TESTING FOR BASELINE BALANCE IN CLINICAL TRIALS

analysis of covariance

baseline balance

bias

clinical trials

randomization

statistical methods

Core Principle: This paper argues that performing tests of baseline homogeneity (p-value tests) in randomized controlled trials (RCTs) is philosophically unsound, of no practical value, and potentially misleading, as randomization ensures comparability in expectation.
The Flaw: A non-significant baseline difference does not prove balance (it reflects low power), and a significant difference is merely a chance event that does not invalidate the randomization.
Recommendation: The authors recommend that researchers must abandon baseline testing and, instead, identify known prognostic variables in the trial-plan and fit them in an Analysis of Covariance (ANCOVA) model regardless of their baseline p-value, which increases the precision and power of the final treatment effect estimate.

Published

23 January 2026

PubMed: 7997705 DOI: 10.1002/sim.4780131610 Overview generated by: Gemini 2.5 Flash, 26/11/2025

Key Findings: The Misguided Practice of Testing Baseline Balance

This influential paper by Stephen Senn critiques the common, but statistically unsound, practice in randomized controlled trials (RCTs) of performing “tests of baseline homogeneity” (i.e., p-value tests comparing baseline characteristics between treatment arms) before proceeding to analyze the treatment effect on the outcome. Senn argues that this practice is both philosophically flawed and practically misleading.

The Problem with Testing for Balance

The core issue lies in the interpretation of randomization and the nature of the null hypothesis in a properly conducted RCT:

Philosophically Unsound: The goal of randomization is to ensure that, in expectation, the treatment groups are comparable. Any observed differences at baseline are due purely to chance, and no statistical test is required to confirm this. Performing a test is equivalent to testing the effectiveness of a coin toss—a pointless exercise.
No Practical Value: A statistically significant difference at baseline (e.g., \(p < 0.05\) for age difference) does not indicate a flaw in the randomization process; it is merely a low-probability chance event that is expected to occur in 5% of all covariates tested. Such a finding offers no useful guidance on how to analyze the treatment effect.
Potentially Misleading:
- Non-significant difference (\(p > 0.05\)): This does not prove the groups are “balanced” or comparable. It only means the study lacked the power to detect a difference, or that the observed difference was not large enough to cross the arbitrary significance threshold. The non-significant result might wrongly convince the investigator that no adjustment is needed, even if the difference is clinically important.
- Significant difference (\(p < 0.05\)): This may wrongly prompt an investigator to use an ad hoc adjustment (like Analysis of Covariance, ANCOVA) only for that specific variable, introducing subjectivity into the analysis plan and potentially invalidating the comparison of treatment effects.

The Recommended Practice: Analysis of Covariance (ANCOVA)

Senn strongly recommends replacing baseline balance testing with a principled approach to analysis:

Pre-specify Prognostic Variables: The study protocol should identify and list all known or suspected prognostic covariates (variables predictive of the outcome), regardless of their baseline distribution.
Routine ANCOVA: These prognostic variables should be included in the primary analysis model using Analysis of Covariance (ANCOVA), irrespective of their p-value from the baseline comparison.
Statistical Advantages: Adjusting for important prognostic variables using ANCOVA increases the precision and statistical power of the treatment effect estimate, leading to narrower confidence intervals. Crucially, the validity of ANCOVA in an RCT relies on the fact that randomization ensures the baseline covariates are unrelated to the treatment assignment, not on their baseline p-value.

Conclusion

The paper concludes that the practice of baseline testing is a confusion of philosophy and statistics. Researchers should focus on design (randomization) to ensure validity and statistical efficiency (ANCOVA) to maximize power, rather than using post-randomization tests that are incapable of fulfilling the purpose for which they are intended.