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Glossary of Pharmacology 

observational studies

Definition: 

Observational studies are studies that involve observing an outcome of interest in a group of participants in regular settings, without researchers assigning interventions. Researchers collect data on participants’ habits, environment, risk factors, or other factors that may affect the outcome being studied and try to find correlations between them.  

Relevance:  

While clinical trials are crucial for drug development, observational studies provide additional information not always obtained from clinical trials. For example, observational studies allow for the assessment of long-term effects of approved drugs in real world conditions, providing data on their efficacy and safety in patient populations. Moreover, these studies are typically larger and more inexpensive, allowing for the evaluation of rare outcomes that may not have been observed during clinical trials. Therefore, observational studies provide valuable insights in pharmaceutical research, highlighting its significance in drug development.    

    Teaching Tips:  

    https://www.youtube.com/watch?v=kaoWPEzvf9A 

     What is clinical research and why is it done? https://clinicaltrials.gov/study-basics/learn-about-studies  

    Linked terms: Clinical trials, Randomized controlled trials, Drug development   

    Resources:  

    Gilmartin-Thomas, J. F., Liew, D., & Hopper, I. (2018). Observational studies and their utility for practice. Australian prescriber, 41(3), 82–85. https://doi.org/10.18773/austprescr.2018.017  

    What Are Clinical Trials and Studies? (2023, March 22). National Institute on Aging. https://www.nia.nih.gov/health/clinical-trials-and-studies/what-are-clinical-trials-and-studies  

    What is clinical research and why is it done? https://clinicaltrials.gov/study-basics/learn-about-studies  

    What Are Observational Studies? https://www.cancer.gov/research/participate/what-are-observational-studies#:~:text=Observational%20studies%20are%20research%20studies,data%20that%20was%20already%20collected. 

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