Fits a survey-weighted GLM (quasibinomial) to the trend in ever-smoking at age 25-34. This provides the "target" level for the Holford adjustment.
Usage
ever_smoke(
data,
time_horizon = 2100,
num_bins = 7,
model = "model2",
min_age = 15,
min_year = 2003,
age_cats = c("25-34")
)Arguments
- data
Data table of individual characteristics.
- time_horizon
Integer - the last year for projection.
- num_bins
Integer - bins for the period trend to reduce noise.
- model
Character - Model specification (interaction terms).
- min_age
Integer - youngest age for prediction.
- min_year
Integer - first year of survey data.
- age_cats
Character vector - age category for reference (e.g., "25-34").