Types of randomisation in clinical trials

Hence, sample size and covariates must be balanced in clinical research. The stratified randomization method addresses the need to control and balance the influence of covariates. Specific covariates must be identified by the researcher who understands the potential influence each covariate has on the dependent variable.

Stratified randomization is achieved by generating a separate block for each combination of covariates, and subjects are assigned to the appropriate block of covariates. After all subjects have been identified and assigned into blocks, simple randomization is performed within each block to assign subjects to one of the groups.

The stratified randomization method controls for the possible influence of covariates that would jeopardize the conclusions of the clinical research. For example, a clinical research of different rehabilitation techniques after a surgical procedure will have a number of covariates. It is well known that the age of the subject affects the rate of prognosis. Thus, age could be a confounding variable and influence the outcome of the clinical research.

Stratified randomization can balance the control and treatment groups for age or other identified covariates. Although stratified randomization is a relatively simple and useful technique, especially for smaller clinical trials, it becomes complicated to implement if many covariates must be controlled. However, this method is rarely applicable because clinical research subjects are often enrolled one at a time on a continuous basis.

When baseline characteristics of all subjects are not available before assignment, using stratified randomization is difficult. One potential problem with small to moderate size clinical research is that simple randomization with or without taking stratification of prognostic variables into account may result in imbalance of important covariates among treatment groups.

Imbalance of covariates is important because of its potential to influence the interpretation of a research results. Covariate adaptive randomization has been recommended by many researchers as a valid alternative randomization method for clinical research. This online software is very simple and easy to implement. Up to 10 treatments can be allocated to patients and the replication of treatment can also be performed up to 9 times. The major limitations of this software is that once the randomization plan is generated, same randomization plan cannot be generated as this uses the seed point of local computer clock and is not displayed for further use.

Other limitation of this online software Maximum of only 10 treatments can be assigned to patients. For example, the total number of patients in a three group experimental study is 30 and each group will assigned to 10 patients. The results is obtained as shown as below partial output is presented. The seed for the random number generator[ 14 , 15 ] Wichmann and Hill, , as modified by McLeod, is obtained from the clock of the local computer and is printed at the bottom of the randomization plan.

If a seed is included in the request, it overrides the value obtained from the clock and can be used to reproduce or verify a particular plan. Up to 20 treatments can be specified. The randomization plan is not affected by the order in which the treatments are entered or the particular boxes left blank if not all are needed.

The program begins by sorting treatment names internally. The sorting is case sensitive, however, so the same capitalization should be used when recreating an earlier plan. The output of this online software is presented as follows. The benefits of randomization are numerous. It ensures against the accidental bias in the experiment and produces comparable groups in all the respect except the intervention each group received. The purpose of this paper is to introduce the randomization, including concept and significance and to review several randomization techniques to guide the researchers and practitioners to better design their randomized clinical trials.

Use of online randomization was effectively demonstrated in this article for benefit of researchers. For small to moderate size clinical trials with several prognostic factors or covariates, the adaptive randomization method could be more useful in providing a means to achieve treatment balance. Several researchers have proposed covariate adaptive randomisation as a legitimate alternative form of randomization for clinical research.

In covariate adaptive randomization, the sequential assignment of a new participant to a specific treatment group takes into account the specific covariates and previous assignments of participants. Covariate adaptive randomization uses the minimisation approach by measuring the sample size difference of multiple covariates. You might want to balance your participants into groups, or blocks.

Permuted block randomization is a way to randomly allocate a participant to a treatment group, while keeping a balance across treatment groups. Save my name, email, and website in this browser for the next time I comment.

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Research Funding Opportunities. Cancer Grand Challenges. Shah Presented by Mr. Randomization A method based on chance alone by which study participants are assigned to a treatment group.

Randomization minimizes the differences among groups by equally distributing people with particular characteristics among all the trial arms.

The researchers do not know which treatment is better. Need of Randomization If, at the end of a clinical trial, a difference in outcomes occurs between two treatment groups say, intervention and control possible explanations for this difference would include: 1.

Randomization aims to obviate the third possibility. Criteria for randomization 1. Simple Randomization Randomization approach is simple and easy to implement in a clinical research. In large clinical research, simple randomization can be trusted to generate similar numbers of subjects among groups. However, randomization results could be problematic in relatively small sample size clinical research, resulting in an unequal number of participants among groups.

Dynamic adaptive random allocation Simple and block randomization methods are defined, and allocation sequences set up, before the start of the trial. In contrast, dynamic randomization methods allocate patients to treatment group by checking the allocation of similar patients already randomized, and allocating the next treatment group "live" to best balance the treatment groups across all stratification variables.

Biased coin randomization and minimisation are two such methods. Performance Bias Performance bias is the tendency for participants to change their behavior or responses to questions because they are aware of being in a trial and of their treatment allocation.

Detection Bias Detection bias is concerned primarily with blinding. In this case, however, the concern is about those collecting the data, not those providing it. Sample size bias Sample size calculations are a deceptively simple tool to minimize bias — an insufficiently large sample size can lead to imprecise estimation, biasing the results downward. Blinding Blinding is a procedure in which one or more parties in a trial are kept unaware of which treatment arms participants have been assigned to, in other words, which treatment was received.

Blinding is an important aspect of any trial done in order to avoid and prevent conscious or unconscious bias in the design and execution of a clinical trial.