An Overview of Randomization Techniques for Clinical Trials

What is Randomization?

Randomization is the method of assigning participants to different groups in Clinical Trials , such as treatment and control groups, ensuring each participant has an equal probability of being placed in any group. This technique has become a cornerstone of scientific research methodology. The demand for randomized clinical trials has surged in various fields of biomedical research, including athletic training. Over the past two decades, leading medical journals like the Journal of the American Medical Association and the BMJ have shown a growing interest in publishing studies based on randomized controlled trials.

The concept of randomization was first introduced by Fisher in a 1926 agricultural study. Since then, it has been recognized as a crucial tool for making unbiased comparisons between treatment groups. Five years after Fisher’s initial paper, the first randomized clinical trial on tuberculosis was conducted. In this trial, 24 participants were paired into 12 comparable pairs, and each participant within a pair was randomly assigned to either the control or treatment group by flipping a coin. This random assignment ensures that each participant has an equal chance of being placed in any group, thereby eliminating potential bias and making the groups comparable on the dependent variable.

Randomization is the only method to avoid systematic characteristic bias in clinical trials. While a simple coin toss can achieve randomization, more sophisticated methods are often required, especially in small clinical trials. These advanced methods will be explored further in this review.

Why Randomize?

Researchers emphasize randomization for several key reasons:

1. Avoid Systematic Differences: It’s crucial that participants in different groups do not differ systematically. In clinical trials, if treatment groups are inherently different, the results can be biased. For instance, in a study on the effectiveness of a walking intervention, if more older adults are placed in the treatment group, the results might be skewed. This imbalance could make it hard to distinguish the treatment effects from the influence of age differences, necessitating adjustments for these variables to achieve unbiased results.
2. Prevent Allocation Bias: Proper randomization ensures that no one knows in advance which group a participant will be assigned to, preventing allocation bias. If researchers or participants know the group assignments beforehand, it could introduce selection bias, affecting the trial’s outcomes. Schulz and Grimes noted that trials with inadequate or unclear randomization could overestimate treatment effects by up to 40% compared to those with proper randomization.
3. Statistical Adjustments: Techniques like analysis of covariance (ANCOVA) and multivariate ANCOVA are often used to correct for covariate imbalances during the analysis phase of a clinical trial. However, interpreting these adjustments can be challenging because covariate imbalances can lead to unexpected interaction effects, such as unequal slopes among covariate subgroups. One critical assumption in ANCOVA is that the regression slopes are consistent across all covariate groups (homogeneity of regression slopes). Variations in the required adjustments for each covariate group can complicate the analysis.

How to Randomize?

There are several methods for randomly assigning participants to treatment groups in clinical trials. Here, we’ll review some common techniques: simple randomization, block randomization, stratified randomization, and covariate adaptive randomization. Each method has its own advantages and disadvantages, so it’s important to choose the one that will yield valid and interpretable results for your study.

Simple Randomization

Simple randomization involves assigning participants to groups based on a single sequence of random assignments. This method ensures complete randomness in group assignments. The most basic form of simple randomization is flipping a coin. For example, with two groups (control and treatment), heads might assign a participant to the control group, while tails assigns them to the treatment group. Other methods include using a shuffled deck of cards (e.g., even cards for control, odd cards for treatment) or rolling a die (e.g., numbers 1-3 for control, 4-6 for treatment). Random number tables or computer-generated random numbers can also be used.

This approach is straightforward and easy to implement, especially in large trials (n > 200), where it typically results in balanced group sizes. However, in smaller trials (n < 100), simple randomization can lead to unequal group sizes. For instance, in a small trial with 10 participants, a coin toss might result in 7 participants in the control group and only 3 in the treatment group, creating an imbalance.

Block Randomization

Block randomization is a technique used to ensure that participants are evenly distributed across treatment groups, maintaining equal sample sizes over time. This method involves creating small, balanced blocks with predetermined group assignments, which helps keep the number of participants in each group similar.

Steps in Block Randomization:

1. Determine Block Size: The researcher decides the block size, which should be a multiple of the number of groups (e.g., for two groups, a block size of 4 or 6). Smaller blocks are preferred for better control over balance.
2. Calculate Balanced Combinations: All possible balanced combinations of assignments within the block are calculated. For example, with two groups (control and treatment) and a block size of 4, the combinations could be TTCC, TCTC, TCCT, CTTC, CTCT, and CCTT.
3. Randomly Assign Blocks: Blocks are randomly selected to assign participants to groups. For instance, in a trial with 40 participants, a sequence might be [TTCC / TCCT / CTTC / CTTC / TCCT / CCTT / TTCC / TCTC / CTCT / TCTC], resulting in 20 participants in each group.

While this method ensures balanced sample sizes, it may not always account for certain covariates. For example, one group might end up with more participants having secondary diseases, which could affect the trial’s results. Therefore, it’s important to control for these covariates to ensure accurate interpretation of the data.

Stratified Randomization

Stratified randomization is a technique used to manage and balance the impact of covariates in research. This method ensures that groups are balanced based on participants’ baseline characteristics. Researchers must identify specific covariates that could influence the dependent variable. The process involves creating separate blocks for each combination of covariates and assigning participants to the appropriate blocks. Once all participants are assigned, simple randomization within each block determines group assignments.

This method helps control the potential influence of covariates that could affect the outcomes of a clinical trial. For instance, in a clinical trial comparing different rehabilitation techniques post-surgery, covariates like patient age, which can affect healing rates, are considered. Stratified randomization ensures that both control and treatment groups are balanced for age or other relevant covariates.

Covariate adaptive randomization is a method recommended by many researchers as an effective alternative for clinical trials. In this approach, each new participant is assigned to a treatment group based on specific covariates and the previous assignments of participants. This method, first described by Taves, aims to minimize imbalance by assessing the sample size across various covariates.

For example, consider a study with two groups and 40 participants, using sex (male, female) and body mass index (underweight, normal, overweight) as covariates. Initially, the first nine participants are randomly assigned to groups. When the 10th participant, who is male and underweight, needs to be assigned, the Taves method calculates the marginal totals for each covariate category in both groups. The participant is then assigned to the group with the lower total to maintain balance. In this case, the control group has a lower total compared to the treatment group , so the 10th participant is assigned to the control group.

The Pocock and Simon method of covariate adaptive randomization is akin to the Taves method but involves temporarily assigning participants to both groups. This approach uses the absolute difference between groups to decide the final assignment. To minimize imbalance, the participant is placed in the group with the lowest sum of absolute differences among covariates.

For instance, when assigning the 10th participant, the Pocock and Simon method would:

1. Temporarily assign the participant to the control group, resulting in marginal totals of 3 for males and 2 for underweight.
2. Calculate the absolute difference between control and treatment groups (males: 3 control – 3 treatment = 0; underweight: 2 control – 2 treatment = 0) and sum these differences (0 + 0 = 0).
3. Temporarily assign the participant to the treatment group, resulting in marginal totals of 4 for males and 3 for underweight.
4. Calculate the absolute difference between control and treatment groups (males: 2 control – 4 treatment = 2; underweight: 1 control – 3 treatment = 2) and sum these differences (2 + 2 = 4).
5. Assign the participant to the control group because the sum of absolute differences is lower (0 < 4).

Pocock and Simon also proposed a variance approach, which calculates the variance among treatment groups instead of the absolute difference. While this method performs similarly, both approaches are limited to handling only categorical covariates.

Frane introduced a covariate adaptive randomization method for both continuous and categorical covariates, using P values to identify imbalance among treatment groups, where a smaller P value indicates greater imbalance.

The Frane method for assigning participants to control or treatment groups involves several steps:

1. Temporarily assign the participant to both groups.
2. Calculate P values for each covariate using t-tests and ANOVA for continuous variables, and the chi-square test for categorical variables.
3. Identify the minimum P value for each group, which indicates greater imbalance.
4. Assign the participant to the group with the higher minimum P value to avoid imbalance.

For example, when assigning the 10th participant (male and underweight), the Frane method would:

1. Temporarily assign the participant to the control group, resulting in P values for each covariate.
2. Calculate the P values using the chi-square test, as the covariates are categorical.
3. Determine the minimum P values: 1.0 for the control group and 0.317 for the treatment group.
4. Assign the participant to the control group because it has the higher minimum P value, indicating better balance.

Covariate adaptive randomization, such as the Frane method, reduces imbalance more effectively than traditional randomization methods. It is particularly useful for balancing important covariates among groups, even as the number of covariates increases.

One issue with covariate adaptive randomization methods is that treatment assignments can sometimes become quite predictable. Researchers using these methods may start to believe that they can easily predict the group assignment for the next participant, which contradicts the fundamental principle of randomization. This predictability arises from the continuous assignment of participants to groups, where the current distribution of participants might indicate future group assignments. Scott et al. noted that this predictability is also present in other methods, such as stratified randomization, and should not be excessively criticized. Zielhuis et al. and Frane proposed a practical solution to mitigate predictability: randomly assigning a small number of participants to groups before applying the covariate adaptive randomization technique.

Randomization is a cornerstone of clinical research, safeguarding against selection bias and ensuring comparable groups. By balancing sample size and baseline characteristics, it underpins the validity of statistical comparisons.

Selecting the optimal randomization method depends on factors such as trial size, desired balance (sample size or covariates), and participant enrollment pattern. Figure 6 provides a decision-making flowchart for this purpose. For instance, a small trial with continuous enrollment and a crucial covariate like age would benefit from CAR.

Simple randomization is suitable for large trials, while block randomization balances sample size and stratified randomization controls for specific covariates. However, CAR often surpasses these methods in achieving overall balance.

In conclusion, this review aimed to introduce randomization concepts and techniques to athletic training researchers. Careful consideration of trial characteristics is essential for selecting the most appropriate method to enhance the rigor of clinical research.