Urn randomization program

While both designs share the same favorable features of consistent imbalance control, simple implementation and applicability to all trial scenarios, the BUD significantly reduces the probability and the timing predictability of deterministic assignments. The practical advantage of the BUD over the MP comes from simplicity in implementation and applicability for all treatment number and allocation ratio scenarios.

As a randomization design without a uniform distribution for all feasible randomization sequences, the proposed BUD could be challenged when a randomization-model is desired for the analysis of the trial results. For small trials, when all feasible randomization sequences can be listed by a computer program and the probability associated with each sequence is calculated, a randomization model based permutation test could be performed with the consideration of the unequal probabilities associated with different sequences.

For trials with a large sample size, this could be difficult because of the total number of feasible sequence for BUD will be prohibitively large. Computer simulation could be considered to repeatedly sample the randomization sequence using the BUD algorithm for randomization-model based analysis. Further works are needed to exam this issue. In practice, the likelihood based analysis using the population model can be performed regardless of the randomization procedure.

Some limitations of the BUD have been noticed. By using the urn model, the BUD cannot control treatment imbalance below the value of w 1. For example, if the target allocation is , the minimal balanced set will include 12 assignments. Treatment imbalances in any subsets of these 12 assignments could occur.

In conclusion, the proposed block urn design combines the high allocation randomness of the maximal procedure and the simplicity of the permuted block design. It can be used in clinical trials where both treatment imbalances and deterministic assignments are seriously concerned. The authors would like to thank Dr.

Yuko Palesch, Dr. Robert Woolson, Dr. Valerie Durkalski, and Dr. Sharon Yeatts for their careful review and great comments for this manuscript. From 4 and 5 , we have. Based on 2 and 9 ,. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication.

As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

National Center for Biotechnology Information , U. Contemp Clin Trials. Author manuscript; available in PMC Nov 1. Wenle Zhao and Yanqiu Weng. Author information Copyright and License information Disclaimer. Copyright notice. The publisher's final edited version of this article is available at Contemp Clin Trials.

See other articles in PMC that cite the published article. Abstract Permuted block design is the most popular randomization method used in clinical trials, especially for trials with more than two treatments and unbalanced allocation, because of its consistent imbalance control and simplicity in implementation.

Keywords: randomization, block urn design, sequential clinical trial, treatment imbalance, allocation randomness, deterministic assignment, correct guess. Introduction Selection bias is the most devastating factor in clinical trials.

After each treatment allocation, the selected ball is placed in the inactive urn. Repeat steps 2 through 4 until the last subject is randomized. Open in a separate window. Statistical properties of the block urn design 3.

Steady-state probabilities With an urn model, the conditional probability depends solely on the contents in the active urn. Table 3 Steady-state probability of BUD randomization sequence Two-treatment balanced allocation scenarios. Randomness comparison under two-treatment balanced allocation scenarios The trade-off between treatment imbalance and allocation randomness exists for all randomization designs. Figure 1. Randomness comparison under general scenarios Based on the availabilities of implementation algorithms, the comparison under two-treatment unbalanced allocation scenarios will be limited to the BUD, the PBD and the MP, the comparison under multiple treatment scenarios will be limited to the BUD and the PBD only.

Figure 2. PBD: Permuted block design. Discussion and Conclusion The proposed block urn design BUD consistently demonstrates advantages over the commonly used permuted block design PBD across all trial scenarios. Footnotes Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. References 1. Hill AB. The Clinical trial. Br Med Bull. Achieving balance in clinical trials. Applied Clinical Trials.

Trends in the application of dynamic allocation methods in multi-arm cancer clinical trials. Clinical Trials.

Control Clinical Trials. Quantification of predictability in clinical trials using block randomization. Drug Information Journal. Zhao W, Weng Y. A simplified formula for quantification of the probability of deterministic assignments in permuted block randomization.

Journal of Statistical Planning and Inference. Poststratified subgroup analyses can also be performed on the basis of the urn design permutational distribution. This provides a basis for analyzing the subset of patients with observed responses when some patients' responses can be assumed to be missing-at-random.

For multiple mutually exclusive strata, these tests are correlated. For this case, a combined covariate-adjusted test of treatment effect is described. Finally, we show how to generalize the urn design to a prospectively stratified trial with a fairly large number of strata. If an error such as this occurs, you will need to delete the subject and the re-enter. There is no way to view total counts of observations except via the built-in print screen routine of DOS. UConn Health.

Search University of Connecticut. A to Z Index. Hit the Enter key. UCHC underscore urn dot exe, all one word, without the quotes to start the program. Otherwise, if the program does not find any data files, it asks: Is this the first time you have run this program?

Defining Outcome Randomization Groups After you name the study, the program will ask you to define your outcome groups. The program asks: How many outcome assignments are there? Before you hit the last enter after the cat food group, your screen should look like this: Defining the Balance Variables After creating the randomization groups, the program will then ask: How many variables are there? Sex Variable The program will lead you through a series of prompts.

How many choices for this variable? Enter a number from At the prompt, enter 2 since our sex variable has two response categories. Enter the text string for choice 1 Twenty character maximum For the text string for choice 1, type Buck and hit enter. A buck is a male rabbit. Enter the text string for choice 2 Twenty character maximum For the text string for choice 2, type Doe and hit enter.

Recruitment Location Variable A new screen will appear to create the information for the second balancing variable. Menus Either at the completion of setting up a study or, if one is already created, upon selecting a particular study from the Main Menu, the screen displays a set of 6 study-specific options from which to choose.

Open Study Menu View study info allows you to look at the study design, including what outcome randomization assignment groups are defined, how many variables are being balanced on and the choices for balancing variables.

View urns allows you see the current distribution of the randomization procedure. Add subject allows you to enter new subjects. You should carefully record and double-check the ID of the subject you are entering so that you will be able to view this subject later. After you enter the subject data, the last screen will indicate the randomization group to which the subject was assigned. View Subject will allow you look at the urn characteristics and assignment group of any given subject, one at a time.