A dose-schedule finding design for phase I-II clinical trials
Journal of the Royal Statistical Society. Series C: Applied Statistics, ISSN: 1467-9876, Vol: 65, Issue: 2, Page: 259-272
2016
- 17Citations
- 8Usage
- 20Captures
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations17
- Citation Indexes17
- 17
- CrossRef9
- Usage8
- Downloads7
- Abstract Views1
- Captures20
- Readers20
- 20
Article Description
Summary: Dose finding methods aiming at identifying an optimal dose of a treatment with a given schedule may be at a risk of misidentifying the best treatment for patients. We propose a phase I-II clinical trial design to find the optimal dose-schedule combination. We define schedule as the method and timing of administration of a given total dose in a treatment cycle. We propose a Bayesian dynamic model for the joint effects of dose and schedule. The model proposed allows us to borrow strength across dose-schedule combinations without making overly restrictive assumptions on the ordering pattern of the schedule effects. We develop a dose-schedule finding algorithm to allocate patients sequentially to a desirable dose-schedule combination, and to select an optimal combination at the end of the trial. We apply the proposed design to a phase I-II clinical trial of a γ-secretase inhibitor in patients with refractory metastatic or locally advanced solid tumours, and we examine the operating characteristics of the design through simulations.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84954381764&origin=inward; http://dx.doi.org/10.1111/rssc.12113; http://www.ncbi.nlm.nih.gov/pubmed/26877554; https://academic.oup.com/jrsssc/article/65/2/259/7061304; https://repository.lsu.edu/ag_exst_pubs/94; https://repository.lsu.edu/cgi/viewcontent.cgi?article=1093&context=ag_exst_pubs; http://doi.wiley.com/10.1111/rssc.12113; http://onlinelibrary.wiley.com/doi/10.1111/rssc.12113/abstract
Oxford University Press (OUP)
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know