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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Optimal dose escalation methods using deep reinforcement learning in phase I oncology trials
|
|---|---|
| Published in |
Journal of Biopharmaceutical Statistics, January 2023
|
| DOI | 10.1080/10543406.2023.2170402 |
| Pubmed ID | |
| Authors |
Kentaro Matsuura, Kentaro Sakamaki, Junya Honda, Takashi Sozu |
| Abstract |
In phase I trials of a novel anticancer drug, one of the most important objectives is to identify the maximum tolerated dose (MTD). To this end, a number of methods have been proposed and evaluated under various scenarios. However, the percentages of correct selection (PCS) of MTDs using previous methods are insufficient to determine the dose for late-phase trials. The purpose of this study is to construct an action rule for escalating or de-escalating the dose and continuing or stopping the trial to increase the PCS as much as possible. We show that deep reinforcement learning with an appropriately defined state, action, and reward can be used to construct such an action selection rule. The simulation study shows that the proposed method can improve the PCS compared with the 3 + 3 design, CRM, BLRM, BOIN, mTPI, and i3 + 3 methods. |
X Demographics
The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 3 | 38% |
| Unknown | 5 | 63% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 8 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 5 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 1 | 20% |
| Unknown | 4 | 80% |
| Readers by discipline | Count | As % |
|---|---|---|
| Mathematics | 1 | 20% |
| Unknown | 4 | 80% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 February 2023.
All research outputs
#7,237,706
of 25,365,817 outputs
Outputs from Journal of Biopharmaceutical Statistics
#46
of 383 outputs
Outputs of similar age
#133,153
of 472,231 outputs
Outputs of similar age from Journal of Biopharmaceutical Statistics
#3
of 11 outputs
Altmetric has tracked 25,365,817 research outputs across all sources so far. This one has received more attention than most of these and is in the 71st percentile.
So far Altmetric has tracked 383 research outputs from this source. They receive a mean Attention Score of 2.2. This one has done well, scoring higher than 88% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 472,231 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 11 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.