graphPAF (version 2.0.0)

PAF_calc_discrete: Calculation of attributable fractions using a categorized risk factor

Description

Calculation of attributable fractions using a categorized risk factor

Usage

PAF_calc_discrete(
  model,
  riskfactor,
  refval,
  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,
  verbose = TRUE
)

Value

An estimated PAF if ci=FALSE, or for survival data a vector of estimated PAF corresponding to event times in the data. If ci=TRUE, a vector with elements corresponding to the raw estimate, 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 differing times at which the PAF might be calculated.

Arguments

model

Either a clogit, glm or coxph fitted regression object. Non-linear effects can be specified in these models if necessary via ns(x, df=y), where ns is the natural spline function from the splines library.

riskfactor

The name of the risk factor of interest in the dataset. The risk factor values can be 0/1 numeric, categorical or factor valued.

refval

The reference value for the risk factor. If a risk factor is 0/1 numeric, 0 is assumed as the default value, otherwise refval must be specified.

data

A dataframe containing variables used for fitting the model

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

The estimated prevalence of disease (A number between 0 and 1). 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). Note that at least 50 replicates are recommended to achieve stable estimates of standard error. In the examples below, values of boot_rep less than 50 are sometimes used to limit run time.

t_vector

Numeric. A vector of times at which to calculate PAF (only specified if model is coxph)

ci_level

Numeric. A number between 0 and 1 specifying the confidence level

ci_type

Character. A vector specifying the types of confidence interval desired, as available in the 'Boot' package. The default is c('norm'), which calculates a symmetric confidence interval: (Est-Bias +- 1.96*SE), with the standard error calculated via Bootstrap. Other choices are 'basic', 'perc' and 'bca'. Increasing the number of Bootstrap repetitions is recommended for the 'basic', 'perc' and 'bca' 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 will be ignored if prev is specified and calculation_method="D", in which case the weights will be constructed so the empirical re-weighted prevalence of disease is equal to prev.

verbose

A logical indicator for whether extended output is produced when ci=TRUE, default TRUE

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)
library(parallel)
options(boot.parallel="snow")
options(boot.ncpus=2)
# The above could be set to the number of available cores on the machine
data(stroke_reduced)
model_exercise <- glm(formula = case ~ region * ns(age, df = 5) +
 sex * ns(age, df = 5) + education + exercise + ns(diet, df = 3) +
 smoking + alcohol + stress, family = "binomial", data = stroke_reduced)
# calculate discrete PAF using Bruzzi method
PAF_calc_discrete(model_exercise, "exercise", refval=0,
data=stroke_reduced, calculation_method="B",ci=FALSE)
# \donttest{
# calculate discrete PAF using Direct method
# Use bootstrap resampling to calculate a confidence interval
# 10 Bootstrap reps used here for speed.
#  In real examples, use at least 50 repetitions.
PAF_calc_discrete(model_exercise, "exercise", refval=0,
data=stroke_reduced, calculation_method="D", prev=0.005, ci=TRUE, boot_rep=10)
### use the Bruzzi method derived by Bruzzi, 1985, instead
PAF_calc_discrete(model_exercise, "exercise", refval=0, data=stroke_reduced,
 calculation_method="B", ci=TRUE, boot_rep=10)
# examples of clogit and coxph regressions

model_high_blood_pressure_clogit <- 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)
PAF_calc_discrete(model_high_blood_pressure_clogit, "high_blood_pressure",
refval=0, data=stroke_reduced, calculation_method="B",ci=TRUE, boot_rep=10,
 ci_type=c('norm'))

model_high_blood_pressure_coxph <- coxph(formula = Surv(time,event) ~
 ns(age,df=5) + education +exercise + ns(diet, df = 3) + smoking + alcohol +
  stress + ns(lipids,df = 3) + ns(waist_hip_ratio, df = 3) +
  high_blood_pressure, data = stroke_reduced)
PAF_calc_discrete(model_high_blood_pressure_coxph, "high_blood_pressure",
refval=0, data=stroke_reduced, calculation_method="D", ci=TRUE,
boot_rep=10, ci_type=c('norm'),t_vector=c(1,2,3,4,5,6,7,8,9))
# }