Novel statistical and data science methodology developments are often motivated by the increasingly complex data collected for research studies that involve a disease or health outcome. For example, methodological studies motivated by breast cancer or Alzheimer’s disease are common. However, whether the effort for methodological development is appropriately being used for diseases that affect the global population the most is unknown.

In 2020, Lancet published the updated global burden of 369 diseases and injuries in 204 countries and territories. For each top 25 disease, using an automated systematic literature review, we will identify all published methodological research motivated by the disease.

Then we will:

1. Assess the relationship between the common global diseases and the common diseases that motivate methodological research.
2. Identify global diseases that are being “neglected” by methodologists.

The student will learn how to conduct an automated systematic literature review, text analysis, produce professional tables, figures, and graphics. The student will have the opportunity to be a co-author of a paper in an academic journal. The R statistical programming language will be primarily used, but other programming languages may be considered based on the student’s proficiency.