Statistical Methods and Theory for Clinical Trials in the Era of Patient-Oriented Research and Personalized Medicine

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The primary objectives of my research program are to lay solid mathematical foundations for statistical methods in the design and analysis of clinical trials for the evaluation of new drugs or procedures in the prevention and treatment of the diseases and, if methods are not available, to develop new statistical methods with a through investigation of their mathematical properties.  In recent years, patient-orientated research and development of personalized medicines are two focus areas in the clinical trials. Patient-orientated research intends to involve patients in the design, conduct, and interpretation of a study including the assessment on the effects of treatments by outcomes reported by patients through answers to questions on specific questionnaires, while personalized medicine aims the development of medical treatment tailored to the individual characteristics of each patient. New statistical methods and theory are required for the design and analysis of clinical trials with these two research focuses.  In the last granting cycle, among other achievements, we have made major contributions in this area of statistical research. We plan to continue this research program with focus on the study of the following four problems we have encountered recently in our practices: (1) Development of a mixture density ratio model and associated inference procedures for the analysis of longitudinal patient reported outcomes which are restricted to a closed interval; (2) Development of statistical methods which can be used to identify subgroups of patients who may benefit from or be harmed by a new treatment with respect to outcomes reported by patients based on continuous and/or multiple biomarkers; (3) Theoretical evaluations of resampling procedures for the assessment of the effects on time to an event outcome for biomarkers which are selected by LASSO and related procedures; (4) Development of statistical procedures for subgroup analyses with multiple biomarkers and time to an event outcomes based on a nonparametric measurement of treatment effects. It is expected that this research will generate some new statistical tools for applied statisticians working in clinical trials and other related areas and also lay solid mathematical foundations for these new methods, which will result in more efficient and reliable identification of new strategies for the prevention and treatment of diseases and, therefore, reduction of morbidity and mortality from these diseases. It is also expected it will stimulate new development in the mathematical theory of probability and statistics. The theory and methods developed from this project may be applied to other types of clinical studies and also other fields of natural science and engineering such as physical and chemical testing with limits of detection and reliability engineering. All levels of Highly Qualified Personnel will be trained through the participation in the projects listed in this proposal.