Adaptive optimal designs for dose-finding studies and an adaptive multivariate CUSUM control chart

Abstract

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] There are many areas where optimal designs are applied to, for example, the development of a new drug, where a conventional dose- finding study involves learning about the dose-response curve in order to bring forward right doses of drug to late-stage development. The first part of this dissertation focus on three pharmacodynamics sigmoid Emax models, we derive the corresponding simple formats of the adaptive optimal designs regardless of the optimality criteria or parameters of interest. An algorithm for deriving a specific adaptive optimal design is developed. A simulation study comparing the adaptive optimal designs and the uniform designs is also performed. The second part of this dissertation focuses on the statistical process control, we proposed an adaptive approach for the multivariate CUSUM statistical process control chart for signaling a range of location shifts. This method is based on the multivariate CUSUM control chart proposed by Pignatiello and Runger in 1990. We used the exponentially moving weighted average (EMWA) statistic to estimate the current process mean shift and change the reference value adaptively in each run. By specifying the minimal magnitude of the mean shift through the non-centrality parameter, our proposed control chart can achieve an overall good performance for detecting a range of shifts rather than a single value.