## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----setup--------------------------------------------------------------------
library(deltapif)

## -----------------------------------------------------------------------------
library(deltapif)

paf(p = 0.085, beta = log(1.59), quiet = TRUE)

## -----------------------------------------------------------------------------
var_log_rr <- ((log(2.20) - log(1.15)) / (2 * 1.96))^2
var_log_rr

## -----------------------------------------------------------------------------
paf_dementia <- paf(
  p         = 0.085, 
  beta      = log(1.59), 
  var_beta  = var_log_rr, 
  var_p     = 0
)
paf_dementia

## -----------------------------------------------------------------------------
lee_pif <- pif(
  p        = 0.085, 
  p_cft    = 0.085 * (1 - 0.15), # 15% reduction
  beta     = log(1.59), 
  var_beta = var_log_rr, 
  var_p    = 0
)
lee_pif

## -----------------------------------------------------------------------------
averted_cases(426.5, lee_pif, variance = 2647.005)

## -----------------------------------------------------------------------------
attributable_cases(426.5, paf_dementia, variance = 2647.005)

## -----------------------------------------------------------------------------
paf_men   <- paf(p = 0.41, beta = 0.31, var_p = 0.001,
                 var_beta = 0.14,
                 label = "Men")
paf_women <- paf(p = 0.37, beta = 0.35, var_p = 0.001, 
                 var_beta = 0.16,
                 label = "Women")

## -----------------------------------------------------------------------------
paf_total(paf_men, paf_women, weights = c(0.49, 0.51))

## -----------------------------------------------------------------------------
paf_lead  <- paf(p = 0.41, beta = 0.31, var_p = 0.001,
                 var_beta = 0.014,
                 label = "Lead")
paf_absts <- paf(p = 0.61, beta = 0.15, var_p = 0.001, 
                 var_beta = 0.001,
                 label = "Asbestus")

## -----------------------------------------------------------------------------
paf_ensemble(paf_lead, paf_absts, weights = c(0.1, 0.2))

## -----------------------------------------------------------------------------
weighted_adjusted_paf(paf_lead, paf_absts, weights = c(0.2, 0.3))