In louisahsmith/simpleSens: What the Package Does (One Line, Title Case)

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

library(simpleSens)

# confounding

calculate_bound(RRAU = 2, RRUY = 2, biases = confounding())

param_vals <- c(1.3, 1.5, 1.8, 2, 2.5, 3, 3.5, 4, 5, 6, 8, 10)

params <- expand.grid(
  RRAU = param_vals,
  RRUY = param_vals
)

table_1_vals <- mapply(calculate_bound,
  RRAU = params$RRAU, RRUY = params$RRUY,
  MoreArgs = list(biases = confounding())
)

table_1 <- matrix(table_1_vals,
  ncol = length(param_vals),
  dimnames = list(param_vals, param_vals)
)
round(table_1, 2)

# reduce to number other than 1
calculate_evalue(RRobs = 2.5, biases = confounding())
# figure out how to make plot


######### selection

get_param_info(biases = selection("general"))

calculate_bound(
  RRSUA0 = 1.5, RRSUA1 = 1.7, RRUYA0 = 2, RRUYA1 = 2,
  biases = selection("general")
)

calculate_evalue(RRobs = 73.1, biases = selection("general"))

calculate_evalue(RRobs = 5.2, biases = selection("S = U", "increased risk"))

calculate_evalue(RRobs = 1.5, biases = selection("selected"))

# misclassification

calculate_evalue(RRobs = 1.5, biases = misclassification("outcome"))

calculate_evalue(RRobs = 1.5,
  biases = misclassification("exposure",
    exposure_rare = TRUE, outcome_rare = TRUE))

# from paper
example1 <- list(
  selection("decreased risk"), 
  misclassification("outcome")
  )
example2 <- list(
  confounding(), 
  misclassification('exposure', outcome_rare = TRUE, exposure_rare = TRUE)
  )

calculate_bound(RRAYy = 1.125, RRUYA0 = 2, RRSUA0 = 1.5, biases = example1)

calculate_bound(RRYAa = 1.59, RRUY = 1/0.82, RRAU = 2, biases = example2)

#calculate_evalue(ORobs = 1.30, outcome_rare = TRUE, biases = example1)
calculate_evalue(RRobs = 1.30, biases = example1)

# calculate_evalue(ORobs = 0.51, outcome_rare = TRUE, biases = example2)
calculate_evalue(RRobs = 1/0.51, biases = example2)