Title | Continual reassessment method with regularization in phase I clinical trials. |
Publication Type | Journal Article |
Year of Publication | 2020 |
Authors | Li, Xiang, Anastasia Ivanova, Hong Tian, Pilar Lim, and Kevin Liu |
Journal | J Biopharm Stat |
Volume | 30 |
Issue | 6 |
Pagination | 964-978 |
Date Published | 2020 Nov 01 |
ISSN | 1520-5711 |
Keywords | Clinical Trials, Phase I as Topic, Computer Simulation, Dose-Response Relationship, Drug, Humans, Maximum Tolerated Dose, Probability, Research Design |
Abstract | Many Phase I trial designs have been developed to improve upon the standard design. These designs can be classified as long-memory designs, for example, the continual reassessment method (CRM), and short-memory designs such as the modified toxicity probability interval (mTPI) design. Long-term memory designs use all data but their performance can be negatively affected by the model misspecification. Short-term memory designs only use data at the current dose and might lose efficiency as a result. To overcome these issues, we propose a regularized CRM (rCRM). The rCRM offers a trade-off between long-term memory and short-term memory methods. The rCRM gives more weight to data obtained at the doses with the estimated probability of toxicity closer to the target toxicity rate. The addition of a regularization term has an effect of shrinking the dimension of the model and leads to improved performance of the 2-parameter CRM. The rCRM is a good design choice to guide assignments in an expansion cohort phase of a dose-finding trial since dose assignments do not seem to change as often as in corresponding CRMs. |
DOI | 10.1080/10543406.2020.1818251 |
Alternate Journal | J Biopharm Stat |
Original Publication | Continual reassessment method with regularization in phase I clinical trials. |
PubMed ID | 32926652 |
PubMed Central ID | PMC7954799 |
Grant List | P01 CA142538 / CA / NCI NIH HHS / United States |