Designing efficient and safe clinical trials that generate reliable and accurate data is indispensable for the success of clinical drug development programs. Even though randomized, double-blind clinical trials, which reduce the risk of bias, are a preferred clinical trial design, they are not always feasible. In such cases, carefully designed open-label clinical trials may deliver valuable, high-quality clinical data that can advance the drug development process.
When planning open-label clinical trials, it is vital to carefully consider and minimize potential sources of bias. Working with an experienced and competent clinical research organization (CRO) can ensure that all potential sources of bias in open-label clinical trials are considered and addressed appropriately.
What Are Open-Label Clinical Trials, and Why Are They Needed?
Open-label clinical trials are clinical studies that are not fully blinded and in which, typically, study volunteers and investigators are aware of the assigned treatment.
Even though fully blinded clinical trials are the preferred design for clinical drug development programs, they are not always feasible. For example, blinding may be very difficult or impossible in clinical trials investigating medical devices, surgical interventions, or other non-pharmacologic treatments and in clinical trials, administering treatments in different arms via different routes. In such cases, data from open-label clinical trials can be conducted to advance drug development programs.
Open-label clinical trials can be performed at different stages of clinical drug development, from early phase to late phase clinical trials. For example, an open-label extension study may be conducted as a continuation of a randomized, late phase clinical trial.
Addressing Challenges Associated With Open-Label Clinical Trial Design
The design and conduct of open-label clinical trials come with a distinct set of challenges. They are related primarily to bias concerns due to the awareness of study volunteers and investigators of treatment assignment.
However, the careful and thoughtful design of open-label clinical trials can enhance their quality and ensure the delivery of reliable and accurate study data. Strategies that can help address the challenges associated with open-label clinical trials include:
1. A careful approach to randomization and allocation concealment
Even in open-label trials, randomization should be performed whenever possible, as it minimizes the risk of bias by ensuring that study volunteers are assigned to different treatment arms only by chance. Moreover, the randomization strategy should be considered carefully and explained explicitly in the study protocol. In addition, allocation concealment should be ensured so that it is not possible to predict what treatment an individual will receive during the clinical trial prior to randomization.
2. Introduction of blinding whenever possible
Even though open-label clinical trials do not allow full blinding, blinding still can and should be introduced whenever possible. Depending on the specifics of the trial, blinding should not be limited only to study volunteers and/or study investigators but should also be extended to caregivers and other staff members, including data collectors, outcomes assessors, data managers, and statisticians. Introducing blinding in open-label clinical trials can help reduce the risk of bias based on perceptions about the effects of the administered treatment in each arm.
3. Management of study volunteers in different treatment arms as similarly as possible
To reduce the risk of bias due to differential treatment open-label clinical trials, study volunteers in different arms should be managed in the same manner, with the exception of the study treatment. In other words, all study volunteers should be subjected to identical inclusion and exclusion criteria, study visits, procedures for sample and data collection, and timing of endpoints and analyses.
4. Performance of blinded assessments of study outcomes
One of the potential sources of bias in open-label clinical trials is the assessment of study endpoints. For subjective study outcomes, there is a risk of exaggerating the estimates of treatment effects if assessors are not blinded to treatment allocation. Therefore, it is recommended to blind outcome assessors to treatment allocation. This can be achieved by assigning independent clinicians, unaware of treatment assignment and not otherwise associated with the trial, to assess study outcomes. Alternatively, an adjudication committee, including independent clinical experts who are blinded to clinical trial operations, can validate outcome assessments.
5. Preference for objective study outcomes
Even though blinded assessments of study outcomes can help reduce bias, this is not always feasible. For example, this approach may not be applicable when most of the staff at a clinical institution is aware of treatment allocations or when the assessments occur at random time points, hindering the inclusion of blinded outcome assessors. Moreover, blinded assessments may not be feasible when it is difficult to send blinded information to an adjudication committee or when this information has to be prepared by unblinded personnel. When assessing study outcomes in a blinded manner is not possible, as robust and objective outcomes as possible should be selected. Such objective outcomes (for example, all-cause mortality, blood pressure, or body temperature) in open-label clinical trials are less prone to bias, and an unblinded assessment is not likely to affect the study results.
6. Maintenance of the confidentiality of accumulating data
Throughout an open-label clinical trial, the confidentiality of accumulating data related to treatment allocation should be maintained. If such interim data are not kept confidential, awareness of study volunteers and investigators of them may negatively affect the conduct of the clinical trial (including the recruitment of study participants) and, ultimately, the interpretation of the study findings.
7. Special considerations regarding the statistical analysis plan (SAP) and statistical analysis:
- Early finalization of the study protocol and SAP – In open-label clinical trials, the study protocol and SAP should be finalized as early as possible and preferably before the initiation of the trial.
- Design of an efficient strategy for handling missing data – An efficient approach should be designed to reduce missing data across all study arms. Moreover, the distribution of missing data across study arms should be evaluated after the completion of the study.
- Selection of the analysis population – In open-label clinical trials, study volunteers should not be excluded from the analysis for not having received the study medication. This is based on the idea that the decision not to receive study medication may have been affected by awareness of the assigned treatment.
- Strategy for handling intercurrent events – The approach to handling intercurrent events, such as taking a medication unrelated to the clinical trial during its course, should be considered thoroughly in open-label trials, as intercurrent events may be affected by study volunteers’ knowledge of treatment allocation.
Can Open-Label Clinical Trials Use Patient-Reported Outcomes?
Patient-reported outcomes can be used to record and communicate patient experience related to symptoms, functional status, and health-related quality of life. However, there are concerns that, in open-label clinical trials, the perception of a study volunteer of their symptoms or functional state may be affected by awareness of their treatment allocation.
Nevertheless, data suggest that well-designed open-label clinical trials can detect meaningful treatment effects on objective outcomes and patient-reported outcomes. For example, an analysis of individual patient data from clinical trials on non-small cell lung cancer (NSCLC) and melanoma demonstrated no evidence of clinically or statistically significant differences in measures related to both disease symptoms and overall health status, functioning, and quality of life between open-label and blinded groups.
Partner With BioPharma Services for Your Next Open-Label Clinical Trial
Due to the complexity of open-label clinical trials, working with an experienced and competent clinical partner is critical to their success. BioPharma Services is a full-service, award-winning CRO that has completed over 2,000 trials. To design efficient and safe clinical trials for our clients’ drug development programs, we rely on the expertise of a multidisciplinary team, including physicians, pharmacologists, pharmacokinetic scientists, data managers, biostatisticians, bioanalytical scientists, regulatory professionals, and recruitment specialists.
Our team’s know-how extends to all aspects of clinical trial design, conduct, and analysis, enabling us to carry out high-quality open-label clinical trials with minimized risk of bias, delivering high-quality, reliable, and accurate data.
If you are looking for an innovative and competent clinical partner to advance your clinical drug development program, complete the form below to schedule a discovery call with a BioPharma Services medical team member.