Package Introduction

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

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RobinCar2 provides robust covariate adjustment methods for estimating and inferring treatment effects through generalized linear models (glm) under different randomization schema.

Common Usage

A minimal call of robin_lm() and robin_glm(), consisting of only formula, data arguments and the randomization scheme, will produce an object of class treatment_effect.

library(RobinCar2)
head(dummy_data)

Data Introduction

In the dummy_data, we have the following columns:

1. id is the patient identifier.
2. treatment is the treatment assignment.
3. s1 is the first stratification factor.
4. s2 is the second stratification factor.
5. covar is the continuous covariate.
6. y is the continuous outcome.
7. y_b is the binary outcome.

Obtain Treatment Effect for Continuous Outcomes Using the General Variance

For the continuous outcome y, the linear model includes covar as a covariate, s1 as a stratification factor. The randomization scheme is a permuted-block randomization stratified by s1. The model formula also includes the treatment by stratification interaction as y ~ treatment * s1 + covar.

robin_lm(y ~ treatment * s1 + covar,
  data = dummy_data,
  treatment = treatment ~ pb(s1)
)

We can also use the Huber-White variance estimator by setting vcov = "vcovHC". Please note that in this case, the model formula should not contain the treatment by stratification (covariate) interaction.

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

Note that robin_glm can also handle continuous outcomes using the default family and the default link function family = gaussian().

Obtain Treatment Effect for Binary Outcomes

For binary outcomes, the logistic model includes covar as a covariate, s1 as a stratification factor. The randomization scheme is a permuted-block randomization stratified by s1. The model formula also includes the treatment by stratification interaction as y_b ~ treatment * s1 + covar. Note here we need to specify family to be binomial(link = "logit").

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

Obtain Treatment Effect for Counts

For counts, the log link model includes covar as a covariate, s1 as a stratification factor. The randomization scheme is a permuted-block randomization stratified by s1. The model formula also includes the treatment by stratification interaction as y_count ~ treatment * s1 + covar. Note here we need to specify family to be poisson(link = "log") to use the Poisson model or to be MASS::negative.binomial(theta = NA) to use the negative binomial model. A fixed theta could be provided if it is known.

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

Using Different Covariate-Adaptive Randomization Schema

If the randomization schema is not permuted-block randomization, we can use other randomization schema. Currently RobinCar2 supports sp for the simple randomization, pb for the permuted-block randomization, and ps for the Pocock-Simon randomization.

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

RobinCar2 documentation built on April 3, 2025, 9:34 p.m.