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Research Methodology Podcasts

Pros and Cons of Non-Inferiority Trials

There are multiple papers that question the ethics of doing non-inferiority trials, because it’s probably going to end up with treatments being recommended from patients that are inferior to other treatments. And that’s where the controversies come out. The reason a lot of people liked them is that, you can get significant results with very small sample sizes. All I have to do to set it basic way down and set that criterion and way down there, and everything is going to come out significant. 

Three types of trials are conducted to compare a new treatment to the standard treatment:

  • superiority trials seek to understand which is superior , the new treatment of the standard treatment
  • non-inferiority trials seek prove that the new treatment is not inferior to standard treatment
  • equivalence trials seek to explore if the new and the standard treatments are equivalent.

To learn more about the pros and cons of superiority trials and non-inferiority trials from Dr. Helena Kraemer listen to the podcast episodes below.

VJ Periyakoil · Noninferiority Trials Part 1 of 2
VJ Periyakoil · Non-Inferiority Trials Part 2 of 2

Click here for more Research Methodology Podcasts

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Previous Post: « Veterans and Palliative Care with Dr. VJ Periyakoil
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Next-Generation Artificial Intelligence for Diagnosis: From Predicting Diagnostic Labels to "Wayfinding"

Julia Adler-Milstein, PhD1; Jonathan H. Chen, MD, PhD; Gurpreet Dhaliwal, MD

Improving the diagnostic process is a quality and safety priority.With the digitization of health records and rapid expansion of health data, the cognitive demand on the diagnostician has increased. The use of artificial intelligence (AI) to assist human cognition has the potential to reduce this demand and associated diagnostic errors. However, current AI tools have not realized this potential, due in part to the long-standing focus of these tools on predicting final diagnostic labels instead of helping clinicians navigate the dynamic refinement process of diagnosis. This Viewpoint highlights the importance of shifting the role of diagnostic AI from predicting labels to “wayfinding” (interpreting context and providing cues that guide the diagnostician).