The FDA released its final guidance regarding the use of covariates to improve the precision of statistical analyses in clinical trials on 26 May 2023. It is a major step forward in the statistical analysis of clinical trial data. Recognizing that nearly a decade ago, the publication of the EMA guide initiated the movement, the FDA has crystallized the concept with the current guidance.
The overall risk of clinical trial failure is still too high – meaning more repeat trials, lost timelines and premature abandonment of programs. In part, this is because of the placebo response: the measured improvement of a patient in a trial after receiving a sham treatment. While a high placebo response doesn’t necessarily mean the
Clinical trials typically evaluate efficacy of experimental therapies in heterogeneous patient populations, as patient characteristics vary significantly. These patient characteristics might be prognostic factors that ultimately induce variability in clinical trial data. An imbalance in these factors between treatment groups at baseline will increase variability of the estimated treatment effect, ultimately compromising study power and decreasing
There is a continuous growth in data collected in clinical trials. Many of those patient’s characteristics are potential confounding factors. Ideally, these factors should be accounted for in the randomization process to balance study arms and reduce the variability of the estimated treatment effect.
Often, the primary endpoint of RCTs is defined as a change from baseline of a continuous outcome. In
such cases, regulators recommend including the outcome’s baseline value as a covariate in the
statistical analysis. Regression to the mean can explain the benefits of this procedure.
The amount of data collected from patients involved in clinical trials is continuously growing. All those patient’s characteristics are potential covariates that could be used to improve study analysis and power. At the same time, the development of computerized systems simplifies the access to huge amount of historical data.