Prerequisites: Ph.D. students in Biostatistics, Statistics or related field (e.g. Survey Methodology)
Advisory Prerequisites: None
Description: Statistics has developed as a field through seminal papers and fascinating controversies. Seminal ideas and controversies in statistics will be reviewed and discussed. Students will be assigned to present and discuss key papers, with the aid of later commentaries in the literature that help elucidate the issues. The goal is to expand student's knowledge of the statistics literature and encourage a historical perspective. A draft list of papers, arranged below by topic, is provided; in additional to original papers there are some more recent commentaries that provides a modern perspective. Topics are arranged in three groupings: (a) philosophy of statistics; (b) seminal problems in statistical analysis (c) design topics, focusing on the role of randomization. The instructor will also present summaries of the topics covered.
Students will be assigned homework with a few basic discussion questions about the assigned paper or papers. Also, one "lead presenter" student or students will prepare and deliver a presentation summarizing each topic and paper(s). For the class to work it is essential that students read the assigned material, participate in class discussions, and express their own opinions on the homework questions - often there is not a "right" answer.
Course Goals: (1) To provide a deeper understanding and appreciation of the history of statistics
(2) To cover and discuss a number of important ideas in the history of statistics, concerning (a) philosophical approaches; (b) seminal problems in statistical analysis (c) design topics, focusing on the role of randomization
(3) To enhance writing and presentation skills
Learning Objectives: After completing this class, students are expected to be able to attain the following competencies:
(a) Demonstrate effective written, oral and thinking skills
(1) To learn some key ideas and concepts in statistics concerning philosophy of inference, statistical methods and statistical design, through seminal articles
(b) To learn how to read a research paper and understand the key concepts
(c) To learn how to develop clear and logical written and oral presentations based on reading seminal articles in statistics
(c) To start to develop a personal philosophy for statistical practice
Prerequisites: courses in basic statistics and standard regression
Description: This course provides an introduction to Bayesian methods in epidemiology. Topics include: contrasting the Bayesian and classical approaches to hypothesis testing and interval estimation; strengths and weaknesses of the two paradigms, and when they give similar and dissimilar answers; objective and subjective Bayes; calibrated Bayes, a conceptual approach that combines Bayesian and frequentist ideas; computational tools, including Markov Chain Monte Carlo. the Bayesian approach to some important problems in epidemiology: contingency tables, diagnostic testing, comparison of means, regression, hierarchical models, measurement error, and analysis of data from common study designs.
Course Goals: The Bayesian approach to some important problems in epidemiology: contingency tables, diagnostic testing, comparison of means, regression, hierarchical models, measurement error, and analysis of data from common study designs.