Learning gaps among statistical competencies for clinical and translational science learners

Area of Science:

• Clinical and Translational Science (CTS)
• Statistical Education

Background:

• Statistical literacy is crucial for clinical and translational science (CTS).
• Existing statistical competencies guide graduate coursework for CTS students.
• This study identifies common elements and gaps in graduate CTS statistical curricula.

Purpose of the Study:

• To describe common elements of graduate curricula for CTS.
• To identify gaps in statistical competencies within CTS graduate programs.
• To inform the development of targeted educational materials.

Main Methods:

• Surveyed statistics educators via email through four professional organizations.
• Respondents rated the inclusion of 24 statistical competencies in required and elective coursework.
• Data collected from institutions with Clinical and Translational Science Awards (CTSAs).

Main Results:

• 24 CTSA-funded institutions participated, representing 13 doctoral and 23 master's programs.
• Doctoral programs extensively covered probability, hypothesis testing, and method selection implications.
• Master's programs extensively covered only method selection implications; meta-analysis and early stopping rules were least covered.

Conclusions:

• Graduate CTS courses cover fundamental statistical concepts but have gaps in specialized areas.
• Significant learning gaps exist, especially in advanced statistical competencies.
• There is a need for educational resources to address these identified gaps in CTS statistical training.