if_direct: Internal: Calculation of an impact fraction using the direct...

In graphPAF: Estimating and Displaying Population Attributable Fractions

Internal: Calculation of an impact fraction using the direct approach

Description

Internal: Calculation of an impact fraction using the direct approach

Usage

if_direct(
  data,
  ind,
  model,
  model_type,
  new_data,
  prev,
  t_vector,
  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, glm or coxph 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
prev	Population prevalence of disease (default NULL)
t_vector	A vector of times at which PAF estimates are desired (for a coxph model)
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.

graphPAF documentation built on May 29, 2024, 10:21 a.m.