if_bruzzi: Internal: Calculation of an impact fraction using the Bruzzi...

In graphPAF: Estimating and Displaying Population Attributable Fractions

Internal: Calculation of an impact fraction using the Bruzzi approach

Description

Internal: Calculation of an impact fraction using the Bruzzi approach

Usage

if_bruzzi(data, ind, model, model_type, new_data, response, weight_vec)

Arguments

data	A dataframe containing variables used for fitting the model
ind	An indicator of which rows will be used from the dataset
model	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.
model_type	Either a "clogit", "glm" or "coxph" model object
new_data	A dataframe (of the same variables and size as data) representing an alternative distribution of risk factors
response	A string representing the name of the outcome variable in data
weight_vec	An optional vector of inverse sampling weights

Value

A numeric estimated impact fraction.

References

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

graphPAF documentation built on Feb. 16, 2023, 6:24 p.m.