Stratified randomization is a common practice in randomized, controlled clinical trials. It ensures that key characteristics are evenly distributed across treatment groups and the treatment assignments are balanced within each randomization stratum, enhancing the validity of study results. However, during the course of a trial, mis-stratification can occur—this happens when an incorrect stratification stratum is used during randomization. Let's explore what this means, why it happens, and how it impacts clinical trials.
What is Mis-Stratification?
In clinical trials, stratification factors (e.g., age, disease severity, disease subgroup, or background medication use) are used to group participants before randomization. Stratified randomization is used to ensure that equal numbers of subjects with one or more characteristic(s) thought to affect the treatment outcome in efficacy measure will be allocated to each comparison group. Mis-stratification - a type of randomization errors, occurs when:
- An incorrect stratification factor is used for randomization, or
- A participant is placed in the wrong stratum due to an error.
Despite this, the treatment assignment and drug dispensation remain accurate, making it a minor deviation rather than a critical error. When mis-stratification occurs, the random code and the treatment assignment is pulled from an incorrect stratum.
Historical Approach: Intention-to-Treat Principle
Traditionally, clinical trials have adhered to the Intention-to-Treat (ITT) principle, where participants are analyzed according to the group they were originally randomized to, regardless of any errors. This approach maintains the integrity of the randomization process.
In practice, this means using the original stratification data—even if incorrect—in the statistical analysis. A typical Statistical Analysis Plan (SAP) might state:
“All original stratification information used in the randomization procedure will be used for analyses, regardless of whether it was later found to be incorrect. All efficacy analyses will be performed primarily on the ITT Population.”
This approach minimizes bias and reflects the 'real-world' impact of treatment. However, in the mis-stratification situation, using the incorrect stratum information in analyses may be too harsh and too strict unnecessarily.
Why Does Mis-Stratification Occur?
Mis-stratification can result from several factors, including:
- Too Many Stratification Factors: More factors increase the complexity and likelihood of error.
- Local vs. Central Lab Results: Differences between local and central lab measurements can lead to misclassification.
- Timing of Measurement: Stratification factors measured at different times (baseline vs. screening) may not align.
- Medication Use: Stratifying by prior or concomitant medication use can be complicated by variations in patient reporting or prescription practices.
These issues highlight potential flaws in protocol design and study quality, emphasizing the need for clear definitions and consistent procedures.
Regulatory Perspective: FDA Guidance
The FDA's guidance document, “Adjusting for Covariates in Randomized Clinical Trials for Drugs and Biological Products,” provides clarity on handling mis-stratification:
“Randomization is often stratified by baseline covariates. A covariate adjustment model should generally include strata variables and can also include covariates not used for stratifying randomization. In some cases, incorrect stratification may occur and result in actual and as-randomized baseline strata variables. A covariate adjustment model can use either strata variable definition as long as this is prespecified. “
This guidance supports the use of either the originally assigned stratification or the actual baseline data in the analysis, provided it is specified before data unblinding. This flexibility helps maintain the study's validity while addressing stratification errors transparently.
Impact on Study Results
Mis-stratification is generally considered a minor deviation because its impact on efficacy and safety analyses is minimal. It does not affect treatment assignment or drug dispensation but only the stratum from which the assignment was drawn.
When incorrect stratification occurs, the actual stratification information is collected in the Electronic Data Capture (EDC) system and can be used in sensitivity analyses to evaluate the robustness of the study results.
There is an article "Handling misclassified stratification variables in the analysis of randomised trials with continuous outcomes" where the authors did the simulation study to investigate the impact of the mis-stratification on the statistical analyses.
Minimizing Mis-Stratification in Randomization
Too many mis-stratification errors indicate the poor quality of the clinical trial. To reduce the risk of mis-stratification, consider the following best practices:
- Limit Stratification Factors: Use the minimum necessary factors to reduce complexity.
- Consistent Measurement Timing: Align the timing of stratification factor measurements (e.g., always at baseline).
- Clear Definitions: Ensure stratification criteria are clearly defined, identified or measured, and uniformly applied.
- Training and Quality Checks: Provide thorough training for study personnel and implement rigorous quality checks.
Conclusion
While mis-stratification is not ideal, its impact on clinical trial results is usually minimal. By adhering to the Intention-to-Treat principle and following regulatory guidance, researchers can maintain the integrity of their analyses. As clinical trial designs become more complex, understanding and managing mis-stratification will continue to be crucial for maintaining study quality and reliability.
