simulations/Table2&3_SimSetup.R

In Epic-Doughnut/Combined-Reg: Semiparametric Likelihood Estimation with Errors in Variables

###########################################################
# Simulation setup for Tables 2/3 -------------------------
# Errors in binary outcome, -------------------------------
# Continuous covariate (additive)--------------------------
# Varied covariate error variance -------------------------
###########################################################

set.seed(918)

# Set sample sizes ----------------------------------------
N <- 1000 # Phase-I = N
n <- 250 # Phase-II/audit size = n

# Generate true values Y, Xb, Xa ----------------------------
Xa <- rbinom(n = N, size = 1, prob = 0.25)
Xb <- rnorm(n = N, mean = 0, sd = 1)
Y <- rbinom(n = N, size = 1, prob = (1 + exp(-(- 1 + Xb - 0.5 * Xa))) ^ (- 1))

# Generate error-prone Xb* = Xb + U -------------------------
## For U ~ N(mean, var) -----------------------------------
muU <- 0 ## mean muU = 0 for unbiased, != 0 for biased ----
s2U <- 0.1 ## variance ------------------------------------
U <- rnorm(n = N, mean = muU, sd = sqrt(s2U))
Xbstar <- Xb + U

# Generate error-prone Y* from error model P(Y*|Xb*,Y,Xb,Xa) -
sensY <- 0.95; specY <- 0.90
theta0 <- - log(specY / (1 - specY))
theta1 <- - theta0 - log((1 - sensY) / sensY)
Ystar <- rbinom(n = N, size = 1,
  prob = (1 + exp(- (theta0 + Xbstar + theta1 * Y + Xb - 0.5 * Xa))) ^ (- 1))

# Choose audit design: SRS or -----------------------------
## Unvalidated case-control: case-control based on Y^* ----
## Table 2 was SRS, Table 3 was Unvalidated case-control --
audit <- "SRS" #or "Unvalidated case-control"

# Draw audit of size n based on design --------------------
## V is a TRUE/FALSE vector where TRUE = validated --------
if(audit == "SRS")
{
  V <- seq(1, N) %in% sample(x = seq(1, N), size = n, replace = FALSE)
}
if(audit == "Unvalidated case-control")
{
  V <- seq(1, N) %in% c(sample(x = which(Ystar == 0), size = 0.5 * n, replace = FALSE),
    sample(x = which(Ystar == 1), size = 0.5 * n, replace = FALSE))
}

# Build dataset --------------------------------------------
sdat <- data.frame(cbind(Y, Xb, Ystar, Xbstar, Xa, V))
# Make Phase-II variables Y, Xb NA for unaudited subjects ---
sdat[!V, c("Y", "Xb")] <- NA

# Fit models -----------------------------------------------
## (1) Naive model -----------------------------------------
naive <- glm(Ystar ~ Xbstar + Xa, family = "binomial", data = sdat)

## (2) Complete data model (for SRS) -----------------------
cd <- glm(Y ~ Xb + Xa, family = "binomial", data = sdat[V, ])

## (3) Horvitz Thompson (for case-control)------------------
## Note: if audit = "SRS", then CC = HT --------------------
if (audit == "Unvalidated case-control") {
  library(sandwich)
  sample_wts <- ifelse(Ystar[V] == 0, 1 / ((0.5 * n) / (table(Ystar)[1])), 1 / ((0.5 * n) / (table(Ystar)[2])))
  ht <- glm(Y[V] ~ Xb[V] + Xa[V], family = "binomial",
    weights = sample_wts)
  beta_ht <- ht$coefficients[2]
  se_ht <- sqrt(diag(sandwich(ht)))[2]
}

## (4) Generalized raking ----------------------------------
### Influence function for logistic regression
### Taken from: https://github.com/T0ngChen/multiwave/blob/master/sim.r
inf.fun <- function(fit) {
  dm <- model.matrix(fit)
  Ihat <- (t(dm) %*% (dm * fit$fitted.values * (1 - fit$fitted.values))) / nrow(dm)
  ## influence function
  infl <- (dm * resid(fit, type = "response")) %*% solve(Ihat)
  infl
}

naive_infl <- inf.fun(naive) # error-prone influence functions based on naive model
colnames(naive_infl) <- paste0("if", 1:3)

# Add naive influence functions to sdat -----------------------------------------------
sdat <- cbind(id = 1:N, sdat, naive_infl)
library(survey)
if (audit == "SRS") {
  sstudy <- twophase(id = list(~id, ~id),
   data = data.frame(sdat),
   subset = ~V)
  } else if (audit == "Unvalidated case-control") {
    sstudy <- twophase(id = list(~id, ~id),
     data = data.frame(sdat),
     strat = list(NULL, ~Ystar),
     subset = ~V)
  }

# Calibrate raking weights to the sum of the naive influence functions ----------------
scal <- calibrate(sstudy, ~ if1 + if2 + if3, phase = 2, calfun = "raking")
# Fit analysis model using calibrated weights -----------------------------------------
rake <- svyglm(Y ~ Xb + Xa, family = "binomial", design = scal)
beta_rake <- rake$coefficients[2]
se_rake <- sqrt(diag(vcov(rake)))[2]

## (5) SMLE ------------------------------------------------
### Construct B-spline basis -------------------------------
### We chose cubic B-splines, ------------------------------
### with 20 df for N = 1000 and 24 df for N = 2000 ---------
nsieve <- 20
B <- matrix(0, nrow = N, ncol = nsieve)
B[which(Xa == 0),1:(0.75 * nsieve)] <- splines::bs(x = Xbstar[which(Xa == 0)], df = 0.75 * nsieve, Boundary.knots = range(Xbstar[which(Xa == 0)]), intercept = TRUE)
B[which(Xa == 1),(0.75 * nsieve + 1):nsieve] <- splines::bs(x = Xbstar[which(Xa == 1)], df = 0.25 * nsieve, Boundary.knots = range(Xbstar[which(Xa == 1)]), intercept = TRUE)
colnames(B) <- paste0("bs", seq(1, nsieve))
sdat <- cbind(sdat, B)

smle <- logreg2ph(Y_unval = "Ystar",
 Y = "Y",
 X_unval = "Xbstar",
 X = "Xb",
 Z = "Xa",
 Bspline = colnames(B), 
 data = sdat,
 noSE = FALSE,
 MAX_ITER = 1000,
 TOL = 1E-4)
beta_smle <- smle$Coefficients$Coefficient[2]
se_smle <- smle$Coefficients$SE[2]