Effect Modification

[@UzoamakaEdithOkwor]

my subscription and like was just the least I could do to say thank you

[@nancyzambrana3550]

You have no idea how confused I was, after watching your video I can finally say: I got it! Thank you Sir!

[@drboston]

Thanks Dr.Shaneyfelt! Much more clear and short explanation better than my epi professor!

[@RostamiMehdi]

Confounding is when a third variable falsely makes it seem like there's a causal relationship between two other variables. Effect modification is when the strength or direction of a causal relationship varies based on another factor. The key difference is that confounding distorts the observed relationship, while effect modification reveals different relationships in subgroups.

[@kuntalbandyopadhyay3693]

Wow..well explained sir...im a preventive health specialist...it cudnt hv been explained in better or simpler way..good job

[@obinnadaniel2001]

This was awesome! Crystal clear explanation

[@jamesrobertson9149]

this is a wonderfully clear explanation.

[@kaitdoeschem9181]

Thank you, Dr. Shaneyfelt. I use your videos to study for my epidemiology exams (MSPH 7001 at Meharry Medical College).

[@fienamerzistya9528]

Thank youu sooo much for helping me Dr. Terry, this video SAVED MY LIFE!!! :')

[@fazilehkhani213]

THIS VIDEO SAVED MY LIFe thank youuuu

[@MohamedGamal-bn3xe]

Thanks so much, great and simple illustration

[@ShafiKhan-km4lw]

Bundle of thanks sir, now i got it

[@Yassin-Ghassan-Taha]

Thank you very much Dr Terry for the excellent video..

[@carolineoreardon2229]

Great explanation! Thank you.

[@katherinekcarreromartinez8236]

thank you from Puerto Rico!

[@dr.anantsapra1114]

I think the age and smoking combo with lung ca can be both effect mod and confounding. 1. Different age smoker have different outcomes( effect mod) 2. Higher age is having 2-3x more dev of lung ca as compared to young age. Also old age person tends to smoke more as compared to young person. (As seen in asia/ india) like confounder.

[@kliphlove3458]

Good job as usually Dr. Shaneyfelt- I miss UAB!

[@carolynh6852]

This finally cleared it up fo me. Thanks Dr. S!!!

[@drokraebube2983]

Could age have been an effect modifier in covid deaths? Considering covid infection as the exposure? Just saying thank you so much for the wonderful explanation. It is now more clear to me.

[@valeriepasquale3741]

This was so helpful! Thanks!

[@sharlotkantonieta]

Thank you so much doctor!

[@hannahjenkins4698]

So helpful - thank you!

[@MohamedKandilMD]

Very helpful explanation thanks a lot

[@heidihi547]

Very helpful. Thanks

[@juliawang3055]

this was amazingly helpful! Is there a way to differentiate between confounders, effect modification, and interaction?

[@el.priest6518]

Great videos!

[@basmaowaidy8512]

thank you so much that was helpful !!

[@mariafridh25]

Excellent explanation! Thank you so much!

[@orthopedicmbbs]

kzbin.info/www/bejne/bYqTlHeVi96nbZI

[@rajeshshigdel1472]

Excellent lecture

[@gonzalomunoztapia]

Thank you, very clear and useful.

[@jlngweshemi]

This is very very helpful!!

[@pmahcthitikorn]

Helpful

[@khurrumshehzad718]

thanks dr,,,i m ur fan

[@indrajitsaha6099]

A study was conducted to assess the extrapyramidal side effects of a new antipsychotic drug in patients with schizophrenia. Many of these patients were smokers and some of them were on anticholinergic drugs. What was the role of the anticholinergic drugs in this study?

[@UABEBMcourse]

Would have to test like i suggest at the 4 minute mark or so. Probably confounder is my guess but its only a guess.

[@yvesburtworthington3244]

outstanding!!!

[@markcheruiyot9934]

useful information

[@stevegerrish6720]

Thanks so much!

[@vipinkravi]

thank you Terry

[@tracyquetzal9477]

How did you calculate if the effect modification was significance? Where does the p value come from?

[@zzgrace5599]

good

[@imanibessem9080]

THANK YOU SOOOOOOO MUCH!!!

[@MelbourneMaster]

Ive seen examples of age and sex being a confounder. So I still dont see how to differentiate

[@susheelgautam]

thanx a lot

[@rohanshinkre]

Thank u

[@fatboy117]

there has to be an easier way to explain this

[@hindhader]

please do cause i am not following this at all. lol and i need to know cause im writing my thesis

[@Tntpker]

@@hindhader Like he explained in the video, a confounding variable (smoking in the video) is not involved in the causal chain of the effect of Hormone Replacement Therapy (HRT) on Cardiovascular disease (CVD). However, smoking itself DIRECTLY effects Hormone replacement therapy and CVD (because smoking has been proven to be bad for CVD). Therefore, it basically 'skews' the effect of HRT on CVD (which you're trying to find out) in people who smoke, compared to people who don't smoke and you need to adjust for this. Smokers will LIKELY have more CVD problems than non smokers, so you will naturally find more CVD problems in people who smoke. Now effect modifcation IS involved in the causal chain of HRT -> CVD, and thus this means that it is a *biological phenomenon* . In the video the variable 'age' is mentioned. When you stratify according to age, the age of a patient 'modifies' the effect of HRT on CVD, it changes the effect (older people have less elastic artery walls, and therefore the effect of HRT on CVD may change with age).

[@chitritaghosh4279]

@Tntpker Thank you very much for your lucid explanation of the difference .. Feeling stress free. I have a seminar on bias in clinical trial in this week. And I was fighting for a clear Idea about this thing. You just saved me.

[@orthopedicmbbs]

kzbin.info/www/bejne/bYqTlHeVi96nbZI