inst/doc/multibias.R

In multibias: Simultaneous Multi-Bias Adjustment

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

## -----------------------------------------------------------------------------
df_observed <- data_observed(
  df_uc_sel,
  bias = c("uc", "sel"),
  exposure = "X",
  outcome = "Y",
  confounders = c("C1", "C2", "C3")
)

## -----------------------------------------------------------------------------
bp <- bias_params(
  coef_list = list(
    u = c(-0.19, 0.61, 0.72, -0.09, 0.10, -0.15),
    s = c(-0.01, 0.92, 0.94)
  )
)

## -----------------------------------------------------------------------------
df_validation <- data_validation(
  df_uc_sel_source,
  true_exposure = "X",
  true_outcome = "Y",
  confounders = c("C1", "C2", "C3", "U"),
  selection = "S"
)

## -----------------------------------------------------------------------------
multibias_adjust(
  data_observed = df_observed,
  bias_params = bp
)

## -----------------------------------------------------------------------------
multibias_adjust(
  data_observed = df_observed,
  data_validation = df_validation
)

## -----------------------------------------------------------------------------
n <- nrow(df_uc_sel)
est <- vector()
nreps <- 100

for (i in 1:nreps) {
  df_bootstrap <- df_uc_sel[sample(seq_len(n), n, replace = TRUE), ]
  df_observed <- data_observed(
    df_bootstrap,
    bias = c("uc", "sel"),
    exposure = "X",
    outcome = "Y",
    confounders = c("C1", "C2", "C3")
  )
  results <- multibias_adjust(
    df_observed,
    df_validation
  )
  est[i] <- results$estimate
}

# odds ratio estimate
round(median(est), 2)

# confidence interval
round(quantile(est, c(.025, .975)), 2)