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Article: Robust EM continual reassessment method in oncology dose finding
Title | Robust EM continual reassessment method in oncology dose finding | ||||||
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Authors | |||||||
Keywords | Adaptive design Expectation-maximization algorithm Late-onset toxicity Maximum tolerated dose Missing data Model averaging Model selection | ||||||
Issue Date | 2011 | ||||||
Publisher | American Statistical Association. The Journal's web site is located at http://www.amstat.org/publications/jasa/index.cfm?fuseaction=main | ||||||
Citation | Journal Of The American Statistical Association, 2011, v. 106 n. 495, p. 818-831 How to Cite? | ||||||
Abstract | The continual reassessment method (CRM) is a commonly used dose-finding design for phase I clinical trials. Practical applications of this method have been restricted by two limitations: (1) the requirement that the toxicity outcome needs to be observed shortly after the initiation of the treatment; and (2) the potential sensitivity to the prespecified toxicity probability at each dose. To overcome these limitations, we naturally treat the unobserved toxicity outcomes as missing data, and use the expectation-maximization (EM) algorithm to estimate the dose toxicity probabilities based on the incomplete data to direct dose assignment. To enhance the robustness of the design, we propose prespecifying multiple sets of toxicity probabilities, each set corresponding to an individual CRM model. We carry out these multiple CRMs in parallel, across which model selection and model averaging procedures are used to make more robust inference. We evaluate the operating characteristics of the proposed robust EM-CRM designs through simulation studies and show that the proposed methods satisfactorily resolve both limitations of the CRM. Besides improving the MTD selection percentage, the new designs dramatically shorten the duration of the trial, and are robust to the prespecification of the toxicity probabilities. © 2011 American Statistical Association. | ||||||
Persistent Identifier | http://hdl.handle.net/10722/139716 | ||||||
ISSN | 2023 Impact Factor: 3.0 2023 SCImago Journal Rankings: 3.922 | ||||||
ISI Accession Number ID |
Funding Information: The authors thank Dr. Xiudong Lei at the University of Texas MD Anderson Cancer Center for her help in the simulation studies. We gratefully acknowledge the editor, the associate editor, and two anonymous referees for their insightful and constructive comments which substantially improved the article. The research is partially supported by NCI grant R01CA154591-01A1, United States, and a grant from the Research Grants Council of Hong Kong. | ||||||
References |
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yuan, Y | en_HK |
dc.contributor.author | Yin, G | en_HK |
dc.date.accessioned | 2011-09-23T05:54:45Z | - |
dc.date.available | 2011-09-23T05:54:45Z | - |
dc.date.issued | 2011 | en_HK |
dc.identifier.citation | Journal Of The American Statistical Association, 2011, v. 106 n. 495, p. 818-831 | en_HK |
dc.identifier.issn | 0162-1459 | en_HK |
dc.identifier.uri | http://hdl.handle.net/10722/139716 | - |
dc.description.abstract | The continual reassessment method (CRM) is a commonly used dose-finding design for phase I clinical trials. Practical applications of this method have been restricted by two limitations: (1) the requirement that the toxicity outcome needs to be observed shortly after the initiation of the treatment; and (2) the potential sensitivity to the prespecified toxicity probability at each dose. To overcome these limitations, we naturally treat the unobserved toxicity outcomes as missing data, and use the expectation-maximization (EM) algorithm to estimate the dose toxicity probabilities based on the incomplete data to direct dose assignment. To enhance the robustness of the design, we propose prespecifying multiple sets of toxicity probabilities, each set corresponding to an individual CRM model. We carry out these multiple CRMs in parallel, across which model selection and model averaging procedures are used to make more robust inference. We evaluate the operating characteristics of the proposed robust EM-CRM designs through simulation studies and show that the proposed methods satisfactorily resolve both limitations of the CRM. Besides improving the MTD selection percentage, the new designs dramatically shorten the duration of the trial, and are robust to the prespecification of the toxicity probabilities. © 2011 American Statistical Association. | en_HK |
dc.language | eng | en_US |
dc.publisher | American Statistical Association. The Journal's web site is located at http://www.amstat.org/publications/jasa/index.cfm?fuseaction=main | en_HK |
dc.relation.ispartof | Journal of the American Statistical Association | en_HK |
dc.subject | Adaptive design | en_HK |
dc.subject | Expectation-maximization algorithm | en_HK |
dc.subject | Late-onset toxicity | en_HK |
dc.subject | Maximum tolerated dose | en_HK |
dc.subject | Missing data | en_HK |
dc.subject | Model averaging | en_HK |
dc.subject | Model selection | en_HK |
dc.title | Robust EM continual reassessment method in oncology dose finding | en_HK |
dc.type | Article | en_HK |
dc.identifier.email | Yin, G: gyin@hku.hk | en_HK |
dc.identifier.authority | Yin, G=rp00831 | en_HK |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1198/jasa.2011.ap09476 | en_HK |
dc.identifier.scopus | eid_2-s2.0-80054707561 | en_HK |
dc.identifier.hkuros | 195637 | en_US |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-80054707561&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.volume | 106 | en_HK |
dc.identifier.issue | 495 | en_HK |
dc.identifier.spage | 818 | en_HK |
dc.identifier.epage | 831 | en_HK |
dc.identifier.eissn | 1537-274X | - |
dc.identifier.isi | WOS:000296224200008 | - |
dc.publisher.place | United States | en_HK |
dc.identifier.scopusauthorid | Yuan, Y=7402709174 | en_HK |
dc.identifier.scopusauthorid | Yin, G=8725807500 | en_HK |
dc.identifier.issnl | 0162-1459 | - |