Wang, Meihua
(2007)
AN ADAPTIVE BAYESIAN APPROACH TO JOINTLY MODELING RESPONSE AND TOXICITY IN PHASE I DOSE-FINDING TRIALS.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
The Belmont Report (1979) presents ethical principles governing clinical research: respect for persons, beneficence, and justice. This dissertation attempts to improve beneficence, in particular, in early stage clinical trials, in three directions. First, we develop a "dose-choice control panel" (DCCP) computer program. Inputs are complete population information and patient utilities. DCCP produces optimal dose assignment decisions, and helps users to explore how the population parameters and utilities affect the dose recommendation.Second, we present a new adaptive Bayesian method for dose-finding in phase I clinical trials based on both response and toxicity. Although clinical responses are rare in cancer trials, biological responses may be common and may help decide how aggressive a phase I escalation should be. The model assumes that response and toxicity events happen depending on respective dose thresholds for the individual, assuming that the thresholds jointly follow a bivariate log-normal distribution or a mixture. The design utilizes prior information about the population threshold distribution as well as accumulated data. The next dose is assigned to maximize expected utility integrated over the current posterior distribution. The design is evaluated in a setting inspired by the Gleevec story, with population parameters equaling estimates from early Gleevec trials. This exercise provides evidence for the value of the use of the proposed design for future clinical trials. Third, we propose an adaptive Bayesian design based on a hierarchical pharmacokinetics/pharmacodynamic (PK/PD) model, incorporating prior knowledge and/or patient-specific measurements related to PK/PD processes. Because genetic variations or drug co-administration can lead to huge inter-individual differences in drug efficacy and toxicity, it is desirable to individualize chemotherapy dosage. Those factors influencing drug metabolism and clearance are expected to affect all PD processes downstream, leading to efficacy and toxicity outcomes, while other genetic variations or drug co-administration may affect only one PD process. Application of the design to the Gleevec and Irinotecan settings is encouraging with regard to patient protection and accuracy of estimates. This work could improve public health by providing more accurate answers quicker, and by encouraging accrual through explicit consideration of what is best for each individual patient.
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Details
| Item Type: |
University of Pittsburgh ETD
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| Status: |
Unpublished |
| Creators/Authors: |
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| ETD Committee: |
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| Date: |
26 September 2007 |
| Date Type: |
Completion |
| Defense Date: |
27 June 2007 |
| Approval Date: |
26 September 2007 |
| Submission Date: |
12 June 2007 |
| Access Restriction: |
No restriction; Release the ETD for access worldwide immediately. |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Public Health > Biostatistics |
| Degree: |
PhD - Doctor of Philosophy |
| Thesis Type: |
Doctoral Dissertation |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
adaptive design; Bayesian design; log-bivariate normal distribution; PD; Phase I trials; PK; threshold |
| Other ID: |
http://etd.library.pitt.edu/ETD/available/etd-06122007-144306/, etd-06122007-144306 |
| Date Deposited: |
10 Nov 2011 19:47 |
| Last Modified: |
15 Nov 2016 13:44 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/8080 |
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