impact_fraction: General calculations of impact fractions

In graphPAF: Estimating and Displaying Population Attributable Fractions

General calculations of impact fractions

Description

General calculations of impact fractions

Usage

impact_fraction(
  model,
  data,
  new_data,
  calculation_method = "B",
  prev = NULL,
  ci = FALSE,
  boot_rep = 50,
  t_vector = NULL,
  ci_level = 0.95,
  ci_type = c("norm"),
  weight_vec = NULL
)

Arguments

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.
data	A dataframe containing variables used for fitting the model
new_data	A dataframe (of the same variables and size as data) representing an alternative distribution of risk factors
calculation_method	A character either 'B' (Bruzzi) or 'D' (Direct method). For case control data, the method described in Bruzzi 1985 is recommended. Bruzzi's method estimates PAF from relative risks and prevalence of exposure to the risk factor. The Direct method estimates PAF by summing estimated probabilities of disease in the absence of exposure on the individual level
prev	estimated prevalence of disease. This only needs to be specified if the data source is from a case control study, and the direct method is used
ci	Logical. If TRUE, a bootstrap confidence interval is computed along with point estimate (default FALSE)
boot_rep	Integer. Number of bootstrap replications (Only necessary to specify if ci=TRUE)
t_vector	Numeric. A vector of times at which to calculate PAF (only specified if model is coxph)
ci_level	Numeric. Default 0.95. A number between 0 and 1 specifying the confidence level
ci_type	Character. Default norm. A vector specifying the types of confidence interval desired. "norm", "basic", "perc" and "bca" are the available methods
weight_vec	An optional vector of inverse sampling weights for survey data (note that variance will not be calculated correctly if sampling isn't independent). Note that this vector will be ignored if prev is specified, and the weights will be calibrated so that the weighted sample prevalence of disease equals prev.

Value

A numeric estimated impact fraction if ci=FALSE, or for survival data a vector of estimated impact corresponding to event times in the data. If ci=TRUE, a vector with elements corresponding to the raw estimated impact fraction, estimated bias, bias corrected estimate and lower and upper elements of any confidence procedures requested. If ci=TRUE, and a coxph model is fit, a matrix will be returned, with rows corresponding to the times at which the impact fraction is calculated.

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

Examples

library(splines)
library(survival)
new_data <- stroke_reduced
N <- nrow(new_data)
inactive_patients <- (1:N)[stroke_reduced$exercise==1]
N_inactive <- sum(stroke_reduced$exercise)
newly_active_patients <- inactive_patients[sample(1:N_inactive,0.2*N_inactive)]
new_data$exercise[newly_active_patients] <- 0
model_exercise <- clogit(formula = case ~ age + education +exercise +
ns(diet, df = 3) + smoking + alcohol + stress + ns(lipids,df = 3) +
ns(waist_hip_ratio, df = 3) + high_blood_pressure +strata(strata),
data=stroke_reduced)
impact_fraction(model=model_exercise,stroke_reduced,new_data,
calculation_method = "B")

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