{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T15:22:15Z","timestamp":1743088935873,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031395383"},{"type":"electronic","value":"9783031395390"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-39539-0_5","type":"book-chapter","created":{"date-parts":[[2023,7,30]],"date-time":"2023-07-30T00:02:38Z","timestamp":1690675358000},"page":"51-59","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Safe Exploration in\u00a0Dose Finding Clinical Trials with\u00a0Heterogeneous Participants"],"prefix":"10.1007","author":[{"given":"Isabel","family":"Chien","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Javier Gonzalez","family":"Hernandez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Richard E.","family":"Turner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,30]]},"reference":[{"key":"5_CR1","unstructured":"Phases of clinical trials (2022). https:\/\/www.cancerresearchuk.org\/about-cancer\/find-a-clinical-trial\/what-clinical-trials-are\/phases-of-clinical-trials"},{"key":"5_CR2","unstructured":"Aziz, M., Kaufmann, E., Riviere, M.K.: On multi-armed bandit designs for dose-finding trials. J. Mach. Learn. Res. 22, 14:1\u201314:38 (2020)"},{"key":"5_CR3","unstructured":"Baek, J., Farias, V.F.: Fair exploration via axiomatic bargaining. In: Neural Information Processing Systems (2021)"},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Br\u00f8gger-Mikkelsen, M., Ali, Z.S., Zibert, J.R., Andersen, A.D., Thomsen, S.F.: Online patient recruitment in clinical trials: systematic review and meta-analysis. J. Med. Internet Res. 22 (2020)","DOI":"10.2196\/preprints.22179"},{"key":"5_CR5","doi-asserted-by":"crossref","unstructured":"Chien, I., Deliu, N., Turner, R.E., Weller, A., Villar, S.S., Kilbertus, N.: Multi-disciplinary fairness considerations in machine learning for clinical trials. In: 2022 ACM Conference on Fairness, Accountability, and Transparency (2022)","DOI":"10.1145\/3531146.3533154"},{"issue":"2","key":"5_CR6","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1634\/theoncologist.2017-0237","volume":"23","author":"LJ Dickmann","year":"2018","unstructured":"Dickmann, L.J., Schutzman, J.L.: Racial and ethnic composition of cancer clinical drug trials: how diverse are we? Oncologist 23(2), 243\u2013246 (2018)","journal-title":"Oncologist"},{"key":"5_CR7","unstructured":"Hensman, J., de G. Matthews, A.G., Ghahramani, Z.: Scalable variational gaussian process classification. In: International Conference on Artificial Intelligence and Statistics (2014)"},{"key":"5_CR8","doi-asserted-by":"publisher","first-page":"305","DOI":"10.2147\/DDDT.S76135","volume":"9","author":"J Huang","year":"2015","unstructured":"Huang, J., et al.: Sample sizes in dosage investigational clinical trials: a systematic evaluation. Drug Design Dev. Ther. 9, 305\u2013312 (2015)","journal-title":"Drug Design Dev. Ther."},{"key":"5_CR9","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1023\/A:1008306431147","volume":"13","author":"DR Jones","year":"1998","unstructured":"Jones, D.R., Schonlau, M., Welch, W.J.: Efficient global optimization of expensive black-box functions. J. Global Optim. 13, 455\u2013492 (1998)","journal-title":"J. Global Optim."},{"key":"5_CR10","unstructured":"Journel, A.G., Huijbregts, C.J.: Mining geostatistics (1976)"},{"key":"5_CR11","unstructured":"Kazerouni, A., Ghavamzadeh, M., Abbasi, Y., Roy, B.V.: Conservative contextual linear bandits. In: NIPS (2016)"},{"key":"5_CR12","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1177\/1740774513500589","volume":"11","author":"JS Koopmeiners","year":"2014","unstructured":"Koopmeiners, J.S., Modiano, J.F.: A Bayesian adaptive phase I\u2013II clinical trial for evaluating efficacy and toxicity with delayed outcomes. Clin. Trials 11, 38\u201348 (2014)","journal-title":"Clin. Trials"},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Kurzrock, R., Lin, C., Wu, T.C., Hobbs, B.P., Pestana, R.C., Hong, D.S.: Moving beyond 3+3: the future of clinical trial design. Am. Soc. Clin. Oncol. Educ. Book. Am. Soc. Clin. Oncol. Ann. Meet. 41, e133\u2013e144 (2021)","DOI":"10.1200\/EDBK_319783"},{"key":"5_CR14","unstructured":"Lee, H.S., Shen, C., Jordon, J., van der Schaar, M.: Contextual constrained learning for dose-finding clinical trials. ArXiv abs\/2001.02463 (2020)"},{"key":"5_CR15","doi-asserted-by":"crossref","unstructured":"\u00d6zdemir, B.C., Gerard, C.L., da Silva, C.E.: Sex and gender differences in anticancer treatment toxicity - a call for revisiting drug dosing in oncology. Endocrinology (2022)","DOI":"10.1210\/endocr\/bqac058"},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Pfohl, S.R., Xu, Y., Foryciarz, A., Ignatiadis, N., Genkins, J.Z., Shah, N.H.: Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcare. In: 2022 ACM Conference on Fairness, Accountability, and Transparency (2022)","DOI":"10.1145\/3531146.3533166"},{"key":"5_CR17","unstructured":"Raghavan, M., Slivkins, A., Vaughan, J.W., Wu, Z.S.: The externalities of exploration and how data diversity helps exploitation. In: Annual Conference Computational Learning Theory (2018)"},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Ramamoorthy, A., Kim, H.H., Shah-Williams, E., Zhang, L.: Racial and ethnic differences in drug disposition and response: Review of new molecular entities approved between 2014 and 2019. J. Clin. Pharmacol. 62 (2021)","DOI":"10.1002\/jcph.1978"},{"key":"5_CR19","doi-asserted-by":"publisher","first-page":"466","DOI":"10.1177\/0962280216631763","volume":"27","author":"MK Riviere","year":"2018","unstructured":"Riviere, M.K., Yuan, Y., Jourdan, J.H., Dubois, F., Zohar, S.: Phase I\/II dose-finding design for molecularly targeted agent: plateau determination using adaptive randomization. Stat. Methods Med. Res. 27, 466\u2013479 (2018)","journal-title":"Stat. Methods Med. Res."},{"key":"5_CR20","unstructured":"Shen, C., Wang, Z., Villar, S.S., van der Schaar, M.: Learning for dose allocation in adaptive clinical trials with safety constraints. In: International Conference on Machine Learning (2020)"},{"key":"5_CR21","doi-asserted-by":"crossref","unstructured":"Steinberg, J.R., et al.: Analysis of female enrollment and participant sex by burden of disease in us clinical trials between 2000 and 2020. JAMA Netw. Open 4 (2021)","DOI":"10.1001\/jamanetworkopen.2021.13749"},{"key":"5_CR22","unstructured":"Sui, Y., Gotovos, A., Burdick, J.W., Krause, A.: Safe exploration for optimization with gaussian processes. In: International Conference on Machine Learning (2015)"},{"key":"5_CR23","unstructured":"Sui, Y., Zhuang, V., Burdick, J.W., Yue, Y.: Stagewise safe Bayesian optimization with gaussian processes. In: International Conference on Machine Learning (2018)"},{"key":"5_CR24","doi-asserted-by":"publisher","first-page":"684","DOI":"10.1111\/j.0006-341X.2004.00218.x","volume":"60","author":"PF Thall","year":"2004","unstructured":"Thall, P.F., Cook, J.D.: Dose-finding based on efficacy-toxicity trade-offs. Biometrics 60, 684\u2013693 (2004)","journal-title":"Biometrics"},{"key":"5_CR25","doi-asserted-by":"publisher","first-page":"1608","DOI":"10.1002\/sim.7594","volume":"37","author":"M Thomas","year":"2018","unstructured":"Thomas, M., Bornkamp, B., Seibold, H.: Subgroup identification in dose-finding trials via model-based recursive partitioning. Stat. Med. 37, 1608\u20131624 (2018)","journal-title":"Stat. Med."},{"key":"5_CR26","doi-asserted-by":"publisher","first-page":"1474","DOI":"10.1200\/JCO.21.02377","volume":"40","author":"JM Unger","year":"2022","unstructured":"Unger, J.M., et al.: Sex differences in risk of severe adverse events in patients receiving immunotherapy, targeted therapy, or chemotherapy in cancer clinical trials. J. Clin. Oncol. 40, 1474\u20131486 (2022)","journal-title":"J. Clin. Oncol."},{"issue":"2","key":"5_CR27","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1214\/14-STS504","volume":"30","author":"SS Villar","year":"2015","unstructured":"Villar, S.S., Bowden, J., Wason, J.M.S.: Multi-armed bandit models for the optimal design of clinical trials: benefits and challenges. Stat. Sci.: Rev. J. Inst. Math. Stat. 30(2), 199\u2013215 (2015)","journal-title":"Stat. Sci.: Rev. J. Inst. Math. Stat."},{"key":"5_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40425-018-0389-8","volume":"6","author":"NA Wages","year":"2018","unstructured":"Wages, N.A., Chiuzan, C., Panageas, K.S.: Design considerations for early-phase clinical trials of immune-oncology agents. J. Immunother. Cancer 6, 1\u201310 (2018)","journal-title":"J. Immunother. Cancer"},{"key":"5_CR29","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12874-018-0638-z","volume":"19","author":"GM Wheeler","year":"2019","unstructured":"Wheeler, G.M., et al.: How to design a dose-finding study using the continual reassessment method. BMC Med. Res. Methodol. 19, 1\u201315 (2019)","journal-title":"BMC Med. Res. Methodol."},{"key":"5_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13293-020-00308-5","volume":"11","author":"I Zucker","year":"2020","unstructured":"Zucker, I., Prendergast, B.J.: Sex differences in pharmacokinetics predict adverse drug reactions in women. Biol. Sex Differ. 11, 1\u201314 (2020)","journal-title":"Biol. Sex Differ."}],"container-title":["Lecture Notes in Computer Science","Trustworthy Machine Learning for Healthcare"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-39539-0_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,30]],"date-time":"2023-07-30T00:27:54Z","timestamp":1690676874000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-39539-0_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031395383","9783031395390"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-39539-0_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"30 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"TML4H","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Trustworthy Machine Learning for Healthcare","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 May 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 May 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"tml4h2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cse.hkust.edu.hk\/~jhc\/2023tml4h.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"30","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"16","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"53% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.73","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1.44","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}