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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.11406 |
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| _version_ | 1866913318697959424 |
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| author | Maity, Arnab Kumar Chowdhury, Satrajit Roy Li, Ray Markovtsova, Lada Bugarini, Roberto |
| author_facet | Maity, Arnab Kumar Chowdhury, Satrajit Roy Li, Ray Markovtsova, Lada Bugarini, Roberto |
| contents | Oncology drug development starts with a dose escalation phase to find the maximal tolerable dose (MTD). Dose limiting toxicity (DLT) is the primary endpoint for dose escalation phase. Traditionally, model-based dose escalation trial designs recommend a dose for escalation based on an assumed dose-DLT relationship. Pharmacokinetic (PK) data are often available but are currently only used by clinical teams in a subjective manner to aid decision making. Formal incorporation of PK data in dose-escalation models can make the decision process more efficient and lead to an increase in precision. In this talk we present a Bayesian joint modeling framework for incorporating PK data in Oncology dose escalation trials. This framework explores the dose-PK and PK-DLT relationships jointly for better model informed dose escalation decisions. Utility of the proposed model is demonstrated through a real-life case study along with simulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_11406 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Pharmacokinetic Measurements in Dose Finding Model Guided by Escalation with Overdose Control Maity, Arnab Kumar Chowdhury, Satrajit Roy Li, Ray Markovtsova, Lada Bugarini, Roberto Applications Computation Oncology drug development starts with a dose escalation phase to find the maximal tolerable dose (MTD). Dose limiting toxicity (DLT) is the primary endpoint for dose escalation phase. Traditionally, model-based dose escalation trial designs recommend a dose for escalation based on an assumed dose-DLT relationship. Pharmacokinetic (PK) data are often available but are currently only used by clinical teams in a subjective manner to aid decision making. Formal incorporation of PK data in dose-escalation models can make the decision process more efficient and lead to an increase in precision. In this talk we present a Bayesian joint modeling framework for incorporating PK data in Oncology dose escalation trials. This framework explores the dose-PK and PK-DLT relationships jointly for better model informed dose escalation decisions. Utility of the proposed model is demonstrated through a real-life case study along with simulation. |
| title | Pharmacokinetic Measurements in Dose Finding Model Guided by Escalation with Overdose Control |
| topic | Applications Computation |
| url | https://arxiv.org/abs/2404.11406 |