| if_bruzzi {graphPAF} | R Documentation |
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