But, there's a catch. Let's talk about right-censoring and why it's important 🧵
Say, 1,000 smokers and 1,000 non-smokers
The difference is non significant statistically
Does this mean that smoking isn't associated with lung cancer?
Might this make our study's results biased towards a conclusion?
This meant that the difference - even though it was there! - was impossible to see in our data
If you stop counting results too early, suddenly your study has a significant bias (often towards low/no difference)
Well, think about how these studies are conducted. We test a bunch of people randomly on day x to get an idea of how many people are immune to COVID-19 on that day
People don't die from COVID-19 immediately. It usually takes somewhere between 15-20 days from when they get infected
The data is right-censored!
a) use a statistical model to account for this issue
b) wait a few weeks and use different death estimates to correct for potential right-censoring
This will almost certainly underestimate the 'true' infection-fatality rate, and is a big worry