inst/doc/intro.R

In RobinCar2: ROBust INference for Covariate Adjustment in Randomized Clinical Trials

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

## -----------------------------------------------------------------------------
library(RobinCar2)
head(glm_data)

## -----------------------------------------------------------------------------
robin_lm(y ~ treatment * s1 + covar,
  data = glm_data,
  treatment = treatment ~ pb(s1)
)

## -----------------------------------------------------------------------------
robin_lm(y ~ treatment + s1 + covar,
  data = glm_data,
  treatment = treatment ~ pb(s1),
  vcov = "vcovHC"
)

## -----------------------------------------------------------------------------
robin_glm(y_b ~ treatment * s1 + covar,
  data = glm_data,
  treatment = treatment ~ pb(s1),
  family = binomial(link = "logit")
)

## -----------------------------------------------------------------------------
glm_data$y_count <- rpois(nrow(glm_data), lambda = 20)
robin_glm(
  y_count ~ treatment * s1 + covar,
  data = glm_data,
  treatment = treatment ~ pb(s1),
  family = MASS::negative.binomial(theta = 1)
)

## -----------------------------------------------------------------------------
robin_glm(y_b ~ treatment * s1 + covar,
  data = glm_data,
  treatment = treatment ~ ps(s1),
  family = binomial(link = "logit")
)

## -----------------------------------------------------------------------------
robin_res <- robin_glm(y_b ~ treatment * s1 + covar,
  data = glm_data,
  treatment = treatment ~ ps(s1),
  family = binomial(link = "logit"),
  contrast = "log_risk_ratio"
)
robin_res

## -----------------------------------------------------------------------------
confint(robin_res$marginal_mean, parm = 1:2, level = 0.7)
confint(robin_res$marginal_mean, level = 0.7)

## -----------------------------------------------------------------------------
confint(robin_res$contrast)
confint(robin_res$contrast, transform = identity)