Name
Abstract | ||
|---|---|---|
In dose escalation studies cohorts of subjects are given increasing doses of a candidate drug to assess safety and tolerability, pharmacokinetics and pharmacological response. The escalation is carried on until a predefined stopping limit is achieved, often identified by a pharmacokinetic endpoint such as peak plasma concentration or area under the plasma concentration-time profile. In the present work, the application of Bayesian methodologies to Phase I dose escalation studies is explored. A Bayesian population model is devised, which provides predictions of dose-response and dose-risk curves, both for individuals already enrolled in the trial and for a new, previously untested subject. Empirical and fully Bayesian estimation algorithms are worked out. Such methods provide equivalent performances on both experimental and simulated datasets. With respect to previous work, it is quantitatively proven not only that a more general and flexible model is identifiable, but also that such flexibility is needed in real scenarios. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1016/j.cmpb.2011.05.010 | Computer Methods and Programs in Biomedicine |
Keywords | Field | DocType |
previous work,present work,flexible model,dose escalation study,plasma concentration-time profile,bayesian estimation algorithm,dose escalation studies cohort,bayesian population approach,bayesian methodology,peak plasma concentration,bayesian population model,mixed effects model | Tolerability,Population,Computer science,Mixed model,Statistics,Population model,Bayes estimator,Bayesian probability | Journal |
Volume | Issue | ISSN |
107 | 2 | 1872-7565 |
Citations | PageRank | References |
1 | 0.36 | 2 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alberto Russu | 1 | 2 | 1.08 |
| Giuseppe De Nicolao | 2 | 738 | 76.26 |
| Italo Poggesi | 3 | 4 | 1.21 |
| Marta Neve | 4 | 12 | 1.28 |
| Roberto Gomeni | 5 | 1 | 0.70 |