How to optimize observational studies to provide high quality real world data?

Ömer Dizdar, M.D., Prof.

Medical Oncology, Gastrointestinal Cancers, Sarcoma/Melanoma,

Hacettepe University Cancer Institute, Turkiye

Summary

The aim of this e-learning module is to touch on how to optimize observational studies in order to provide high quality real world data by comparing ups and downs of observational studies with randomized studies. Randomized studies are gold standard design for evaluating the effectiveness of a treatment by eliminating systemic differences between treated and untreated patients. Besides, observational studies provide real world data on treatment efficacy under real life conditions in patients encountered in a day-to-day clinical practice. One of the advantages of observational studies is allowing large sample size and long follow-up period with relatively lower cost. On the other hand, selection biases, misclassifications, and confounding should be considered to optimize study design and when interpreting the results in observational studies.

This e-learning module was developed by: the EFIM Academy.

Target audience

• This e-learning module is suitable for internists, but is also built to serve internal medicine trainees and physicians in other disciplines, as well as health care professionals who have an interest in infectious diseases.
• Additional Information
• This e-learning module is designed to last approximately 1 hour in total.
• The author has declared his conflicts of interest in relation to the material in this e-learning module.
• The production of this e-learning module was supported by Sanofi Pasteur.
• The material used in this e-learning module was prepared in 2022.
• Unless otherwise stated, all display items are provided courtesy of the e-learning module authors.
• Learners are invited to test their knowledge at the beginning and the end of the e-learning module via 4 questions.
• The e-learning module has a pass mark of 75%. Those scoring less than the required pass mark are encouraged to revisit the e-learning module and retake the assessment.

Released on June 27, 2023.