A course on thoughtful observational study design.
Healthcare systems generate enormous volumes of data in various forms, with electronic health records and administrative claims in particular serving as rich sources of information. These real-world health data sources are potential reservoirs of insights for biomedical discovery. Real-world data, however, arises from a data-generating process, shaped by clinical workflow and billing practices, that is distinct from the true biomedical processes we aim to study. This can make distilling reliable insights from these observational data sources challenging. This course introduces the theoretical foundations and practical methods required for addressing these challenges through rigorous observational study design using real-world health data that can be used to generate reliable evidence to answer a range of clinical questions.
Each course topic is developed through three layers of theory, application in informatics, and implementation through tools developed by the Observational Health and Data Sciences and Informatics (OHDSI) consortium, which has developed analytic tools based on best practices for high-quality observational studies. The theory for this course is developed around the frameworks of probabilistic and causal inference, to facilitate understanding of study-design principles and analytic approaches that are thoughtfully informed by domain knowledge. We use theory to understand the challenges and biases of real-world health data, and how observational study design choices and methods can address them. Each stage of the observational study pipeline is covered including formulating a precise research question, identifying the right data, defining study populations and phenotypes, and determining and executing the appropriate analyses.
Tools and conventions developed by the OHDSI community will serve as a practical framework for implementing these concepts. We will examine how OHDSI tools can be used to quickly translate a well-designed research question into an executable study and to evaluate whether the resulting estimates are credible. Each class meeting includes both lecture and hands-on exercises so that concepts are translated into practice as they are introduced.
Twelve weeks, each moving from theory to application to tool. Week titles to come.
Putting theory and application into practice with OHDSI tools. Assignments not finished in class are due before the next session.
An independent or paired OHDSI-style observational study, developed in consultation with the instructor, answering a clinically relevant research question of your choosing.
Attendance is expected at every session (two weeks' notice for planned absences; 24 hours or ASAP for illness). Assessed through active engagement in lecture and lab.
Work independently or in pairs to design and execute one or more OHDSI-style observational studies. The goal is to leave the course with a manuscript draft ready to submit to a clinical journal.
A journal-style research report formatted for a clinical journal, presenting your research question, analytical approach, results, and interpretation of findings.
A reflection piece describing a more thorough account of your research process, including intermediate steps that won't be reflected in the manuscript itself.
You will access to and analyze real patient health data for educational purposes. This data is protected under HIPAA, and every student is bound by the corresponding standards of appropriate use.
You're generally encouraged to use GenAI tools as you see fit throughout the course, with the data-security exceptions above. For any submitted work, you'll be asked to describe with some specificity how you used GenAI. If your use appears to have hindered your understanding of key concepts or prevented you from submitting accurate, high-quality work, you'll meet with the instructor to discuss it and revise and resubmit with that guidance.
Recording or transcribing lectures with any tool isn't permitted, except with written approval from the accommodations office. Slides are posted after each class.
Use GenAI to debug code or otherwise support your work, as long as it doesn't hinder your comprehension or compromise data security, including tools with full file access, such as Claude Code, that could read your database connection details directly.
Same allowances and exceptions as weekly assignments. You're encouraged to develop your research ideas, write-ups, and slides on your own as each has requirements of accuracy and specificity you're unlikely to meet without your own critical thinking.