Biostatistics: ROC Curve | USMLE Step 1 Crash Course

Figures and Curves are common in biostats, and the ROC curve is quite a challenging one. Hopefully with this short video, you'll be able to understand it a lot better!
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Sensitivity and Specificity: • Biostatistics: Evaluat...
📝 Content:
00:00 ROC Curve
📝 Notes/Errata:
1) None; please comment if you found an inconsistency and I'll be sure to add it here along with the time stamp!
Social Media:
🐦 Twitter: Khalemedic
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🙋🏻‍♂️ Who am I?
I'm Khaled, a final-year medical student, and I make videos on quite a few different topics within the scope of medicine such as reacting and breaking down medical shows, studying for exams, discussing important health issues, etc... I hope that my videos help you in any way, shape, or form and that you enjoy them regardless of your background.
Medicine is quite the challenging topic, but I don't see why we can't enjoy it together!
Disclaimer: The information and education material contained herein is meant to promote the general understanding of medical topics by healthcare professionals and related parties. Such information is not meant or intended to serve as a substitute for a healthcare professional's clinical training, experience, or judgment. For patient’s care or regarding your own health please consult your doctor or refer to the most recent evidence based practice.

[@lipikapal2878]

I got 2 questions from this curve in my Step 1 exam. Thank you so much for the explanation. It really helped me to answer those questions.

[@poonamp9559]

This was the simplest explanation I could find. Thank you so much

[@Khalemedic]

You’re most welcome! 🙏🏼

[@cyrilu2146]

Simplified ! Thank you.

[@doctormk58]

Great quick review, like I love it 🤠

[@ajkovic914]

Simple and straight to the point. Thank you

[@lors6739]

Thank you for this short and clear video. am very grateful!

[@chideraphilippa7619]

I just want to say thank you for this video! You just saved me a lot of stress right now.

[@lluminascent]

This was the best explanation i could find. Thank you!

[@Khalemedic]

Thank you for the kind words!

[@ahmedhelmy3173]

thank you! I 'd been struggling for a while until I found this video :D

[@amsemidfifty7457]

The simplest and very clear explanation.. Thank you

[@Khalemedic]

You’re most welcome! 🙏🏼

[@mahnoorwaqas7985]

Thanks! 🎉 Loved this simple explanation. Easy to remember

[@sneh4191]

Amazing explanation and to the point

[@lin5750]

Thank you Dr

[@fahadwaraich6814]

thnkyou. short and precise

[@Khalemedic]

You’re welcome, Fahad! I’m glad you found the video useful.

[@alancabrera7116]

Obviously, these videos are super helpful. JFYI, these videos are supplemental study material for an A.I. study group that I'm mentoring. Your slides are so nicely laid out. I suspect that you really wanted to be an industrial designer but your parents made you go into medicine. :) Many thanks!

[@Khalemedic]

Umm… okay.

[@joaobrum3483]

Excellent!!

[@Khalemedic]

Thank you!

[@Rasha.Ishtar]

Thank you so much you made it so much easier for me 🤗

[@pratik9841052968]

Great Explanation

[@delfiaj2417]

Well explained!! Thank you so much.

[@Khalemedic]

You’re most welcome!

[@manufonseca1464]

wow amaizing!! thanks!!

[@Khalemedic]

You’re most welcome!

[@step_zero]

Dig the video. Good to see more Medutubers unite ! subbed

[@rajaniprabha]

Thankyou, It is really helpful Sir

[@Khalemedic]

You’re most welcome, Paramita! I’m glad you found it helpful.

[@michaeldawson4234]

Awesome, thank you!

[@Khalemedic]

You’re the one who’s awesome, friend! 👉🏼👉🏼

[@MUHAMMADUMAR-je5uo]

Nice work .

[@Khalemedic]

Thank you!

[@iidmukhtarjama9245]

Very precise and understandable

[@cocoarecords]

really well explained

[@Khalemedic]

Thank you, friend! I’m glad you benefitted! 🙏🏼

[@Jefe2024]

Much appreciated!

[@Khalemedic]

🙏🏼

[@sm4887]

Thank you so much god bless

[@sm4887]

Brilliant

[@drsurajkp]

Nice video

[@qurratulainmansoor8765]

Hi, thank you so much for the amazing explanation. I want to ask regarding the last point that you taught. lets say, if the question asks us to choose the curve with highest specificity, and they have given different curves with different AUCs. Will we still choose the left most point curve? like if the point which is the left most among all the curves has its AUC lower than the curve whose specificity point is relatively to the right but its AUC is more. which curve will have highest specificity? I got it complicated, hope you get my question!

[@Khalemedic]

Great question! I understand where it feels complicated. My understanding is that every line represents a test and the different points along that line represent different cutoffs. For such a question to be valid, they would have to focus on ONE point and DIRECTLY compare it to another point. When it comes to AUC, it is just a simple way to tell if one test if better performing than the other. I doubt a question would show you two lines and then ask you which test is more specific as it truly depends on which cutoffs they specify. Such a question where the curves are intermixed such that specific cutoffs can be found more on the left with the overall AUC less than another test is in my opinion a very unfair question that doesn’t target any true objectives of the concept. I hope that made sense. If anyone else can shed light on this, I would appreciate seeing your thoughts.

[@qurratulainmansoor8765]

@@Khalemedic It is immensely helpful, thanks so much. It does make sense. Much appreciated!

[@MysticMango412]

If the question asked, which line is "most accurate", which line should you pick?

[@Khalemedic]

You should select the line with the highest area under its curve.

[@MysticMango412]

@@Khalemedic thanks!

[@cocoarecords]

how are you able to digest such things and understand them well? do you have any approach you can share with us will be very grateful

[@Khalemedic]

I do plan on making a “How to Study Productively” video so stay tuned 😉

[@cocoarecords]

@@Khalemedic Awesome subbed. i believe many of these videos miss the systematic approach they use on their courses and focus on time management mostly

[@statseek6117]

Hi can we find sample size based on AUC?

[@Khalemedic]

Hmm, that’s a neat question. If I recall correctly, the AUC does not tell you anything about the sample size. The ROC curve in general is a visual depiction of the different cutoffs that could be used as well as their associated sensitivities and specificities. These are good measures to highlight how well the test can rule out or rule in disease. I don’t believe they say anything about the sample size.

[@yeknomican]

MONEY!