A nonparametric Bayesian continual reassessment method in single-agent dose-finding studies.
BMC Med Res Methodol
; 18(1): 172, 2018 12 18.
Article
in En
| MEDLINE
| ID: mdl-30563454
ABSTRACT
BACKGROUND:
The main purpose of dose-finding studies in Phase I trial is to estimate maximum tolerated dose (MTD), which is the maximum test dose that can be assigned with an acceptable level of toxicity. Existing methods developed for single-agent dose-finding assume that the dose-toxicity relationship follows a specific parametric potency curve. This assumption may lead to bias and unsafe dose escalations due to the misspecification of parametric curve.METHODS:
This paper relaxes the parametric assumption of dose-toxicity relationship by imposing a Dirichlet process prior on unknown dose-toxicity curve. A hybrid algorithm combining the Gibbs sampler and adaptive rejection Metropolis sampling (ARMS) algorithm is developed to estimate the dose-toxicity curve, and a two-stage Bayesian nonparametric adaptive design is presented to estimate MTD.RESULTS:
For comparison, we consider two classical continual reassessment methods (CRMs) (i.e., logistic and power models). Numerical results show the flexibility of the proposed method for single-agent dose-finding trials, and the proposed method behaves better than two classical CRMs under our considered scenarios.CONCLUSIONS:
The proposed dose-finding procedure is model-free and robust, and behaves satisfactorily even in small sample cases.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Algorithms
/
Bayes Theorem
/
Statistics, Nonparametric
/
Drug-Related Side Effects and Adverse Reactions
/
Models, Theoretical
Type of study:
Diagnostic_studies
/
Prognostic_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
BMC Med Res Methodol
Journal subject:
MEDICINA
Year:
2018
Type:
Article