Multiple Hypothesis Testing Procedures with Applications to Epidemiologic Studies
Epidemiologic and genetic studies often involve the testing of a large number of hypotheses with test statistics that are potentially dependent. In this project, we investigate multiple testing procedures to control the family-wise error rate and false discovery rate. We consider several classic and novel multiple hypothesis testing procedures. Furthermore, we compare the results of the procedures which take advantage of the dependent structure among test statistics to those of the procedures which do not. The data we used is from a case-control study of non-Hodgkin Lymphoma.
This type of interdisciplinary work is a hallmark of our program in Applied Statistics at Simon Fraser University. For more information, please contact Conghui Qu (firstname.lastname@example.org) or her supervisors Jinko Graham (email@example.com)Department of Statistics and Actuarial Science or John Spinelli (firstname.lastname@example.org) BC Cancer Agency.