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AN ADAPTIVE BAYESIAN APPROACH TO JOINTLY MODELING RESPONSE AND TOXICITY IN PHASE I DOSE-FINDING TRIALS

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 (Unpublished)

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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
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Wang, Meihuasumswang@yahoo.com
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairDay, Rogerday@upci.pitt.eduDAY01
Committee MemberSampson, Allanasampson@stat.pitt.eduASAMPSON
Committee MemberPotter, Douglaspotter@upci.pitt.edu
Committee MemberCostantino, Josephcostan@nsabp.pitt.eduCOSTAN
Committee MemberBranch, RobertBranch@dom.pitt.eduRAB13
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: Graduate 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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