At 20 years old my doctor informed me I was overweight, hypertensive and would be doomed to a life of medications and health problems if I didnât change!
Hereâs the secret to how I lost 60 lbs in around a year and kept it off for over 20 yearsâŠ
Every clinician should be able to spot collider bias on sight.
The âhealthy smoker who lived to 90â is the canonical example, and yes, survivor bias is a special case of collider bias.
Other ones we see constantly:
âą Obesity paradox in CHF and COPD inpatients
âą Better outcomes in low-birth-weight babies of smoking mothers
âą Berksonâs bias in any hospital-based association study
If you condition on survival, hospitalization, or elite status, the variable that got the patient into the sample is influencing every correlation you compute downstream.
Among elite chess players, those with the lowest IQ are the best.
Among NBA players, the shortest ones are the best.
Among Hollywood actors, the least attractive are the most talented.
Among elite academics, those with poorer early academic performance are the best.
Among people with high LDL & high plaque burden, LDL is barely correlated with plaque burden.
Learn collider bias. Nice catch by @AlexTISYoung
I just had the most amazing, spontaneous moment while walking Lilla to the bus stop this morning.
While we were walking, someone sent me a birthday text.
It was beautiful, thoughtful, specific, and clearly written by someone whoâd taken real time to say something that mattered to someone who mattered. It was the kind of message you read twice.
It wasnât meant for me. Iâm 45 and itâs not my birthday. đ
But I did know the person who it was meant for, so I thanked them for the accidental kindness, let them know they had the wrong number, and couldâve just gone on with my morning.
But, hereâs the thing, I was walking Lilla to school when it came through, and I couldnât let the moment pass.
I showed it to her. I said, âThis wasnât written for me. But read it.â
She read it.
I said, âSomebody sat down and wrote that. They thought about this person and what they meant to them, and they put it into words on a random Wednesday morning. For a birthday.â
Then I said what I actually wanted her to hear:
âYou want to be the kind of person that somebody writes this for.â
I donât mean popular or well-liked; those are important, but I mean the kind of person who is so special that someone stops, opens their phone, and takes the time say something real and meaningful. Something that takes longer than âHBD.â
Because hereâs what I hope she picked up from this. Joy is not expensive. A text like that takes two minutes, a voicemail, or a handwritten note. Showing up. The bar for making someoneâs day is low, but itâs so easy to just not do the little things to make it happen. I am certainly guilty of this.
This morning someone accidentally made mine.
And my daughter got to watch me smile at something that wasnât even for me.
Thatâs the whole lesson. Be someone worth writing to, and write to the people who are worth it.
Donât wait. Donât assume they know.
Two minutes. Thatâs all it costs.
Have a great day friends đ
Great thread! IMO, one thing worth layering in: that 15-20% baseline assumes general population incidence rates.
But the denominator shifts before any test is applied. Combined lifestyle optimization (WFPBD, high-volume exercise, alcohol/tobacco abstinence, sleep quality) shows reduced cancer incidence ~30% and mortality ~50% in large meta-analyses vs. the least healthy cohort, independent of screening.
The Galleri miss this week is an interesting development: detection is our last line of defense. Iâd argue that prevention changes the prior probability that there's anything to detect.
You can't Galleri,Colonoscopy, EGD your way out of a high-inflammation, high-IGF-1, disrupted-sleep biology, but we can change that biology, and it compounds with every year we do.
Bayesâ theorem is probably the single most important thing any rational person can learn.
So many of our debates and disagreements that we shout about are because we donât understand Bayesâ theorem or how human rationality often works.
Bayesâ theorem is named after the 18th-century Thomas Bayes, and essentially itâs a formula that asks: when you are presented with all of the evidence for something, how much should you believe it?
Bayesâ theorem teaches us that our beliefs are not fixed; they are probabilities. Our beliefs change as we weigh new evidence against our assumptions, or our priors. In other words, we all carry certain ideas about how the world works, and new evidence can challenge them.
For example, somebody might believe that smoking is safe, that stress causes mouth ulcers, or that human activity is unrelated to climate change. These are their priors, their starting points. They can be formed by our culture, our biases, or even incomplete information.
Now imagine a new study comes along that challenges one of your priors. A single study might not carry enough weight to overturn your existing beliefs. But as studies accumulate, eventually the scales may tip. At some point, your prior will become less and less plausible.
Bayesâ theorem argues that being rational is not about black and white. Itâs not even about true or false. Itâs about what is most reasonable based on the best available evidence. But for this to work, we need to be presented with as much high-quality data as possible. Without evidenceâwithout belief-forming dataâwe are left only with our priors and biases. And those arenât all that rational.
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