estimate_varcov {beeca}

Estimate variance-covariance matrix for marginal estimand based on GLM model

Description

Main variance estimation function. Estimates the variance-covariance matrix of a marginal estimand for a generalized linear model (GLM) object using specified methods. This function supports both Ge's and Ye's methods for variance estimation, accommodating different estimand specifications.

Usage

estimate_varcov(
  object,
  strata = NULL,
  method = c("Ge", "Ye"),
  type = c("HC0", "model-based", "HC3", "HC", "HC1", "HC2", "HC4", "HC4m", "HC5"),
  mod = FALSE
)

Arguments

object

a fitted glm object augmented with counterfactual.predictions, counterfactual.predictions and counterfactual.means

strata

an optional string or vector of strings specifying the names of stratification variables. Relevant only for Ye's method and used to adjust the variance-covariance estimation for stratification. If provided, each specified variable must be present in the model.

method

a string indicating the chosen method for variance estimation. Supported methods are Ge and Ye. The default method is Ge based on Ge et al (2011) which is suitable for the variance estimation of conditional average treatment effect. The method Ye is based on Ye et al (2023) and is suitable for the variance estimation of population average treatment effect. For more details, see Magirr et al. (2024).

type

a string indicating the type of variance estimator to use (only applicable for Ge's method). Supported types include HC0 (default), model-based, HC3, HC, HC1, HC2, HC4, HC4m, and HC5. See vcovHC for heteroscedasticity-consistent estimators. This parameter allows for flexibility in handling heteroscedasticity and model specification errors.

mod

For Ye's method, the implementation of open-source RobinCar package has an additional variance decomposition step when estimating the robust variance, which then utilizes different counterfactual outcomes than the original reference. Set mod = TRUE to use exactly the implementation method described in Ye et al (2022), default to FALSE to use the modified implementation in RobinCar and Bannick et al (2023) which improves stability.

Details

The estimate_varcov function facilitates robust variance estimation techniques for GLM models, particularly useful in clinical trial analysis and other fields requiring robust statistical inference. It allows researchers to account for complex study designs, including stratification and different treatment contrasts, by providing a flexible interface for variance-covariance estimation.

Note: Ensure that the glm object has been adequately prepared with predict_counterfactuals and average_predictions before applying estimate_varcov(). Failure to do so may result in errors indicating missing components.

Value

an updated glm object appended with an additional component robust_varcov, which is the estimated variance-covariance matrix of the marginal effect. The matrix format and estimation method are indicated in the matrix attributes.

References

Ye T. et al. (2023) Robust variance estimation for covariate-adjusted unconditional treatment effect in randomized clinical trials with binary outcomes. Statistical Theory and Related Fields

Ge M. et al. (2011) Covariate-Adjusted Difference in Proportions from Clinical Trials Using Logistic Regression and Weighted Risk Differences. Drug Information Journal.

Bannick, M. S., et al. A General Form of Covariate Adjustment in Randomized Clinical Trials. arXiv preprint arXiv:2306.10213 (2023).

See Also

average_predictions() for averaging counterfactual predictions.

apply_contrast() for computing a summary measure.

get_marginal_effect() for estimating marginal effects directly from an original glm object

Examples

# Example usage with a binary outcome GLM model
trial01$trtp <- factor(trial01$trtp)
fit1 <- glm(aval ~ trtp + bl_cov, family = "binomial", data = trial01)

#' # Preprocess fit1 as required by estimate_varcov
fit2 <- fit1 |>
  predict_counterfactuals(trt = "trtp") |>
  average_predictions()

# Estimate variance-covariance using Ge's method
fit3_ge <- estimate_varcov(fit2, method = "Ge")
print(fit3_ge$robust_varcov)


# Estimate variance-covariance using Ye's method with stratification
fit4 <- glm(aval ~ trtp + bl_cov_c, family = "binomial", data = trial01) |>
  predict_counterfactuals(trt = "trtp") |>
  average_predictions()
fit4_ye <- estimate_varcov(fit4, method = "Ye", strata = "bl_cov_c")
print(fit4_ye$robust_varcov)