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- S Kaneko
- Japan Development, Biostatistics Pharma, Integrated Biostatistics Japan, Novartis Pharma K.K., Minato-ku, Tokyo, Japan
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- A Hirakawa
- Department of Biostatistics and Bioinformatics, Graduate School of Medicine, the University of Tokyo, Bunkyo-ku, Tokyo, Japan
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- Y Kakurai
- R&D Division, Biostatistics & Data Management, Daiichi-Sankyo Co., Ltd., Shinagawa-ku, Tokyo, Japan
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- C Hamada
- Department of Information and Computer Technology, Tokyo University of Science, Katsushika-ku, Tokyo, Japan
書誌事項
- 公開日
- 2020-04-20
- 資源種別
- journal article
- DOI
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- 10.1080/10543406.2020.1744619
- 10.6084/m9.figshare.12159042
- 10.6084/m9.figshare.12159042.v1
- 公開者
- Informa UK Limited
この論文をさがす
説明
Precision medicine is an emerging approach for disease treatment and prevention that accounts for individual variability in genes, environment, and lifestyle. Cancer is a genomic disease; therefore, the dose-efficacy and dose–toxicity relationships for molecularly targeted agents in cancer most likely differ, based on the genomic mutation pattern. The individualized optimal dose – the maximal efficacious dose with a clinically acceptable safety profile – may vary depending on the genomic mutation patterns and should be determined prior to the use of these agents in precision medicine. In addition, genes that influence the individualized optimal doses should be identified in early-phase development. In this study, we propose a novel dose-finding approach to identify the individualized optimal dose for molecularly targeted agents in phase I cancer trials. Individualized optimal dose determination and gene selection were conducted simultaneously based on L1 and L2 penalized regression. Similar to most reported dose-finding approaches, this study considers non-monotonic patterns for dose-efficacy and dose–toxicity relationships, as well as correlations between efficacy and toxicity outcomes based on multinomial distribution. Our dose-finding algorithm is based on the predictive probability calculated with an estimated penalized regression model. We compare the operating characteristics between the proposed and existing methods by simulation studies under various scenarios.
収録刊行物
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- Journal of Biopharmaceutical Statistics
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Journal of Biopharmaceutical Statistics 30 (5), 834-853, 2020-04-20
Informa UK Limited
