• super simple video
  • you can't distinguish these. it's literally impossible
  • but we want to
• Age effect affects everyone of that age. People tend to forget about works after 10 years or 20 years, etc, and devalue the knowledge.
• Period effect affects everyone in that year. In 1987 there were less citations total than in 1986.
• Cohort effect affects everyone born in that year. This should be most important. Same local intellectual atmosphere.
• this talk gives a nice introduction
• lifelines package in Python
• pyMC3 and categorical variables
• this guy had a crazy SO answer
• some books -- the social sciences one looks best
• MIT lecture notes on survival analysis
• this notation looks nice

The outcome I'm interested in is the probability of death.
I should be able to run this regression. They aren't perfectly colinear in my dataset. I have a huge longitudinal study.
I could just estimate a huge number of fixed effects.

survival analysis

As I dig deeper, I need to account for people being nested. Maybe survival analysis does better at this?
first tutorial

it would fix my censored data problem