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Simple approach:
Corresponding to the frequentist model, in the first instance we assume we know with essentially no uncertainty the general population background mortality, but using the points values from life tables.
Life tables are over large populations so could consider the point values to be accurate
Better approach:
(Bayesian) Model the survival by ensuring that the model parameters are such that you "anchor" the survival to known values (while allowing for uncertainty).
Stratified by country and sex
How to include uncertainty:
Could fit a survival curve to the life table data (life table survival is log linear for adults) and then add uncertainty to the parameters
Or directly on each age. Separate/exchangeable uncertainty at each age or the same?
The text was updated successfully, but these errors were encountered:
Simple approach:
Corresponding to the frequentist model, in the first instance we assume we know with essentially no uncertainty the general population background mortality, but using the points values from life tables.
Better approach:
(Bayesian) Model the survival by ensuring that the model parameters are such that you "anchor" the survival to known values (while allowing for uncertainty).
The text was updated successfully, but these errors were encountered: