What is Randomization in Clinical Trials?

There are many different clinical trial types and designs in the clinical research space. If you’re considering taking part in a clinical trial, it’s important to gain a clear understanding of the different types and designs. Firstly, you have controlled and uncontrolled trials, which refer to the presence of a control group, or lack thereof. Then, based on the awareness of participants or investigators, you have open-label and blind studies.

There are also non-randomized and randomized trials. Randomization in clinical trials does exactly what it suggests, with researchers randomly assigning participants to groups that receive different treatments. This means everyone is equal and has the same chance of receiving any of the trial treatments. In most cases, the investigational group receives the new treatment and the control group receives standard therapy or a placebo.

In this article, we’ll explore randomization for clinical trials, how it works, and why it’s used.

The Purpose of Randomization in Clinical Trials

Randomization in clinical trials has many different purposes, but it’s primarily used to prevent bias. Using this method, researchers can guarantee more accurate outcomes by preventing subject biases and maximizing the validity of the results.

Clinical trial randomization can also help researchers to achieve comparability between the investigational and control groups. This means they can better explain the outcomes and result differences by treatment.

Randomized Controlled Trials vs Randomized Clinical Trials

Randomization can occur in different types of studies, including both ‘randomized controlled trials’ (RCTs) and ‘randomized clinical trials’. While they sound similar, each term refers to something slightly different.

Randomized Controlled Trials (RCTs)

In RCTs, the emphasis is on the word ‘controlled’. This means that there is a comparison or control group in the trial. For most trials, the control group will receive either a placebo, standard treatment, or no treatment. In contrast, the investigational group will receive the new treatment or intervention. Researchers can then compare outcomes from the two groups.

RCTs are considered to be one of the best types of clinical research. This is because they minimize biases and enable direct comparisons to be made between new treatments and the controls.

Randomized Clinical Trials

‘Randomized clinical trials’ is a wider term used to define clinical trials that have a random allocation of participants in different groups. The difference compared to RCTs is that randomized clinical trials don’t specifically highlight the presence of a control group.

Many randomized clinical trials have control groups and can be classed as RCTs. However, randomized clinical trials can also refer to trials with no control group and several intervention groups who all receive active treatments.

Why is Randomization Important in Clinical Trials?

Randomization is a key part of experimental design, particularly when it comes to general and clinical trials. It’s important for eliminating bias, strengthening the validity of results, and enabling researchers to gather high-quality and accurate results.

By randomly assigning participants to different and comparable treatment groups, researchers can obtain objective and reliable results for use in further research or advancements of treatments. Randomization allows for accurate statistical analysis and ensures researchers can verify that any outcomes or effects are genuinely due to the treatment and not external factors.

Although randomization is important for eliminating bias, it can never fully eliminate the possibility of selection bias. To do this, complete randomization would be required, which means each treatment assignment would be determined by ‘flipping a coin’ which is rarely, if ever used.

Benefits of Randomization

Randomization in clinical trials has several benefits, some of which have already been touched upon. Here are some more benefits of randomization in clinical trials:

• Participants are treated equally, eliminating bias in both selection and treatment
• Treatments can be directly compared to evaluate effectiveness
• Results are often more accurate than non-randomized trials
• Outcome differences can be aligned to the treatment itself and not external factors

Disadvantages of Randomization

Just as there are benefits to randomization, there are also drawbacks. Some disadvantages to randomization in clinical trials include:

• Results do not always reflect real-life situations
• Randomization of participants can sometimes be unethical or impractical if an intervention is superior or specialized medical equipment from a specific location is needed
• Participants could drop out of a trial if the random assignment does not align with their preferences
• In smaller trials, some groups could end up imbalanced and affect the validity of the outcome
• External validity could be negatively impacted if strict inclusion and exclusion criteria make the results less generalizable to the wider public

How is Randomization Done in Clinical Trials?

There are a few different ways of randomizing patients in a clinical trial. The most commonly used method is to use a table of random numbers or a computer program to generate random numbers. These random numbers are then assigned to the subjects or treatment conditions.

Sometimes, clinical teams will also assign participants by their date of birth or alternation, but this can be more prone to bias and isn’t typically classed as true randomization.

Different Types of Randomization

There are several randomization methods in clinical trials. Which option a clinical team uses will depend on factors like the trial’s size, its objectives, and logistical considerations.

Simple Randomization

Simple randomization is one of the most straightforward randomization methods in clinical trials. It is based on a single sequence of random assignments and typically involves flipping a coin, throwing a dice, or a similar mechanism to allocate participants into treatment groups with equal probability.

Simple randomization is easy to implement and computerized random number generators are often used to eliminate predictability and minimize bias.

Issues with Simple Randomization

While simple randomization is easy to do and is often used in clinical research, it doesn’t work particularly well with smaller trials. This is because the group sizes can't always be balanced. Additionally, if participant recruitment is ongoing and participants are added to groups over time, simple randomization can lead to inaccurate results.

Blocked Randomization

Also known as ‘block randomization’, this method is when participants are assigned to treatment groups in blocks or batches, instead of individually. The size of each block is usually decided by the investigative team and is determined by dividing the total number of participants into equal-sized groups.

For example, if there are 100 participants and two treatment groups, they can be divided into 10 blocks of 10. The blocks can then be randomly assigned to either treatment group.

Issues with Block Randomization

One of the most common problems with block randomization is the possibility of selection bias. This can happen if researchers can easily predict the treatment allocation of a group or if participants are aware of the block structure.

Stratified Randomization

This method of randomization involves separating participants into groups depending on categories of characteristics or values that could affect the outcome of the experiment. For example, gender, age, or disease severity. The total number of participants is usually divided into subgroups depending on the agreed characteristics, before randomly assigning each participant in that characteristic group to a treatment group.

Stratified randomization is often used when researchers want to ensure that certain subgroups have equal representation across all treatment groups, and that randomization is independent of these factors.

For example, you could divide a group of 100 into four characteristic groups based on age and gender (i.e. young and old; male and female). The participants in each characteristic group would then be randomly assigned to either treatment group A or B.

Issues with Stratified Randomization

Although stratified randomization is used to remove selection bias, it can often mean that the treatment groups don’t always have the same important characteristics. Additionally, it can be difficult to implement if there are too many characteristics at play or the sample group is very small.

Adaptive Randomization

In adaptive randomization, allocation is changed and participants are assigned to different treatment groups depending on the information or data collected during the trial. It involves adjusting the probability of allocation to each treatment group according to predefined rules or criteria and the outcomes of previously enrolled participants. This could be an observed response, the balance of characteristics, or ethical considerations.

As an example, a clinical study of 100 participants split into two groups could have more participants assigned to the treatment group that shows more positive results. This could help researchers to better monitor the effectiveness of a new drug against a placebo.

Issues with Adaptive Randomization

Adaptive randomization can be a complex method, which can cause problems with using it in clinical studies. It is also more difficult to design and analyze, with more careful monitoring and evaluation often needed. This method can also lead to the possibility of bias or manipulation of results.

Minimization

Minimization is used to balance out the prognostic factors in clinical trials and ensure there is a balance between any treatment groups that have one or more baseline variables. It is an alternative to the traditional methods mentioned above and has sometimes been referred to as ‘adaptive stratified randomization’.

With minimization, the imbalance within each factor is calculated should the patient be allocated to a particular treatment group. These imbalances are then added together to give an overall study imbalance. The treatment group that would minimize the imbalance can be chosen directly or a random element can be added. When implemented correctly, it can lead to better-balanced groups and results.

Issues with Minimization

While minimization can remove imbalance, it doesn’t completely meet all the requirements of randomization. This is because it’s a deterministic procedure, whether there is a random element or not, which means it doesn’t offer the unpredictability of true randomization. This could allow for predictions of treatment assignments to be made in some situations.

Allocation Concealment

This is a technique used to prevent selection bias. It works by ensuring the individuals involved in participant enrollment are completely unaware of the group to which the next participant will be assigned.

Issues with Allocation Concealment

Issues can occur with this method if allocation is not adequately concealed. If researchers are aware of the assignment sequence, they could influence (consciously or otherwise) which patients are assigned to which group.

Unequal Randomization

This method of randomization differs from other methods. It refers to the treatment groups having more participants in one than the other, while other methods focus on having a balanced number between two groups.

It is also known as ‘unbalanced’ or ‘asymmetrical’ randomization. With this method, subjects are not assigned in a 1:1 ratio, and instead will be assigned using any number of other ratios such as 2:1, 3:1, or occasionally 4:1.

This method is typically used in early-phase trials where the key objective is to assess safety or dose-ranging of a new treatment. In these cases, it could be more practical and beneficial to have more participants receive the treatment than the control.

Blinding

Blinding, or a blind trial, is a randomization method that concerns the participants and the treatment they receive. In blinding, the subjects are unaware of the treatment they are getting. There are three types of blind trials:

• Single-blind - information is only hidden from the participant
• Double-blind - information is withheld from both the participant and the data collectors
• Triple blind - everyone in the study is unaware of the information, including the subjects, data collectors and evaluators

Randomization in Modern Clinical Trials

Clinical trials are continuing to evolve at pace, which means randomization approaches are also evolving and modernizing to keep up. With new technologies, methodologies, and statistical techniques available, there will continue to be more innovative ways to ensure unbiased and effective participant assignment.

Ethics of Randomization

Several randomization methods raise questions about ethics, which should be carefully considered by clinical teams. For example, in some randomization methods, participants are not shown the full picture or are given incomplete information. This can raise questions as to whether participants would have agreed to be included in the study if they were given all the information.

In addition, some participants receive no treatment at all if they are part of a placebo group. This can again raise questions about whether they would be involved in a clinical trial if they knew they would not receive any treatment. Another ethical concern here is that participants have a chance of receiving an inferior treatment or one that could see them experience side effects.

There are also ongoing considerations about whether participant treatment methods should be determined by probability and not by medical professionals.

Randomization and Bias in Clinical Trials

We’ve already noted that randomization can minimize bias, but it can also increase bias in some cases. Unfortunately, bias is inherent in human selections, whether through conscious choices or unknowingly. Researchers or those assigning participants may not be able to ignore certain factors such as age or gender if they see the subjects face to face, have a photo of them, or are privy to more detailed information.

For example, clinical professionals could find it difficult to completely ignore the severity of someone’s symptoms when assigning them to a treatment group if they knew which participants were receiving the treatment and which were receiving a placebo.

The Future of Randomization in Clinical Trials

Technology advancements are making it easier than ever to implement randomization in clinical trials. For example, virtual and remote trials are a more common occurrence now and have the potential to randomize patients in decentralized trials. This means it could be possible to bridge the gap between traditional and virtual clinical trials.

Another advancement in modern clinical trials that is enabling randomization to make great advances is wearable technology. Data from wearable devices can inform randomization decisions based on real-time health metrics. This could be particularly beneficial in clinical trials that use adaptive randomization and need to keep a closer eye on outcomes as they happen.

Artificial Intelligence (AI) and Machine Learning (ML) are also playing a more significant role in randomization, as they are in many industries. AI could be used in predictive analysis to forecast patient responses and improve methods of randomization. Additionally, ML algorithms are capable of analyzing vast amounts of data which could enable researchers to create more refined stratification groups and improve randomization accuracy.

Conclusion

Randomization is a key component in many clinical trials when it comes to minimizing bias and improving outcomes. But it’s not without its challenges, especially for participants. If you’re considering taking part in a clinical trial, our guide should have gone some way in helping you gain an understanding of randomization methods and how they could impact you in a trial situation.