BERD KCA Training Unit

The BERD KCA Training Unit collaborates and integrates with the OSCTR Clinical Research Education, Mentoring, and Career Development KCA to enhance training in epidemiology and biostatistics related to clinical and translational research. 

BERD Education Online Request form 

Services Provided

• Create and offer seminars, short courses, and workshops related to the application of biostatistical and epidemiological methods to study design and data analysis
• Participate in clinical or translational working groups or journal clubs
• Provide curricular materials for asynchronous learning, including print and video-streamed seminars, short courses, and tutorials.

Curricular Materials 

Printed Materials (in .pdf format)

• Statistical tests
• Sensitivity and specificity, predictive values, and Bayes Theorem
• What is logistic regression
• Analyzing outcomes measured as a “time to event”
• Agreement between measurements - Bland Altman plots
• Meta-analysis
• Standardized Mortality Ratios (SMR)

Video-streamed tutorial (Mediasite)

• Power and Sample Size 1
• Power and Sample Size 2
• Statistical thinking: Quantifying certainty and uncertainty
• Statistical significance: Alternatives to the p-value
• Analyzing outcomes measured as "time to event"

Foundations of Biostatistics and Epidemiology

This 8-part video series will prepare you to understand and apply principles and methods in biostatistics and epidemiology that are used in medical research. Topics addressed include the development of a research question; research design principles; statistical and epidemiologic measures of disease burden, distribution, and association; and methods of statistical inference and hypothesis testing.

R Short Course

The Training Unit of the Biostatistics, Epidemiology, and Research Design (BERD) Key Component Activity of the Oklahoma Shared Clinical and Translational Resources (OSCTR) hosts an R Short Course. R is a language and environment for statistical computing and graphics. It is free and compiles and runs on various UNIX platforms, Windows, and MacOS. R can be extended via user-contributed packages. Packages are available through the CRAN family of internet sites covering a wide range of modern statistics (https://www.r-project.org/).