SC4: Improving Precision and Power in Randomized Trials by Leveraging Baseline Variables

• Michael Rosenblum, Johns Hopkins University - GitHub
• Kelly Van Lancker, Ghent University - GitHub
• Joshua Betz, Johns Hopkins University - GitHub

In May 2023, the U.S. Food and Drug Administration (FDA) released guidance for industry on “Adjustment for Covariates in Randomized Clinical Trials for Drugs and Biological Products.” Covariate adjustment is a statistical analysis method for improving precision and power in clinical trials by adjusting for pre-specified, prognostic baseline variables. Here, the term “covariates” refers to baseline variables, that is, variables that are measured before randomization such as age, gender, BMI, comorbidities. The resulting sample size reductions can lead to substantial cost savings and more ethical trials since they avoid exposing more participants than necessary to experimental treatments. Though covariate adjustment is recommended by the FDA and the European Medicines Agency (EMA), many trials do not exploit the available information in baseline variables or only make use of the baseline measurement of the outcome.

In Part 1, we introduce the concept of covariate adjustment. We explain what covariate adjustment is, how it works, when it may be useful to apply, and how to implement it in a preplanned way that is robust to model misspecification.

In Part 2, we present statistical methods that enable investigators to easily combine covariate adjustment with trial designs that allow for interim stopping for efficacy and futility, including information monitoring and group sequential designs. The result will be faster, more efficient trials for many disease areas, without sacrificing validity or power. This approach can lead to faster trials even when the experimental treatment is ineffective; this may be more ethical in settings where it is desirable to stop as early as possible to avoid unnecessary exposure to side effects.

In Part 3, we demonstrate how to implement covariate adjustment across the life cycle of a study using data and code, including planning new studies, monitoring ongoing studies, and performing pre-specified analyses.

Statistical/Programming Knowledge Required: Participants should have a basic understanding of randomized trials, regression models, and survival analysis. Familiarity with R is helpful but not required.

Code Examples

Example R code and data are included in this repository: R can be installed from the Comprehensive R Archival Network (CRAN). The R Studio development environment will make it easier to take full advantage of R. Make sure Rtools is installed: this will facilitate compiling R packages from source. After these steps have been installed, additional packages will need to be installed from CRAN and GitHub.

Here is a direct link to download the repo as a .zip file

# NOTE: INSTALL Rtools First: https://cran.r-project.org/bin/windows/Rtools/

installed_packages <- installed.packages()[, "Package"]

cran_packages <-
  c("cobalt", "devtools", "digest", "dplyr", "ggplot2", "ggsci", "here",
    "kableExtra", "labelled", "rpact", "stringr", "table1", "tidyr", "xfun")

github_packages <-
  c("jbetz-jhu/impart")

packages_to_install <-
  setdiff(
    x = cran_packages,
    y = installed_packages
  )

if(length(packages_to_install)){
  install.packages(packages_to_install)
}

github_packages_to_install <-
  setdiff(
    x = 
      gsub(
        pattern = "^[A-Za-z0-9_\\.\\-]*/",
        replacement = "",
        x = github_packages
      ),
    y = installed_packages
  )

if(length(github_packages_to_install) > 0){
  for(i in github_packages_to_install){
    devtools::install_github(repo = i, force = TRUE)
  }
}