Construct estimator for the treatment effect in RCT

Returns an estimator for the estimation of a treatment effect with parametric models (stats::glm and MASS::glm.nb). The returned estimator is a function with a single argument (data) and returns the treatment effect estimate, which is estimated with lava::estimate

Usage

est_glm(
  response = "y",
  treatment = "a",
  covariates = NULL,
  offset = NULL,
  id = NULL,
  level = 0.95,
  family = gaussian(),
  target.parameter = treatment,
  ...
)

Arguments

response: (character) Response variable
treatment: (character) Treatment variable. Additional care must be taken when the treatment variable is encoded as a factor (see examples).
covariates: (character; optional) Single or vector of covariates
offset: (character; optional) Model offset
id: (character; optional) Subject id variable
level: (numeric) Confidence interval level
family: (family or character) Exponential family that is supported by stats::glm and MASS::glm.nb
target.parameter: (character) Target parameter from model output
...: Additional arguments to lava::estimate

Value

function

Author

Klaus Kähler Holst

Examples

if (FALSE) { # \dontrun{
trial <- Trial$new(
    covariates = function(n) data.frame(a = rbinom(n, 1, 0.5), x = rnorm(n)),
    outcome = setargs(outcome_count,
      mean = ~ 1 + a*x,
      par = c(1, -0.1, 0.5, 0.2),
      overdispersion = 2)
)
dd <- trial$simulate(3e2)

# crude mean comparison between arms (default behavior; y ~ a)
est <- est_glm(family = poisson)
est(dd)

# linear adjustment with one covariate (y ~ a + x)
est <- est_glm(family = poisson, covariates = "x")
est(dd)

# return estimates of all linear coefficients (useful for debugging)
est <- est_glm(family = poisson, covariates = "x", target.parameter = NULL)
est(dd)

# comparing robust and non-robust standard errors of poisson estimator by
# passing robust argument via ... to lava::estimate
estimators <- list(
  robust = est_glm(family = poisson),
  non.robust = est_glm(family = poisson, robust = FALSE)
)
res <- do.call(rbind, lapply(estimators, \(est) est(dd)$coefmat))
rownames(res) <- names(estimators)
res

dd_factor <- dd
dd_factor$a <- as.factor(dd_factor$a)
# target parameter needs to be changed because the name of the estimated
# regression coefficient changes when encoding the treatment variable as a
# factor
est_glm(family = poisson, target.parameter = "a1")(dd_factor)
} # }