A curve-free Bayesian decision-theoretic design for two-agent Phase I trials
| Autor: | Ying Lu, Bee Leng Lee, Shenghua K. Fan |
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| Rok vydání: | 2016 |
| Předmět: |
Statistics and Probability
Research design Computer science Bayesian probability Two agent computer.software_genre 01 natural sciences Conjugate prior Article 010104 statistics & probability 03 medical and health sciences Bayes' theorem 0302 clinical medicine Prior probability Humans Pharmacology (medical) 030212 general & internal medicine 0101 mathematics Pharmacology Models Statistical Clinical Trials Phase I as Topic Dose-Response Relationship Drug Phase i trials Bayes Theorem Sample size determination Research Design Sample Size Data mining computer Algorithms |
| Zdroj: | Journal of biopharmaceutical statistics. 27(1) |
| ISSN: | 1520-5711 |
| Popis: | Although Bayesian statistical methods are gaining attention in the medical community, as they provide a natural framework for incorporating prior information, the complexity of these methods limited their adoptions in clinical trials. This article proposes a Bayesian design for two-agent Phase I trials that is relatively easy for clinicians to understand and implement, yet performs comparably to more complex designs, so that it is more likely to be adopted in actual trials. In order to reduce model complexity and computational burden, we choose a working model with conjugate priors so that the posterior distributions have analytical expressions. Furthermore, we provide a simple strategy to facilitate the specification of priors based on the toxicity information accrued from single-agent Phase I trials. The proposed method should be useful in terms of the ease of implementation and the savings in sample size without sacrificing performance. Moreover, the conservativeness of the dose-finding algorithm renders it a relatively safe method. |
| Databáze: | OpenAIRE |
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