Regulatory Focus™ > News Articles > 2019 > 4 > FDA Drafts Guidance on Adjusting for Covariates in Randomized Trials

FDA Drafts Guidance on Adjusting for Covariates in Randomized Trials

Posted 24 April 2019 | By Zachary Brennan 

FDA Drafts Guidance on Adjusting for Covariates in Randomized Trials

The US Food and Drug Administration (FDA) on Wednesday published draft guidance to provide recommendations on the use of covariates in the primary analysis of randomized clinical trials.

Specifically, the three-page draft offers recommendations for adjusting for covariates in randomized trials “with continuous endpoints that are appropriate for analysis with normal-theory methods, such as the two-sample t-test.”

Building on ICH’s E9 guidance, which deals with the statistical principles for clinical trials, FDA offers a series of recommendations for sponsors and explains how the method of adjusting for covariates is usually referred to as analysis of covariance (ANCOVA).

The agency’s five recommendations include:
  1. “Sponsors can use ANCOVA to adjust for differences between treatment groups in relevant baseline variables to improve the power of significance tests and the precision of estimates of treatment effect.
  2. Sponsors should not use ANCOVA to adjust for variables that might be affected by treatment.” FDA also includes a paragraph on why.
  3. Sponsors “should prospectively specify the covariates and the mathematical form of the model in the protocol or statistical analysis plan. When these specifications are unambiguous, FDA will not generally be concerned about the sensitivity of results to the choice of covariates because differences between adjusted estimators and unadjusted estimators of the same parameter, or between adjusted estimators using different models, are random.
  4. Interaction of the treatment with covariates is important, but the presence of an interaction does not invalidate ANCOVA as a method of estimating and testing for an overall treatment effect, even if the interaction is not accounted for in the model. The prespecified primary model can include interaction terms if appropriate. However, interaction means that the treatment effect is different for different subjects, and this fact could be relevant to prescribers, patients, and other stakeholders. Therefore, even though a primary analysis showing an overall treatment effect remains valid, differential effects in subgroups can also be important.
  5. Many clinical trials use a change from baseline as the primary outcome measure. Even when the outcome is measured as a change from baseline, the baseline value can still be used advantageously as a covariate.”
FDA adds: “Nonparametric methods, categorical outcomes, and survival methods, among others, are outside the scope of this document, although some of the same principles might apply to those methods as well.”

Adjusting for Covariates in Randomized Clinical Trials for Drugs and Biologics with Continuous Outcomes: Draft Guidance for Industry

Categories: Regulatory News

Regulatory Focus newsletters

All the biggest regulatory news and happenings.

Subscribe