Innovative Clinical Trial Design and Analysis

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Study design is a crucial first step in clinical trials. Well-designed studies are essential for successful cancer research and cancer drug development. Innovative clinical trial designs can potentially require fewer patients, save resources, and accelerate cancer drug development. The broad, long-term objective of this research project is to develop new statistical methodology to address new and challenging issues in the design and analysis of cancer clinical trials. There are 3 specific aims in this praject. The first aim addresses statistical methods for the design and sample size calculation for longitudinal data and joint models for longitudinal and survival data. Statistical methods will be developed for sample size and power estimation for the overall and direct treatment effect on survival, for the effect of the longitudinal process on survival, and for settings involving multivariate longitudinal and multivariate survival processes. The second aim studies statistical methodology for the design and analysis of group randomized cancer prevention trials with survival and recurrent event outcomes. Empirical process theory will be used to study the asymptotic behavior of the test statistics and both asymptotic approximation as well as permutation test will be used to develop sample size formulas and power estimation. The third aim addresses important statistical issues in the oncology drug development pathway. There are three sub-aims. The first sub-aim is in the area of targeted designs. Methods for alternative designs, including "enrichment" designs, will be developed, and the operating characteristics and costs of these designs to fully targeted designs will be compared. Valid and efficient statistical methods for these trials will be developed by applying a semiparametric empirical likelihood approach. The second sub-aim is in the area of phase 11 designs. New methods for phase II and phase 11/111 clinical trials will be developed and their operating characteristics, costs, and predictive ability for subsequent phase HI trials will be assessed. Information on both combination and non-combination therapies in phase 11 studies and subsequent phase III studies will be gathered to build prediction models using machine learning and other nonparametric classification methods. The third sub-aim is in the area of partially randomized designs. New semiparametric empirical likelihood methods will be developed for the design and analysis of such trials to adjust for selection bias and to improve efficiency. Our research will produce important new and efficient design and analysis tools for cancer research.