Courses Taught by Roderick Little
BIOSTAT593: Design for Health Studies
- Graduate level
- Both Online MPH and Online MS
- This is a first year course for Online students
- Spring-Summer term(s) for online MPH students; Spring-Summer term(s) for online MS students.
- 1 credit hour(s) for online MPH students; 1 credit hour(s) for online MS students;
- Instructor(s): Little, Roderick (Online MPH); Little, Roderick (Online MS);
- Prerequisites: Biostat 501 and Pubhlth 512
- Description: Many courses in Biostatistics focus on how to analyze data, with little attention being paid to where the data came from and how it was collected. This course focuses on the design of health investigations, with particular attention to the role of randomization in the selection of units and the allocation of treatments. The first part will focus on probability sampling designs and alternatives for the selection of units from a population. The second part concerns study designs for comparing treatments or assessing potential risk factors for health outcomes. These designs include randomized clinical trials, prospective and retrospective observational studies, and clinical data bases. Key concepts include accuracy and precision of estimates, the definition of causal effects, internal validity and the role of measured and unmeasured confounders, and external validity and the role of effect modification on the generalizability of study findings. Examples of randomized and nonrandomized studies will be included to illustrate concepts. Students will be assigned readings and asked to assess design strengths and weaknesses. Quizzes will be assigned to assess knowledge of the key concepts.
- Learning Objectives: (a) Learn key features of probability sample designs -- random sampling, stratification, clustering, multistage sampling. Understand potential limitations of purposive sampling designs, and techniques to reduce the potential bias from such designs (b) Review the main study designs for the comparison of treatments and potential risk factors for a health outcome, including randomized clinical trials, prospective and retrospective longitudinal studies, case-control studies, analyses of clinical data bases. Understand the strengths and weaknesses of these alternative designs. (c) Understand how the interpretation of statistical inferences is affected by the choice of study designs.
BIOSTAT880: Statistical Analysis With Missing Data
- Graduate level
- Fall term(s) for residential students;
- 3 credit hour(s) for residential students;
- Instructor(s): Little, Roderick (Residential);
- Prerequisites: Biostat 602 and 651, and at least one of Biostat 690, Biostat 851, Biostat 890, or Biostat 895 or Perm Inst.
- Description: Statistical analysis of data sets with missing values. Pros and cons of standard methods such as complete-case analysis, imputation. Likelihood-based inference for common statistical problems, including regression, repeated-measures analysis, and contingency table analysis. Stochastic censoring models for nonrandom nonresponse. Computational tools include the EM algorithm, the Gibbs' sampler, and multiple imputation.
- Syllabus for BIOSTAT880
EPID702: Bayesian Perspectives in Epidemiology
- Graduate level
- Summer term(s) for residential students;
- 1 credit hour(s) for residential students;
- Instructor(s): Staff Little, Roderick (Residential);
- Last offered Summer 2016
- 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.