Course Details

This course targets the application of sophisticated statistical methodologies to enhance the efficacy and precision of public health interventions. At the heart of this course is a rigorous examination of strategies for reducing bias in observational studies through advanced techniques such as imputation for missing data and propensity scores. The curriculum is expanded to include psychometric analysis, providing essential tools for the evaluation of measurement instruments, and ecological analysis, which aids in the interpretation of population-level health outcomes. A significant addition to this course is the analysis of screening and diagnostic tests, focusing on studying measures of accuracy and agreement critical for evaluating the performance of these tests in public health. The course also explores the complexities of survey design, offering insights into managing and analyzing data from multifaceted study designs to ensure the reliability and validity of intervention assessments.

Outcomes: Apply advanced statistical techniques including imputation for missing data, and propensity score analysis to reduce bias in observational studies; Evaluate the design of public health research studies and interventions for methodological soundness; Apply psychometric principles to evaluate the reliability and validity of measurement instruments used in public health research; Use ecological analysis to interpret population-level health outcomes, understanding the impact of environmental and social factors on public health.