Clinical trial randomization is a method of experimental control. It is the process of assigning patients by chance to groups that receive different treatments.

Randomization helps prevent bias (1). Bias occurs when a trial's results are affected by human choices or other factors not related to the treatment being tested.
A good experiment or trial minimizes the variability of the evaluation and provides unbiased evaluation of the intervention by avoiding confounding from other factors, which are known and unknown.
Randomization ensures that each patient has an equal chance of receiving any of the treatments under study, generate comparable intervention groups, which are alike in all the important aspects except for the intervention each groups receives.
It also provides a basis for the statistical methods used in analyzing the data (2).

References:

NIH. https://www.cancer.gov/about-cancer/treatment/clinical-trials/what-are-trials/randomization/clinical-trial-randomization-infographic#:~:text=Randomization%20helps%20prevent%20bias.,to%20the%20treatment%20being%20tested. [Last Accessed July 2022]

Suresh K. An overview of randomization techniques: An unbiased assessment of outcome in clinical research. J Hum Reprod Sci. 2011 Jan;4(1):8-11. doi: 10.4103/0974-1208.82352.

Benefits of Randomization

The basic benefits of randomization are as follows: it eliminates the selection bias, balances the groups with respect to many known and
unknown confounding or prognostic variables, and forms the basis for statistical tests, a basis for an
assumption of free statistical test of the equality of treatments (1).

References:

Suresh K. An overview of randomization techniques: An unbiased assessment of outcome in clinical research. J Hum Reprod Sci. 2011 Jan;4(1):8-11. doi: 10.4103/0974-1208.82352.

Types of Randomization

Simple Randomization

Randomization based on a single sequence of random assignments is known as simple randomization. This technique maintains complete
randomness of theassignment of a subject to a particular group. The most common and basic method of simple randomization is flipping
a coin. For example, with two treatment groups (control versus treatment), the side of the coin (i.e., heads - control,
tails - treatment) determines the assignment of each subject. Other methods include using a shuffled deck of cards
(e.g., even - control, odd - treatment) or throwing a dice (e.g., below and equal to 3 - control, over 3 - treatment).
A random number table found in a statistics book or computer-generated random numbers can also be used for simple randomization
of subjects (1). The advantages are that it is inexpensive and easy to implement (2). The disadvantages include the risk of producing imbalances (1) in the number of participants
in the groups, as well as in the distribution of baseline risk factors, in studies with small sample sizes (N < 100).

Block Randomization

The block randomization method is designed to randomize subjects into groups that result in equal sample sizes. This method is used to ensure
a balance in sample size across groups over time (1). In block randomization, the randomization list is a random sequence of blocks of participants instead of individual
participants. The blocks have a pre-determined size; for example, four participants in one block, with six possible intervention
and control sequences. This strategy ensures that intervention and control groups are balanced in terms of the number of participants.
To ensure allocation concealment using this method, random variation of block sizes should be used (four to eight participants per block) (2).

Although balance in sample size may be achieved with this method, groups may be generated that are rarely comparable in terms of certain covariates. For example, one group may
have more participants with secondary diseases (e.g., diabetes, multiple sclerosis, cancer, hypertension, etc.) that could confound the data and may
negatively influence the results of the clinical trial. It is important to control for these covariates because of
serious consequences to the interpretation of the results. Such an imbalance could introduce bias in the statistical analysis and reduce the power
of the study. Hence, sample size and covariates must be balanced in clinical research (1).

Stratified Randomization

Stratified randomization is an alternative when balance for key baseline risk factors is desired. Each new participant is first classified
into strata according to baseline characteristics (e.g., age or disease severity), and each stratum has a separate randomization list.
Thereafter, once the participants are categorized into their stratum, they are randomized to either the intervention or the control groups.
Stratification should be carried out using few relevant strata in order to work well. Stratified and block randomization strategies can be
combined so that patients are first categorized into a stratum and then randomized in blocks (2).

References:

Suresh K. An overview of randomization techniques: An unbiased assessment of outcome in clinical research. J Hum Reprod Sci. 2011 Jan;4(1):8-11. doi: 10.4103/0974-1208.82352.

Ferreira JC, Patino CM. Randomization: beyond tossing a coin. J Bras Pneumol. 2016 Sep-Oct;42(5):310. doi: 10.1590/S1806-37562016000000296.

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