Handling Randomization Errors in Clinical Trials with Stratified Randomization

Stratified randomization is very common in randomized, controlled clinical trials. The usage of stratified randomization has been discussed in previous posts. 

• Stratified randomization to achieve the balance of treatment assignment within each strata
• Restricted randomization, stratified randomization, and forced randomization
• Minimization Algorithm to Achieve Treatment Balance across Strata in Stratified Randomization

While stratified randomization has its benefits, it does not mean that more stratification factors are better. The more stratification factors we have, the more easily the randomization error of using a wrong stratum can occur. 

It becomes common to utilize the interactive response technology (IRT) system such as interactive response system (IVR) or interactive web response (IWR) systems for implementing the randomization and treatment assignments. The IRT system usually has to go through extensive quality control (QC) and user acceptance test (UAT) before implementation, therefore the randomization errors can be minimized. Compared to the manual randomization process, the randomization error rate is lower in studies with an IRT system for implementing the randomization. 

However, the use of the IRT system requires the investigational site staff (pharmacist, investigator, or study coordinator) to enter the stratification information at the time of randomization. The site staff can enter the incorrect stratification information into the IRT system, and the treatment assignment will then be pulled from the wrong stratum. The randomization error due to choosing a wrong stratum is probably the most common randomization error we see in clinical trials with stratified randomization. The more stratification factors we have, the more likely an incorrect stratum can be chosen. 

In addition to the number of stratification factors, ambiguous description/definition of the randomization stratum and lack of clarity about the source of measurement (for example, the local lab or central lab results for a lab-related stratification factor) can all contribute to choosing an incorrect stratum for randomization. 

For example, in a clinical trial in the neurology area, the sponsor plan to have patients stratified by their use of cholinesterase inhibitors, corticosteroids, immunosuppressant/immunomodulator. The following stratification factor is constructed.

• Regimen includes only cholinesterase inhibitors
• Regimen includes corticosteroid (CS) as the only
• immunosuppressant/immunomodulator, alone or in combination with other MG medications (e.g., a subject on prednisone plus a cholinesterase inhibitor would be in this stratum)

Without appropriate training, it is likely that the site staff will choose the wrong category for the randomization.

It is also common that the stratification factor is based on one of the laboratory measures. The original laboratory measure is a continuous result and it is then categorized for the stratification purpose. In this case, the protocol must be clear whether or not the stratification will be based on the lab results from the local lab or central lab because the results from local versus central labs can be different. 

When a wrong stratification stratum is chosen for the randomization (the randomization error occurs), the natural reaction is trying to fix it. However, with the IRT system, it is not easy to go back to the system to fix the randomization error. Actually it is strongly encouraged not to try to fix the issue. 

In a paper by Yelland et al "Applying the intention-to-treat principle in practice: Guidance on handling randomisation errors", the author concluded:

"...the safest option is to accept the randomisation errors that do occur and leave the initial randomisation records unchanged. This approach is consistent with the ITT principle, since it enables participants to be analysed as randomised, and avoids further problems that can arise when attempts are made to correct randomisation errors. A potential disadvantage of accepting randomisation errors is that imbalance could be introduced between the randomised groups in the number of participants or their baseline characteristics. However, any imbalance due to randomisation errors is expected to be minimal unless errors are common. Imbalance can be monitored by an independent data monitoring committee during the trial and investigated by the trial statistician at the analysis stage."

It is true that if randomization errors can skew the analyses especially when the occurrence of the randomization errors is not infrequent. In a paper by Ke et al "On Errors in Stratified Randomization", the impact of the randomization errors on treatment balance and properties of analysis approaches was evaluated. 

If there are a lot of randomization errors, the study's quality and integrity will be questioned. From the statistical analysis standpoint, the strict intention-to-treat analysis may not be appropriate. With a significant number of randomization errors with incorrect treatment assignments, we may need to analyze the data using 'as treated' instead of 'as randomized'. With a significant number of randomization errors due to incorrect selection of the randomization stratum, we may need to base the stratum information on the clinical database (assuming it is correctly recorded) instead of from the information used in the IRT system. 

When randomization errors are identified during a study, the root cause of the error should be investigated. Additional training may be needed to prevent the further occurrence of the randomization error.