Randomization is designed to "control" bias by all means. It prevents selection bias and ensures against accidental bias. Specifically, it 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 important aspects except for the intervention each group receives. Additionally, it provides a basis for the statistical methods used in analyzing data. Randomization plan design is to set out an appropriate randomization plan in order that each treatment is equally likely to be assigned to any given experimental unit.
Our statistical experts will help you to develop a clear and simple randomization plan to ensure its unpredictability and balance and thus reduce or eliminate bias. Accordingly, the randomization plan design for the various clinical trials below constitutes most of our work:
Although treatment assignment is completely unpredictable, using the random number generator is simple and easy to implement. For example, in small trials, even if treatment is balanced at the end of a trial, it may not be balanced at some time during the trial. Considering this problem, we design to divide potential patients into m blocks of size 2n, randomize each block such that n patients are allocated to A and the other n patients to B, and then choose the blocks randomly. It will ensure equal treatment allocation within each block if the complete block is used.
Imbalanced randomization in subject numbers will reduce statistical power, and imbalance in prognostic factors is also more likely inefficient for estimating treatment effect. The trial may not be valid if it is not well balanced across prognostic factors. Our solution is using stratified randomization to achieve balance within subgroups. Stratification can balance subjects on baseline covariates, tend to produce comparable groups with regard to certain characteristics (e.g. gender, age, race, disease severity), and thus produces valid statistical tests and eliminate the effect from prognostic factors.
Allocating equal numbers of patients to experimental and control groups is the most statistically but may not be the most economically efficient or ethically/practically feasible. When two or more treatments under evaluation have a cost difference, we can randomize fewer patients to the expensive treatment and more to the cheaper ones. The substantial cost savings can be achieved by adopting a smaller randomization ratio such as a ratio of 2:1, with only a modest loss in statistical power. Generally, randomization ratio of 3:1 will lose considerable statistical power. It may bring useful results. Therefore, we choose randomization plan cautiously.
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1. Himashree Bhattacharyya, D., Dr, D. K. B., Star Pala, D., Dr, J. B. W., Marak, M. D. (2013) ‘Fundamentals of randomized controlled trials’, Internet Journal of Pharmacology, 12(3), 130-139.