Biostatistics Short Courses

A Biostatistics Short Course

A six-week short course for those wanting to become acquainted with basic clinical trial methodology, either to broaden their knowledge of public health and clinical research, or to obtain a basis for more advanced study of the design and conduct of clinical trials. Introduces clinical trial design in the context of epidemiological concepts, covers various topics in the design and conduct of clinical trials, and profiles clinical trials that illustrate these issues.  

Schedule of Topics

• Study Design History and Comparisons
• Identifying the Research Hypotheses and Study Population, Collecting Data
• Exploration of Clinical Trial Designs I
• Exploration of Clinical Trial Designs II and Analyzing the Data
• Sample Size, Power, and Sequential Monitoring
• Novel Study Designs and Reporting Results

A credit card can be used for payment. To inquire about using a Speedtype, please contact cida.admin@cuanschutz.edu.

Sponsored by the Biostatistics, Epidemiology, and Research Design (BERD) core of the CCTSI.​

A Biostatistics Short Course

A Biostatistics Short Course

A six-week course typically offered in the fall. The Fundamentals of Statistical Literacy Short Course will help you improve your ability to critically assess literature and understand and communicate biostatistical issues.  Topics are introduced and discussed in the context of the research process and tied back to phases of research that investigators will understand.

Material is presented in short videos, in-class lecture and discussion, and group study and presentation of specific case studies taken from real-world scenarios.  This class will not teach you how to carry out the analyses themselves, as there is no coding and minimal computation.

A Biostatistics Short Course

A six-week short course offered in the spring for those wanting to follow-up to the companion course to the Fundamentals of Statistical Literacy I. This course will help you improve your ability to critically assess literature and understand and communicate biostatistical issues. Topics are introduced and discussed in the context of the research process and tied back to phases of research that investigators will understand.

Material is presented in short videos, class lecture and discussion, and group study and presentation of specific case studies taken from real-world scenarios. This class will not teach you how to carry out the analyses themselves, as there is no coding and minimal computation.

Schedule of Topics

• Good Science Through Good Statistical Thinking and Decision Making
  
• Covariate Adjustment and Model Selection
  
• Models for Non-Normal Data
  
• Repeated Measures and Longitudinal Data Analysis
  
• Missing Data and Imputation
  
• Censoring, Truncation, and Survival Analysis

A credit card can be used for payment. To inquire about using a Speedtype, please contact cida.admin@cuanschutz.edu.

Sponsored by the Biostatistics, Epidemiology, and Research Design (BERD) core of the CCTSI.​

A Biostatistics Short Course

A six-week short course for researchers or students wanting to learn more about foundations and applications of data visualization. The course has three segments with 2 lectures each. The first segment introduces visualization foundations and discusses general design principles for creating effective visual representations. The second segment covers the visualization of two common data types (multidimensional data and network data), along with tools for visualizing and analyzing tabular and network data. The third segment covers interactive data exploration in the context of time series and text data, and the use of visual representations for communication and storytelling. Coding skills are not required for this course.​

This is different from Fundamentals of Data Science and is a great companion to the course in theme and information although they are not connected nor is one a prerequisite for the other.

Schedule of Topics

• Introduction to Data Visualization: What, Why, and How
• Designing Graphical Figures and Tables
• Visualizing Tabular Data & Introduction to Tableau Application
• Visualizing Networks and Trees & Introduction to Cytoscape Application
• Interactive Data Exploration & Visualizing Time Series and Text
• DataVisual Communication and Storytelling
  

A credit card can be used for payment. To inquire about using a Speedtype, please contact cida.admin@cuanschutz.edu.

Sponsored by the Biostatistics, Epidemiology, and Research Design (BERD) core of the CCTSI.​

A Biostatistics Short Course

A six-week short course for researchers or students wanting to learn more about the foundations of Electronic Health Records-based research. This course provides a practical introduction to EHR data and the study design process. Bring your research question with you as we will also walk through the overall process of conducting an EHR-based study on the CU campus including the COMIRB application and Health Data Compass request process. Finally we will touch on how to combine EHR data with other resources on campus like the Colorado Center for Personalized Medicine Biobank. Coding skills are not required for this course.

Schedule of Topics

• Fundamentals and Ethics of EHR-based Research
  
• Introduction to EHR Data
  
• Defining the Study Population, Exposures, and Outcomes
  
• Assessing Study Feasibility and Preparing the Data Request
  
• Requesting, Receiving, and Analyzing Data
  
• Combining with Specialty Data Sources (e.g., Colorado Center for Personalized Medicine Biobank)
  

A credit card can be used for payment. To inquire about using a Speedtype, please contact cida.admin@cuanschutz.edu.

Sponsored by the Biostatistics, Epidemiology, and Research Design (BERD) core of the CCTSI.​

How does one obtain the correct answers? By asking the right questions! This course is an introduction to causal inference as one of four core data science tasks in the health sciences. The course will focus on causal inference in epidemiology, including use of directed acyclic graphs (DAGs) to illustrate research questions, identify sources of bias, and aid in interpretation of findings.

Schedule of Topics: 

1. Data science tasks: asking the right questions to get the right answers
2. Causal diagrams (DAGs): key elements and how each relate to causal inference
3. To adjust or not adjust? Confounder adjustment, overadjustment, and unnecessary adjustment
4. Overadjustment – when mediation analysis goes wrong
5. Interaction and effect modification 
6. Conceptual models