Courses Taught by Ananda Sen
BIOSTAT601: Probability and Distribution Theory
- Graduate level
- Fall term(s) for residential students;
- 4 credit hour(s) for residential students;
- Instructor(s): Wang, Lu Sen, Ananda (Residential);
- Prerequisites: Three terms of calculus
- Description: Fundamental probability and distribution theory needed for statistical inference. Probability, discrete and continuous distributions, expectation, generating functions, limit theorems, transformations, sampling theory.
- Syllabus for BIOSTAT601
|Department||Program||Degree||Competency||Specific course(s) that allow assessment||BIOSTAT||MPH||Apply the theoretical foundations of probability theory and distribution theory||BIOSTAT601||BIOSTAT||MS||Apply the theoretical foundations of probability theory and distribution theory||BIOSTAT601|
EPID766: Analysis of Longitudinal Data from Epidemiologic Studies
- Graduate level
- Summer term(s) for residential students;
- 1 credit hour(s) for residential students;
- Instructor(s): Sen, Ananda (Residential);
- Offered Every Summer
- Last offered Summer 2019
- Prerequisites: P.I.
- Description: It has been popular in epidemiology to conduct longitudinal studies where study participants are followed over time and repeated measurements of interest are obtained. Compared to traditional cross-sectional or case-control studies, longitudinal studies can be more efficient to detect difference of interest, offer more evidence for possible causal inference, etc. However, longitudinal data are likely to be correlated, which presents substantial challenge in analyzing such data. This course will address 1) epidemiologic methods for the design and interpretation of longitudinal studies involving repeated measures and 2) statistical methods appropriate for longitudinal data including generalized estimating equations (GEEs), linear mixed models and generalized linear mixed models. A series of studies will be used to illustrate the major design issues and statistical approaches. Relevant procedures in statistical package SAS will be introduced and appropriate interpretation of results will be emphasized. Prerequisite: Students are expected to have one or two graduate or biostatistics courses on (simple and multiple) linear regression models, categorical data analysis such as logistic regression models, and experience with conducting data analysis using statistical software SAS.