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Responder identification in clinical trials with censored data

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  • Kehl, Victoria
  • Ulm, Kurt

Abstract

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  • Kehl, Victoria & Ulm, Kurt, 2006. "Responder identification in clinical trials with censored data," Computational Statistics & Data Analysis, Elsevier, vol. 50(5), pages 1338-1355, March.
    • Handle: RePEc:eee:csdana:v:50:y:2006:i:5:p:1338-1355
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    Cited by:

    1. Shonosuke Sugasawa & Hisashi Noma, 2021. "Efficient screening of predictive biomarkers for individual treatment selection," Biometrics, The International Biometric Society, vol. 77(1), pages 249-257, March.
    2. Sauerbrei, Willi & Royston, Patrick & Zapien, Karina, 2007. "Detecting an interaction between treatment and a continuous covariate: A comparison of two approaches," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 4054-4063, May.
    3. Cho, Youngjoo & Zhan, Xiang & Ghosh, Debashis, 2022. "Nonlinear predictive directions in clinical trials," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    4. Yaoyao Xu & Menggang Yu & Ying‐Qi Zhao & Quefeng Li & Sijian Wang & Jun Shao, 2015. "Regularized outcome weighted subgroup identification for differential treatment effects," Biometrics, The International Biometric Society, vol. 71(3), pages 645-653, September.
    5. Shengli An & Peter Zhang & Hong-Bin Fang, 2023. "Subgroup Identification in Survival Outcome Data Based on Concordance Probability Measurement," Mathematics, MDPI, vol. 11(13), pages 1-10, June.
    6. Seibold Heidi & Zeileis Achim & Hothorn Torsten, 2016. "Model-Based Recursive Partitioning for Subgroup Analyses," The International Journal of Biostatistics, De Gruyter, vol. 12(1), pages 45-63, May.

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