargent, D.J., Wieand, H.S., Haller, D.G. et al. (2005). Disease-free survival versus overall survival as a primary end point for adjuvant colon cancer studies: individual patient data from 20,898 patients on 18 randomized trials. Journal of Clinical Oncology, 23(34): 8664-8670. Kemp, R. and Prasad, V. (2017). Surrogate endpoints in oncology: when are they acceptable for regulatory and clinical decisions, and are they currently overused? BMC Medicine, 15(1), 134. Rondeau, V., Pignon, J.P., Michiels, S. and Mach-NC Collaborative Group. (2015). A joint model for the dependence between clustered times to tumour progression and deaths: A meta-analysis of chemotherapy in head and neck cancer. Statistical Methods in Medical Research, 24(6), 711-729. 江村剛志 (2023). 生存時間の2標本問題— コピュラに基づく従属打ち切り問題への対処—， 日本統計学会誌，52(2), 295-317. Schneider, S., Dos Reis, R. C. P., Gottselig, M. M. et al. (2023). Clayton copula for survival data with dependent censoring: An application to a tuberculosis treatment adherence data. Statistics in Medicine, 42(23), 4057-4081. Burzykowski, T., Buyse, M. and Molenberghs, G. (2005). The Evaluation of Surrogate Endpoints. New York: Springer. 古川恭治 (2023). ポアソン混合効果モデルによる生存時間分析． 日本統計学会誌，52(2), 131-152. Branchoux, S., Sofeu, C.L., Gaudin, A.F. et al. (2022). Time to next treatment or death as a candidate surrogate endpoint for overall survival in advanced melanoma patients treated with immune checkpoint inhibitors: an insight from the phase III CheckMate 067 trial. ESMO open, 7(1), 100340. Okui J., Nagashima K., Matsuda S. et al. (2024). Recurrence-free survival as a surrogate endpoint for overall survival after neoadjuvant chemotherapy and surgery for oesophageal squamous cell carcinoma, British Journal of Surgery, 111(22), znae038. Rotolo, F., Legrand, C. and Van Keilegom, I. (2013). A simulation procedure based on copulas to generate clustered multi-state survival data. Computer Methods and Programs in Biomedicine, 109(3), 305-312. Shi, Q., Sargent, D.J. (2009). Meta-analysis for the evaluation of surrogate endpoints in cancer clinical trials. The International Journal of Clinical Oncology, 14(2), 102-111. Burzykowski, T., Molenberghs, G., Buyse, M. et al. (2001). Validation of surrogate end points in multiple randomized clinical trials with failure time end points. Journal of the Royal Statistical Society: Series C, 50(4), 405-422. Duchateau, L. and Janssen, P. (2008). The Frailty Model. Springer Verlag. Michiels, S., Baujat, B., Mahé, C., Sargent, D. J. and Pignon, J. P. (2005). Random effects survival models gave a better understanding of heterogeneity in individual patient data meta-analyses. Journal of Clinical Epidemiology, 58(3), 238-245. Oba, K., Paoletti, X., Alberts, S. et al. (2013). Disease-free survival as a surrogate for overall survival in adjuvant trials of gastric cancer: a meta-analysis. Journal of the National Cancer Institute, 105(21):1600-1607. 江村剛志，古川恭治 (2024). フレイルティモデル— 生存分析におけるランダム効果—， 計量生物学，45(2). Roberts, E.K., Elliott, M.R. and Taylor, J.M.G. (2023). Surrogacy validation for time-to-event outcomes with illness-death frailty models, Biometrical Journal, 66(1), 2200324. Sofeu, C. L., Emura, T. and Rondeau, V. (2021). A joint frailty-copula model for meta-analytic validation of failure time surrogate endpoints in clinical trials. Biometrical Journal, 63(2), 423-446. 塚原英敦 (2021). リスク解析における接合関数， 日本統計学会誌， 51, 101-121. Emura, T., Nakatochi, M., Murotani, K. and Rondeau, V. (2017). A joint frailty-copula model between tumour progression and death for meta-analysis. Statistical Methods in Medical Research, 26(6), 2649-2666. Prentice, R.L. (1989). Surrogate endpoints in clinical trials: Definition and operational criteria. Statistics in Medicine, 8(4), 431-440. Nelsen, R.B. (2006). An Introduction to Copulas, Second Edition, New York: Springer Alonso, A. and Molenberghs, G. (2008). Evaluating time to cancer recurrence as a surrogate marker for survival from an information theory perspective. Statistical Methods in Medical Research, 17(5), 497-504. 塚原英敦 (2003). 接合分布関数とその応用— 統計的従属性と1次元周辺分布を所与とした多変量モデリング—， 応用統計学，32, 77-88. Shi, Q., Renfro, L. A., Bot, B. M. et al. (2011). Comparative assessment of trial-level surrogacy measures for candidate time-to-event surrogate endpoints in clinical trials. Computational Statistics & Data Analysis, 55(9), 2748-2757. Alonso, A., Bigirumurame, T., Burzykowski, T. et al. (2016). Applied Surrogate Endpoint Evaluation Methods with SAS and R. CRC Press. Flórez, A. J., Alonso, A., Molenberghs, G. and Van Der Elst, W. (2020). Generating random correlation matrices with fixed values: An application to the evaluation of multivariate surrogate endpoints. Computational Statistics & Data Analysis, 142, 106834. Fine, J.P., Jiang, H. and Chappell, R. (2001). On semi-competing risks data. Biometrika, 88(4), 907-919. Ciani, O., Buyse, M., Garside, R. et al. (2013). Comparison of treatment effect sizes associated with surrogate and final patient relevant outcomes in randomised controlled trials: metaepidemiological study. BMJ, 346, f457. 杉本知之，田中健太 (2023). 2変量生存時間モデルにおけるコピュラとその利用． 日本統計学会誌，52(2), 153-176. Institute for Quality and Efficiency in Health Care (2005). Validity of surrogate endpoints in oncology: executive summary of rapid report A10-05, version 1.1. Cologne, Germany: Institute for Quality and Efficiency in Health Care. Whitehead, J. (1980). Fitting Cox's regression model to survival data using GLIM. Journal of the Royal Statistical Society Series C: Applied Statistics, 29(3), 268-275. Buyse, M., Molenberghs, G., Burzykowski, T. et al. (2000). The validation of surrogate endpoints in meta-analyses of randomized experiments. Biostatistics, 1(1), 49-67. Paoletti, X., Oba, K., Bang, Y.J. et al. (2013). Progression-free survival as a surrogate for overall survival in advanced/recurrent gastric cancer trials: a meta-analysis. Journal of the National Cancer Institute, 105(21), 1667-1670. GASTRIC Group, Paoletti, X., Oba, K. et al. (2010). Benefit of adjuvant chemotherapy for resectable gastric cancer: a meta-analysis: a meta-analysis. JAMA, 303(17), 1729-1737. 田中司朗，大庭幸治，吉村健一，手良向聡 (2010). 代替エンドポイントの評価のための統計的基準とその適用事例． 計量生物学，31(1), 23-48. Ronellenfitsch, U., Jensen, K., Seide, S. et al. (2019). Disease-free survival as a surrogate for overall survival in neoadjuvant trials of gastroesophageal adenocarcinoma: Pooled analysis of individual patient data from randomised controlled trials. European Journal of Cancer, 123, 101-111. Rotolo, F., Paoletti, X. and Michiels, S. (2018). surrosurv: An R package for the evaluation of failure time surrogate endpoints in individual patient data meta-analyses of randomized clinical trials. Computer Methods and Programs in Biomedicine, 155, 189-198. Michiels, S., Le Maître, A., Buyse, M., et al. (2009). Surrogate endpoints for overall survival in locally advanced head and neck cancer: meta-analyses of individual patient data. The Lancet Oncology, 10(4), 341-50. Emura, T. and Chen, Y. H. (2016). Gene selection for survival data under dependent censoring: a copula-based approach. Statistical Methods in Medical Research, 25(6), 2840-2857. Bellera, C. A., Pulido, M., Gourgou, S. et al. (2013). Protocol of the Definition for the Assessment of Time-to-event Endpoints in CANcer trials (DATECAN) project: formal consensus method for the development of guidelines for standardised time-to-event endpoints' definitions in cancer clinical trials. European Journal of Cancer, 49(4), 769-781. 江村剛志，道前洋史 (2020). コピュラを用いた生存時間解析 — 相関のあるエンドポイントとメタ分析の活用—， 統計数理，68, 147-174. Mavridis, D. and Salanti, G. (2013). A practical introduction to multivariate meta-analysis. Statistical Methods in Medical Research, 22(2), 133-158. Michiels, S., Pugliano, L., Marguet, S. et al. (2016). Progression-free survival as surrogate end point for overall survival in clinical trials of HER2-targeted agents in HER2-positive metastatic breast cancer. Annals of Oncology, 27(6), 1029-1034. Cheema, P.K. and Burkes, R.L. (2013). Overall survival should be the primary endpoint in clinical trials for advanced non-small-cell lung cancer. Current Oncology, 20(2), 150-160. Emura, T., Sofeu, C. L. and Rondeau, V. (2021). Conditional copula models for correlated survival endpoints: Individual patient data meta-analysis of randomized controlled trials. Statistical Methods in Medical Research, 30(12), 2634-2650. 戸坂凡展，吉羽要直 (2005). コピュラの金融実務での具体的な活用方法の解説， 金融研究，24, 115-162. Paoletti, X., Lewsley, L.A., Daniele, G. et al. (2020). Assessment of progression-free survival as a surrogate end point of overall survival in first-line treatment of ovarian cancer: a systematic review and meta-analysis. JAMA Network Open, 3(1), e1918939. Roberts, E. (2022). Causal Inference Methods and Intermediate Endpoints in Randomized Clinical Trials, the University of Michigan Library (doctoral dissertation), https://dx.doi.org/10.7302/6075 . 野間久史 (2014). Individual Participant Data に基づくメタアナリシス， 統計数理，62(2), 313-328. Rotolo, F., Paoletti, X., Burzykowski, T. et al. |
| References_xml | – reference: Emura, T., Nakatochi, M., Murotani, K. and Rondeau, V. (2017). A joint frailty-copula model between tumour progression and death for meta-analysis. Statistical Methods in Medical Research, 26(6), 2649-2666. – reference: 江村剛志 (2023). 生存時間の2標本問題— コピュラに基づく従属打ち切り問題への対処—， 日本統計学会誌，52(2), 295-317. – reference: 塚原英敦 (2021). リスク解析における接合関数， 日本統計学会誌， 51, 101-121. – reference: 塚原英敦 (2003). 接合分布関数とその応用— 統計的従属性と1次元周辺分布を所与とした多変量モデリング—， 応用統計学，32, 77-88. – reference: Paoletti, X., Lewsley, L.A., Daniele, G. et al. (2020). Assessment of progression-free survival as a surrogate end point of overall survival in first-line treatment of ovarian cancer: a systematic review and meta-analysis. JAMA Network Open, 3(1), e1918939. – reference: Sargent, D.J., Wieand, H.S., Haller, D.G. et al. (2005). Disease-free survival versus overall survival as a primary end point for adjuvant colon cancer studies: individual patient data from 20,898 patients on 18 randomized trials. Journal of Clinical Oncology, 23(34): 8664-8670. – reference: Okui J., Nagashima K., Matsuda S. et al. (2024). Recurrence-free survival as a surrogate endpoint for overall survival after neoadjuvant chemotherapy and surgery for oesophageal squamous cell carcinoma, British Journal of Surgery, 111(22), znae038. – reference: Rondeau, V., Pignon, J.P., Michiels, S. and Mach-NC Collaborative Group. (2015). A joint model for the dependence between clustered times to tumour progression and deaths: A meta-analysis of chemotherapy in head and neck cancer. Statistical Methods in Medical Research, 24(6), 711-729. – reference: GASTRIC Group, Oba, K., Paoletti, X. et al. (2013). Role of chemotherapy for advanced/recurrent gastric cancer: an individual-patient-data meta-analysis. European Journal of Cancer, 49(7), 1565-1577. – reference: Buyse, M., Burzykowski, T., Michiels, S. and Carroll, K. (2008). Individual-and trial-level surrogacy in colorectal cancer. Statistical Methods in Medical Research 17(5), 467-475. – reference: 江村剛志，道前洋史 (2020). コピュラを用いた生存時間解析 — 相関のあるエンドポイントとメタ分析の活用—， 統計数理，68, 147-174. – reference: Prentice, R.L. (1989). Surrogate endpoints in clinical trials: Definition and operational criteria. Statistics in Medicine, 8(4), 431-440. – reference: Michiels, S., Baujat, B., Mahé, C., Sargent, D. J. and Pignon, J. P. (2005). Random effects survival models gave a better understanding of heterogeneity in individual patient data meta-analyses. Journal of Clinical Epidemiology, 58(3), 238-245. – reference: Shi, Q., Renfro, L. A., Bot, B. M. et al. (2011). Comparative assessment of trial-level surrogacy measures for candidate time-to-event surrogate endpoints in clinical trials. Computational Statistics & Data Analysis, 55(9), 2748-2757. – reference: Alonso, A. and Molenberghs, G. (2008). Evaluating time to cancer recurrence as a surrogate marker for survival from an information theory perspective. Statistical Methods in Medical Research, 17(5), 497-504. – reference: Duchateau, L. and Janssen, P. (2008). The Frailty Model. Springer Verlag. – reference: 田中司朗，大庭幸治，吉村健一，手良向聡 (2010). 代替エンドポイントの評価のための統計的基準とその適用事例． 計量生物学，31(1), 23-48. – reference: Sklar, M. (1959). Fonctions de repartition an dimensions et leurs marges, Publications de l'Institut de Statistique de l'Université de Paris, 8, 229-231. – reference: Fine, J.P., Jiang, H. and Chappell, R. (2001). On semi-competing risks data. Biometrika, 88(4), 907-919. – reference: Emura, T. and Chen, Y. H. (2016). Gene selection for survival data under dependent censoring: a copula-based approach. Statistical Methods in Medical Research, 25(6), 2840-2857. – reference: Rotolo, F., Paoletti, X. and Michiels, S. (2018). surrosurv: An R package for the evaluation of failure time surrogate endpoints in individual patient data meta-analyses of randomized clinical trials. Computer Methods and Programs in Biomedicine, 155, 189-198. – reference: Emura, T., Sofeu, C. L. and Rondeau, V. (2021). Conditional copula models for correlated survival endpoints: Individual patient data meta-analysis of randomized controlled trials. Statistical Methods in Medical Research, 30(12), 2634-2650. – reference: Bellera. C.A., Penel, N., Ouali, M. et al. (2014). Guidelines for time-to-event end point definitions in sarcomas and gastrointestinal stromal tumors (GIST) trials: results of the DATECAN initiative (Definition for the Assessment of Time-to-event Endpoints in CANcer trials). Annals of Oncology, 26(5), 865-872. – reference: Sofeu, C. L., Emura, T. and Rondeau, V. (2021). A joint frailty-copula model for meta-analytic validation of failure time surrogate endpoints in clinical trials. Biometrical Journal, 63(2), 423-446. – reference: 古川恭治 (2023). ポアソン混合効果モデルによる生存時間分析． 日本統計学会誌，52(2), 131-152. – reference: Rotolo, F., Paoletti, X., Burzykowski, T. et al. (2019). A Poisson approach to the validation of failure time surrogate endpoints in individual patient data meta-analyses. Statistical Methods in Medical Research, 28(1), 170-183. – reference: Flórez, A. J., Alonso, A., Molenberghs, G. and Van Der Elst, W. (2020). Generating random correlation matrices with fixed values: An application to the evaluation of multivariate surrogate endpoints. Computational Statistics & Data Analysis, 142, 106834. – reference: Mavridis, D. and Salanti, G. (2013). A practical introduction to multivariate meta-analysis. Statistical Methods in Medical Research, 22(2), 133-158. – reference: Alonso, A., Bigirumurame, T., Burzykowski, T. et al. (2016). Applied Surrogate Endpoint Evaluation Methods with SAS and R. CRC Press. – reference: GASTRIC Group, Paoletti, X., Oba, K. et al. (2010). Benefit of adjuvant chemotherapy for resectable gastric cancer: a meta-analysis: a meta-analysis. JAMA, 303(17), 1729-1737. – reference: Kemp, R. and Prasad, V. (2017). Surrogate endpoints in oncology: when are they acceptable for regulatory and clinical decisions, and are they currently overused? BMC Medicine, 15(1), 134. – reference: Schneider, S., Dos Reis, R. C. P., Gottselig, M. M. et al. (2023). Clayton copula for survival data with dependent censoring: An application to a tuberculosis treatment adherence data. Statistics in Medicine, 42(23), 4057-4081. – reference: Bellera, C. A., Pulido, M., Gourgou, S. et al. (2013). Protocol of the Definition for the Assessment of Time-to-event Endpoints in CANcer trials (DATECAN) project: formal consensus method for the development of guidelines for standardised time-to-event endpoints' definitions in cancer clinical trials. European Journal of Cancer, 49(4), 769-781. – reference: Flórez, A. J., Molenberghs, G., Van der Elst, W. and Alonso, A. (2022). An efficient algorithm to assess multivariate surrogate endpoints in a causal inference framework. Computational Statistics & Data Analysis, 172, 107494. – reference: Institute for Quality and Efficiency in Health Care (2005). Validity of surrogate endpoints in oncology: executive summary of rapid report A10-05, version 1.1. Cologne, Germany: Institute for Quality and Efficiency in Health Care. – reference: 戸坂凡展，吉羽要直 (2005). コピュラの金融実務での具体的な活用方法の解説， 金融研究，24, 115-162. – reference: 杉本知之，田中健太 (2023). 2変量生存時間モデルにおけるコピュラとその利用． 日本統計学会誌，52(2), 153-176. – reference: Buyse, M., Molenberghs, G., Burzykowski, T. et al. (2000). The validation of surrogate endpoints in meta-analyses of randomized experiments. Biostatistics, 1(1), 49-67. – reference: Paoletti, X., Oba, K., Bang, Y.J. et al. (2013). Progression-free survival as a surrogate for overall survival in advanced/recurrent gastric cancer trials: a meta-analysis. Journal of the National Cancer Institute, 105(21), 1667-1670. – reference: Burzykowski, T., Molenberghs, G., Buyse, M. et al. (2001). Validation of surrogate end points in multiple randomized clinical trials with failure time end points. Journal of the Royal Statistical Society: Series C, 50(4), 405-422. – reference: Nelsen, R.B. (2006). An Introduction to Copulas, Second Edition, New York: Springer – reference: Cheema, P.K. and Burkes, R.L. (2013). Overall survival should be the primary endpoint in clinical trials for advanced non-small-cell lung cancer. Current Oncology, 20(2), 150-160. – reference: Rotolo, F., Legrand, C. and Van Keilegom, I. (2013). A simulation procedure based on copulas to generate clustered multi-state survival data. Computer Methods and Programs in Biomedicine, 109(3), 305-312. – reference: Oba, K., Paoletti, X., Alberts, S. et al. (2013). Disease-free survival as a surrogate for overall survival in adjuvant trials of gastric cancer: a meta-analysis. Journal of the National Cancer Institute, 105(21):1600-1607. – reference: Roberts, E.K., Elliott, M.R. and Taylor, J.M.G. (2023). Surrogacy validation for time-to-event outcomes with illness-death frailty models, Biometrical Journal, 66(1), 2200324. – reference: Renfro, L.A., Shi, Q., Sargent, D.J. and Carlin, B.P. (2012). Bayesian adjusted R2 for the meta-analytic evaluation of surrogate time-to-event endpoints in clinical trials. Statistics in Medicine, 31(8), 743-761. – reference: Sofeu, C. L., Emura, T. and Rondeau, V. (2019). One-step validation method for surrogate endpoints using data from multiple randomized cancer clinical trials with failure-time endpoints. Statistics in Medicine, 38(16), 2928-2942. – reference: 野間久史 (2014). Individual Participant Data に基づくメタアナリシス， 統計数理，62(2), 313-328. – reference: Scannell, J.W., Blanckley, A., Boldon, H. and Warrington, B. (2012). Diagnosing the decline in pharmaceutical R &D efficiency. Nature Reviews Drug Discovery, 11(3), 191-200. – reference: Shi, Q., Sargent, D.J. (2009). Meta-analysis for the evaluation of surrogate endpoints in cancer clinical trials. The International Journal of Clinical Oncology, 14(2), 102-111. – reference: Michiels, S., Le Maître, A., Buyse, M., et al. (2009). Surrogate endpoints for overall survival in locally advanced head and neck cancer: meta-analyses of individual patient data. The Lancet Oncology, 10(4), 341-50. – reference: Ronellenfitsch, U., Jensen, K., Seide, S. et al. (2019). Disease-free survival as a surrogate for overall survival in neoadjuvant trials of gastroesophageal adenocarcinoma: Pooled analysis of individual patient data from randomised controlled trials. European Journal of Cancer, 123, 101-111. – reference: 江村剛志，古川恭治 (2024). フレイルティモデル— 生存分析におけるランダム効果—， 計量生物学，45(2). – reference: Michiels, S., Pugliano, L., Marguet, S. et al. (2016). Progression-free survival as surrogate end point for overall survival in clinical trials of HER2-targeted agents in HER2-positive metastatic breast cancer. Annals of Oncology, 27(6), 1029-1034. – reference: Belaroussi, Y., Bouteiller, F., Bellera, C. et al. (2023). Survival outcomes of patients with metastatic non-small cell lung cancer receiving chemotherapy or immunotherapy as first-line in a real-life setting. Scientific Reports, 13(1), 9584. – reference: Whitehead, J. (1980). Fitting Cox's regression model to survival data using GLIM. Journal of the Royal Statistical Society Series C: Applied Statistics, 29(3), 268-275. – reference: Branchoux, S., Sofeu, C.L., Gaudin, A.F. et al. (2022). Time to next treatment or death as a candidate surrogate endpoint for overall survival in advanced melanoma patients treated with immune checkpoint inhibitors: an insight from the phase III CheckMate 067 trial. ESMO open, 7(1), 100340. – reference: Burzykowski, T., Buyse, M. and Molenberghs, G. (2005). The Evaluation of Surrogate Endpoints. New York: Springer. – reference: Ciani, O., Buyse, M., Garside, R. et al. (2013). Comparison of treatment effect sizes associated with surrogate and final patient relevant outcomes in randomised controlled trials: metaepidemiological study. BMJ, 346, f457. – reference: Emura, T., Matsui, S. and Rondeau, V. (2019). Survival Analysis with Correlated Endpoints: Joint Frailty-Copula Models. Springer Singapore. – reference: Roberts, E. (2022). Causal Inference Methods and Intermediate Endpoints in Randomized Clinical Trials, the University of Michigan Library (doctoral dissertation), https://dx.doi.org/10.7302/6075 . |
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| Title | 生存時間変数に対する代替性評価 ─メタアナリシスアプローチ |
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