ASSESSMENT OF CONFIRMABILITY IN CLINICAL TRIALS AND INDIVIDUALISED MEDICINE DEVELOPMENT USING STATISTICAL TOLERANCE REGIONS

Abstract

This thesis presents assessment of confirmability and individualised medicine development in two-arm randomised clinical trials framework. A novel clinically meaningful model, ETZ model, is proposed for assessing confirmability of study results. We aim to increase the phase transition success rate, that a promising Phase 2 result will have a high probability of being confirmed in a Phase 3 study. Therefore, the drug developer decides whether to invest in additional resources to reduce the variability of a particularly impactful component. A key innovation of our research is to show ETZ variabilities can be estimated from three variances rou- tinely in randomised clinical trials. An app is developed for assessing of each ETZ variability component on confirmability. The idea of our individualised medicine research is that targeting a subgroup is only possible when variability between patients within each treatment arm is small and the measurement error is small. A key innovation is that we consider the patient- to-patient variability in individualised medicine. We also come up with an innovative pivot quantity. In addition, we discuss about the derivation of covariance-variance matrix in mixed model repeated measurements to obtain more appropriate estimates.

Date of Award	1 Aug 2023
Original language	English
Awarding Institution	The University of Manchester
Supervisor	Yang Han (Supervisor) & Jianxin Pan (Supervisor)