The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Consistency of continual reassessment method under model misspecification
Abstract
This paper studies large sample behaviour of the continual reassessment method used to determine the target dose levels in phase I clinical trials. A sequential maximum likelihood procedure is investigated for local estimation of a given targeted toxicity level. Consistency and asymptotic normality for the maximum likelihood estimates are established under model misspecification and it is shown that, in general, the recommended dose level will converge to the target level. There are, however, situations in which the recommended dose may converge to a level close to the target level, but not necessarily the closest. This contrasts with the conclusions of O'Quigley, Pepe & Fisher (1990) and we describe this situation as well as providing the conditions under which convergence is assured.
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Citations
Cites
A Stochastic Approximation Method
Robbins, H., & Monro, S. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729586 · Cited by 9,643
Cited by
Continual Reassessment Method: A Likelihood Approach
O'Quigley, J., & Shen, L. Z. (1996) · Biometrics · doi:10.2307/2532905 · Cited by 382
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Metadata
- Authors
- Shen, L.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 1996-06-01
- Volume
- 83
- Issue
- 2
- Pages
- 395-405
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2075236916
- Cited by
- 168
- References
- 1
- FWCI
- 1.98
- Type
- journal article
- Language
- en
- Open access
- closed
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- Shen, L. (1996). Consistency of continual reassessment method under model misspecification. Biometrika. https://doi.org/10.1093/biomet/83.2.395
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
