Internal: Calculation of an impact fraction using the Bruzzi approach
if_bruzzi(data, ind, model, model_type, new_data, response, weight_vec)A numeric estimated impact fraction.
A dataframe containing variables used for fitting the model
An indicator of which rows will be used from the dataset
Either a clogit or glm fitted model object. Non-linear effects should be specified via ns(x, df=y), where ns is the natural spline function from the splines library.
Either a "clogit", "glm" or "coxph" model object
A dataframe (of the same variables and size as data) representing an alternative distribution of risk factors
A string representing the name of the outcome variable in data
An optional vector of inverse sampling weights
Bruzzi, P., Green, S.B., Byar, D.P., Brinton, L.A. and Schairer, C., 1985. Estimating the population attributable risk for multiple risk factors using case-control data. American journal of epidemiology, 122(5), pp.904-914