Research Randomizer is also a closed-source, free, randomization web service. It does not support group labels. This randomization service has an excellent tutorial with five sample randomization scenarios for random sampling, random assignment, random block assignment, generating random numbers in a specified range, and random ordering of a set of items. It permits complete control over the randomization protocol. However, it does not support randomization for cross-over trials or adapted randomization.
This is a closed-source, free desktop software, available for download and running, on the MS-Windows operating system, under the dot net framework. Two kinds of outcome measures can be fed into the program, which is a binary and time-to-event program. It has a limit of up to ten treatment groups. Also this program supports interim measures and stops rules for terminating a trial when certain criteria are met. It has adaptation factors. The more the adaptation factors, the more will be the probability of their assignment to treatments with better outcomes.
The zero of this adaptation factor no adaptation means complete randomization. This program is a desktop software, running under the Windows operating system and requires pre-installation of the dot net framework. The most important drawback of the randomization software is the problem of unmatched groups. Various methods have been used to overcome the problem of unmatched trial groups including minimization and stratification, with minimization providing more acceptable results.
With minimization the first subjects are enrolled randomly into one of groups. The subsequent subjects will be allocated to treatment groups after hypothetical allocation of each subject to every group, and then calculating an imbalance score. Using these imbalance scores, we can decide to which group the new subject must be allocated, to have the minimum amount of imbalance, in terms of prognostic factors.
Pure minimization is indeed completely deterministic, that is, we can predict which group the next subject will be enrolled in, provided the factor levels of the new subject are known. This may invalidate the principle of trial blindness and introduce some bias into the trial. To overcome this shortcoming some elements of randomness are incorporated into the minimization algorithm, to make the prediction unlikely. Unfortunately the whole process of minimization is well beyond the skill of a typical clinical researcher, especially when the problem of unequal group allocations has to be taken into account.
The difficulty in computation has resulted in a relatively less frequent use of minimization methods, in randomized clinical trials. The computer software can perform excellently in these situations, especially when the implementation has been logical. In the following sections, the aspects of two minimization programs are presented. Again the selection of these programs is based on the availability and ease of use. Minim is a free, but closed-source, MS-DOS program, for minimizing subjects into the arms of a clinical trial.
Minim is an interactive program, which means it prompts for user input, one at a time, then displays the next prompt, and asks for another input. Therefore, if one has already defined a trial by this program and previously saved it, they can enter its name to load it. Otherwise the name will be used as also the name of the new trial settings.
When defining a new trial, the program asks for trial information and the different trial settings. Appendix B is a typical minim program session, which displays the questions the program asks and sample answers to the questions. To the best of our knowledge this program does not support setting the method and the amount of probability used for allocation of subjects to trial groups. Also it does not allow changing the distance measure.
MagMin is an online, closed-source, private minimization service, for blind allocation of subjects to multi-center clinical trials. As this is not a public service, its properties cannot be fully evaluated. However, in an article presenting this program it has been described as a minimization program using the Pocock and Simon's minimization method,[ 18 ] using standard deviation as the distance measure.
Anyhow, due to its unavailability it cannot be elucidated for certainty of its full potential. A demo website is available, which shows active allocation. In the running demo it is possible to add subjects by minimization to an already defined trial. The demo asks for different properties of new subjects and enrolls it into the trial and returns the numerical blind code of the enrolled subject.
MinimPy is a free, open-source, desktop minimization program, which allocates subjects to treatment groups in a clinical trial. Of special note is the ability to choose distance measure and the probability method. In addition this program supports the biased coin minimization as the probability method, which has not been used in previous programs. MinimPy is produced using the python programming language,[ 23 ] which is very strong and efficient for computational purposes, and it is one of the most readable programming languages.
MinimPy requires python to be installed, with gtk libraries, for support of graphic user interface. This program has different windows for such things as minimization settings, variables, groups, allocations, and balance [ Figure 4 ]. It also supports pre-loading an already allocated sample into a trial. MinimPy program showing different tabs for settings, variables, groups, allocations, table and balance. In this figure the settings tab is displaying variuos aspects of minimization protocol such as probability method and distance measure.
With the advent of computer program and online services for randomization and minimization in clinical trials, an increasing number of randomized clinical trials are going to make efficient use of them. However, there are few aspects of these program and services that need more attention to make them more acceptable for various needs of clinical researches.
Also it can be concluded that there is an increasing need to move toward open-source development, to enhance the quality of the produced software and make them available to the critiques of different clinical and software specialists. MS-DOS programs usually do not need installation. Alternatively, you can run the program in a DOS console under Windows. For the latter, click the start menu and select Run. 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. Source of Support: Nil. Conflict of Interest: None declared. National Center for Biotechnology Information , U. J Hum Reprod Sci. KP Suresh. Author information Article notes Copyright and License information Disclaimer.
Address for correspondence: Dr. This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-Share Alike 3. This article has been cited by other articles in PMC. Abstract Randomization as a method of experimental control has been extensively used in human clinical trials and other biological experiments. Keywords: Block, graphpad quickcalc, patient, randomization.
Simple randomization Randomization based on a single sequence of random assignments is known as simple randomization. Block randomization The block randomization method is designed to randomize subjects into groups that result in equal sample sizes. Stratified randomization The stratified randomization method addresses the need to control and balance the influence of covariates.
Covariate adaptive randomization 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.
Frane JW. A method of biased coin randomization, its implementation and validation. Drug Inf J. How to use randomize. Statistics notes. Treatment allocation in controlled trails: Why randomize?
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