Statistical models for clustered survival data and multivariate recurrent events

点击翻译按钮获取中文摘要

In scientific and biomedical research, data being collected may not be independent due to the fact of clustering. For example, in multiple centre clinical trials, patients from the same hospital / clinical centre may share some common risk factors. Fail to take the population heterogeneity into account in the statistical model could lead to biased results and loss of model efficiency. My research plan for this discovery grant is to develop statistical methods for survival and recurrent events data with predictive biomarker variables when individual observations are not independent. In certain types of cancer, such as Hodgkin lymphoma, patients have long life expectancy, and individuals who experience complete remission during the treatment period may have different survival distribution compared to those who do not. It is then important to use the early response data to predict the survival distribution later, and this can be achieved by studying the potential association between the treatment response and survival. I will develop joint models of short term response outcome and long term survival outcome for clustered data. The proposed joint model can be further extended to accommodate different types of outcomes such as continuous variables and counting variables. Predictive biomarkers are patient characteristics that are measured as an indicator of normal biological processes, which can be used to predict which patients will or will not benefit from a new therapy. I will develop new procedures to evaluate biomarker defined subset effect for clustered data. I plan to use flexible statistical models for predictive biomarker in clustered survival data. When the threshold parameter is located near the boundary of the biomarker variable, I will investigate how existing test procedures perform under this circumstance and develop new test for the existence of the biomarker threshold. Recurrent event data occur frequently in many clinical trial and epidemiologic studies. In some situations, the recurrent event may be terminated by a failure event such as death. I will propose multivariate random effects models to investigate the effect of risk factor to multi-type recurrent events in the present of dependent termination and clustering. I will contribute to high quality personnel training by involving different level of students (undergraduate, Master of Sciences and doctoral students) to develop novel statistical methodologies and theories related to this proposal. I will work with M. Sc. Students to develop computational procedures for hypothesis testing problems such as population heterogeneity in joint model, evaluating performance of existing methods for clustered survival and recurrent events data. Together with my Ph. D students, I will investigate new methodologies to make simultaneous statistical inferences for marker response and survival outcomes, develop flexible statistical methods to estimate biomarker-treatment interaction and explore new models for clustered multivariate recurrent events. The proposed research will advance new statistical methodologies and theories to deal with clustered survival data and recurrent event data. These methods can reduce bias of the estimation, improve the efficiency of the statistical model and address the computational challenges for complex data structures. Software developed from the proposed research will benefit statistical science, engineering and reliability research and the biomedical research community in Canada.