Courses Details
BIOSTAT593: Design for Health Studies
- Graduate level
- Online MPH only
- This is a first year course for Online students
- Spring-Summer term(s) for online MPH students;
- 1 credit hour(s) for online MPH students;
- Instructor(s): Roderick Little (Online MPH);
- 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.
