Clinical research in cancer is becoming ever more complex. Our understanding of the molecular characteristics of tumor cell growth is spawning new ideas for anticancer drug development that present challenges for the design and analysis of clinical studies to evaluate their activity. There still exists a need for better understanding of the determinants of variability in the pharmacokinetics and pharmacodynamics of commonly used anticancer drugs, as well as of these newer agents. The fact that most, if not all, anticancer agents are initially examined most closely in early phase clinical studies having relatively small sample sizes begs for methods that will combine information gathered from separate studies efficiently and coherently. Addressing these and similar issues, this application describes continuing research and development of statistical methods for the analysis and design of studies of complex data. The first specific aim concerns hierarchical modeling of correlated data with mixture priors and expands the methods to data from patients treated over multiple cycles and/or collected for multiple end points (e.g., pharmacokinetics of two or more drugs given simultaneously). The second specific aim addresses combining information from studies that generate correlated data (e.g., longitudinal profiles or assay results from multiple biopsies from the same patient) and that may have more or less in common. The application describes the development of statistical tools to carry out full Bayesian meta-analysis across studies with such correlated outcome data. Importantly, the statistical model also allows for common and study-specific components in the mixture model and for inferring in a data-dependent way the degree to which the model allows borrowing strength across studies. The third specific aim proposes optimal design rules for determining sample size, sampling times and/or numbers of samples per patient (or tumor) in the context of modeling complex biologic processes with mixture priors. The designs will allow for sequential updating of design points (e.g., sampling times) for future subjects or current subjects in subsequent cycles. The proposed work will explore the properties of these designs relative to other possible design strategies.
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| Guindani, Michele; Sepúlveda, Nuno; Paulino, Carlos Daniel et al. (2014) A Bayesian Semi-parametric Approach for the Differential Analysis of Sequence Counts Data. J R Stat Soc Ser C Appl Stat 63:385-404 |
| Müller, Peter; Quintana, Fernando A; Rosner, Gary L et al. (2014) Bayesian inference for longitudinal data with non-parametric treatment effects. Biostatistics 15:341-52 |
| Rossell, David; Müller, Peter (2013) Sequential stopping for high-throughput experiments. Biostatistics 14:75-86 |
| Cruz-Marcelo, Alejandro; Rosner, Gary L; Muller, Peter et al. (2013) Effect on Prediction when Modeling Covariates in Bayesian Nonparametric Models. J Stat Theory Pract 7:204-218 |
| Di Lucca, Maria Anna; Guglielmi, Alessandra; Müller, Peter et al. (2013) A Simple Class of Bayesian Nonparametric Autoregression Models. Bayesian Anal 8:63-88 |
| Müller, Peter; Mitra, Riten (2013) Bayesian Nonparametric Inference - Why and How. Bayesian Anal 8: |
| Jiang, Fei; Jack Lee, J; Müller, Peter (2013) A Bayesian decision-theoretic sequential response-adaptive randomization design. Stat Med 32:1975-94 |
| Lee, Jaeyong; Quintana, Fernando A; Müller, Peter et al. (2013) Defining Predictive Probability Functions for Species Sampling Models. Stat Sci 28:209-222 |
| León-Novelo, Luis G; Müller, Peter; Arap, Wahid et al. (2013) Bayesian decision theoretic multiple comparison procedures: an application to phage display data. Biom J 55:478-89 |
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