DESCRIPTION (Adapted from applicant's abstract): This application has three
specific aims dealing with the design and analysis of studies into
population pharmacokinetics (PK) and pharmacodynamics (PD), with application
to cancer chemotherapy.
The first specific aim proposes expanding upon an earlier Bayesian analysis
of hematology data collected as part of a Cancer and Leukemia Group B
(CALGB) Phase I study. In particular, it is proposed to develop a Bayesian
semiparametric model, one flexible enough to allow heterogeneity and
overdispersion in the distribution of patient-specific parameters
(random-effects distribution), as well as non-parametric regression on
patient-specific covariates. Extensions will also include models for
multiple longitudinal outcomes. The investigators will reanalyze the CALGB
Phase I study and evaluate differences from the applicant's earlier
analysis.
The second specific aim is to develop a predictive model relating
hematologic toxicity to patient characteristics. The investigators will
develop a Bayesian hierarchical metamodel and apply it to data from two
completed CALGB studies: the Phase I study mentioned in aim one and a large
Phase I trial of adjuvant chemotherapy for women with stage II breast
cancer.
The third aim proposes new methodology for solving optimal design problems
built around PK/PD models, honestly accounting for uncertainty in the
estimation and prediction in the PK/PD models. In particular, the
investigators will apply decision-theoretic considerations to develop a
rational strategy for picking times to sample patient plasma in population
pharmacokinetic and pharmacodynamic studies. They will evaluate potential
savings, compared to current limited sampling strategies, via simulation
studies under various presumed pharmacokinetic and pharmacodynamic models
reported in the literature.
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