Regulatory Guidelines for the Use of Baseline Covariates to Increase Clinical Trial Study Power

Clinical trials data analyses can employ baseline covariates to control for factors that may impact measurement of outcomes – particularly to describe individual patient characteristics that may or may not relate to treatment response. For example, patient age may be used as a baseline covariate to reduce data variability resulting from this factor. The use of baseline covariates is very prevalent in clinical trial data analysis. In fact, in a survey of randomized controlled clinical trials published in 4 journals from 2009 – 2010, 84% were reported to utilize covariates1. Of the trials that reported covariate-adjusted analyses, 91% were pre-specified before the analysis was conducted1.

Because they are so widely used, the practical utility of baseline covariates has been in regulatory guidances issued by the FDA and EMA. The “Guideline on Adjustment for baseline covariates in clinical trials” published by the EMA became effective as of 01 September 20152. The FDA issued “Adjusting for Covariates in Randomized Clinical Trials for Drugs and Biologics with Continuous Outcomes: Guidance for Industry3 was put forth in draft form in April 2019.  

There are several themes that are common to both documents:

  • ANCOVA can be used to adjust for differences between treatment groups in relevant baseline variables to improve power and improve estimates of treatment effect.
  • Variables measured after randomization and thus affected by treatment should not be used as covariates.
  • Covariates selected should be prospectively specified in the protocol or statistical analysis plan.
  • The baseline value of an outcome can still be used as a covariate when the outcomes is measured as change from baseline.

Points specific to the EMA document:

  • Stratification can be used to ensure balance of treatment across covariates; in this case, these factors should also be used as covariates
  • Only a few covariates should be used
  • Exploratory analyses may be carried out to improve understanding of covariates not included in primary analysis and to help with future drug development

The major difference between the EMA and FDA documents deals with interactions between the covariate and treatment.

  • The FDA draft guidance asserts that interactions may be important, and that the presence of an interaction does not invalidate the analysis. This document suggests that the sponsor should perform sub-group analyses to understand differential treatment effects that could be meaningful to prescribers, patients and other stakeholders.
  • The EMA Guidance states that the primary model should not include interactions; if interactions are expected a priori, the trial should be designed to allow separate estimates of treatment effect in different subgroups.

Covariates that are typically measured include demographic variables that are easily collected (e.g. age and gender), and baseline values of primary outcomes. Now, the Placebell©™ approach provides the opportunity to consider each patient’s placebo responsiveness as a baseline covariate. This tool integrates patient disease history, common demographics and psychological traits obtained through Tools4Patient’s Multi-Dimensional Psychological Questionnaire (MPsQ) to calculate a unique covariate score for each patient as a conservative, low risk method to account for varying placebo responsiveness in a clinical trial population. This approach is consistent with the stipulations outlined in the EMA and FDA guidances in that all components are defined at baseline and can be pre-specified in the statistical analysis plan. This tool provides the overall benefit of increasing study power, reducing bias and improving data efficiency. To request our scientific white paper “Predicting the Placebo Response to Reduce Clinical Data Variability and De-Risk Drug Development”, please click here.

References

1.        Ciolino JD, Palac HL, Yang A, Vaca M, Belli HM. Ideal vs. real: A systematic review on handling covariates in randomized controlled trials. BMC Med Res Methodol. 2019;19(1):1-11. doi:10.1186/s12874-019-0787-8

2.        CHMP. Committee for Medicinal Products for Human Use (CHMP) Guideline on Adjustment for Baseline Covariates in Clinical Trials.; 2015. www.ema.europa.eu/contact. Accessed March 9, 2020.

3.        Fda, Cder, Goldie, Scott. Adjusting for Covariates in Randomized Clinical Trials for Drugs and Biologics with Continuous Outcomes Guidance for Industry. https://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/default.htm. Accessed March 9, 2020.