propertee vs. geex

library(propertee)
library("geex")

Overview

We provide basic validation for the model-based sandwich standard errors provided in by comparing them to estimates from the package (Saul and Hudgens, 2020). In two examples, one with and one without clustering, we estimate the standard error of an intent-to-treat (ITT) effect estimate that has been adjusted for covariates using a prior covariance model. In the M-estimation framework, the prior covariance model defines one set of estimating equations, and the covariate-adjusted intent-to-treat effect estimate defines another. One can stack them togther and use the package to obtain joint estimates of the model coefficients and ITT effect, or one can fit the covariance model first, then use the package to feed necessary information about the first-stage estimating equations to variance estimation for the ITT effect estimate. We demonstrate the equivalence of these two appraoches below.

Example with non-clustered data

set.seed(980)
n <- 60
cmod_df <- data.frame(
  "uid" = seq_len(n),
  "z" = c(rep(0, 3 * n/4), rep(1, n/4)),
  "x1" = rnorm(n, sd = 2),
  "x2" = rep(c(rep(0, n/4), rep(1, n/4)), 2)
)
eps <- rnorm(nrow(cmod_df))
theta <- c(75, 0.1, -0.5, 2.5)
cmod_df$y <- model.matrix(~ x1 + x2 + z, cmod_df) %*% theta + eps

cmod_df$in_q <- c(rep(0, n/2), rep(1, n/2))
q_df <- cmod_df[cmod_df$in_q == 1,]

cmod <- lm(y ~ x1 + x2, cmod_df)
des <- rct_design(z ~ unitid(uid), q_df)
damod <- lmitt(y ~ 1, design = des, data = q_df, weights = ate(des), offset = cov_adj(cmod))

estFun <- function(data){
  function(theta) {
    # covariance model eqns
    Xstar <- model.matrix(y ~ x1 + x2, data)
    cmod_eqns <- drop(data$y - Xstar %*% theta[1:3]) * Xstar

    # itt model eqns
    X <- model.matrix(y ~ x1 + x2, data)
    Z <- model.matrix(y ~ z, data)
    if (data$in_q == 1) {
      damod_eqns <- drop(data$weight * (data$y - X %*% theta[1:3] - Z %*% theta[4:5])) * Z
    } else {
      damod_eqns <- rep(0, 2)
    }

    out <- c(cmod_eqns, damod_eqns)
    return(out)
  }
}

geexRes <- geex::m_estimate(estFun,
                            data = cbind(cmod_df,
                                         "weight" = c(rep(1, n/2), ate(des, data = q_df))),
                            root_control = geex::setup_root_control(start = rep(0.1,5)))
print(paste("geex estimate of var(tau_hat):", round(geexRes@vcov[5,5], 8)))
print(paste("propertee estimate of var(tau_hat):",
            round(vcov_tee(damod, type = "CR0", cadjust = FALSE)[2,2], 8)))

Example with clustered data

set.seed(50)
nc <- 4
mi <- 20
cmod_df <- data.frame(
  "cid" = rep(seq_len(4), each = mi),
  "uid" = rep(seq_len(mi), nc),
  "z" = c(rep(0, 3 * mi), rep(1, mi)),
  "x1" = rbinom(nc * mi, 1, 0.5),
  "x2" = rnorm(nc * mi)
)

theta <- c(75, 7.5, -1, 2.5)
error_sd <- round(runif(nc, 1, 3), 1)
icc <- 0.2
eps <- rnorm(nrow(cmod_df))
Sigma <- matrix(0, nrow = nrow(cmod_df), ncol = nrow(cmod_df))
for (i in (seq_len(nc) - 1)) {
  msk <- cmod_df$cid == (i + 1)
  Sigma[msk, msk] <- diag(error_sd[i + 1] - icc, nrow = sum(msk)) + icc
}
A <- chol(Sigma)
eps <- t(A) %*% eps
cmod_df$y <- model.matrix(~ x1 + x2 + z, cmod_df) %*% theta + eps

cmod_df$in_q <- c(rep(0, mi), rep(1, 3 * mi))
q_df <- cmod_df[cmod_df$in_q == 1,]

cmod <- lm(y ~ x1 + x2, cmod_df)
des <- rct_design(z ~ cluster(cid), q_df)
damod <- lmitt(y ~ 1, design = des, data = q_df, weights = ate(des), offset = cov_adj(cmod))

clusterEstFunc <- function(data){
  function(theta) {
    # covariance model eqns
    Xstar <- model.matrix(y ~ x1 + x2, data)
    cmod_agg_func <- ifelse(dim(Xstar)[2] > 1, colSums, sum)
    cmod_eqns <- cmod_agg_func(drop(data$y - Xstar %*% theta[1:3]) * Xstar)

    # itt model eqns
    X <- model.matrix(y ~ x1 + x2, data)
    Z <- model.matrix(y ~ z, data)
    damod_agg_func <- ifelse(dim(Z)[2] > 1, colSums, sum)
    if (unique(data$in_q) == 1) {
      damod_eqns <- damod_agg_func(
        drop(data$weight * (data$y - X %*% theta[1:3] - Z %*% theta[4:5])) * Z)
    } else {
      damod_eqns <- rep(0, 2)
    }

    out <- c(cmod_eqns, damod_eqns)
    return(out)
  }
}

geexRes <- geex::m_estimate(clusterEstFunc,
                            data = cbind(cmod_df,
                                         "weight" = c(rep(1, mi), ate(des, data = q_df))),
                            units = "cid",
                            root_control = geex::setup_root_control(start = rep(0.1,5)))

print(paste("geex estimate of var(tau_hat):", round(geexRes@vcov[5,5], 8)))
print(paste("propertee estimate of var(tau_hat):",
            round(vcov_tee(damod, type = "CR0", cadjust = FALSE)[2,2], 8)))

References

Saul BC, Hudgens MG (2020). “The Calculus of M-Estimation in R with geex.” Journal of Statistical Software, 92(2), 1–15. doi: 10.18637/jss.v092.i02.

Joshua Wasserman

2024-09-20

Overview

Example with non-clustered data

Example with clustered data

References