Randomizing to Study Groups

  • Updated

Randomize animals into study groups in your studies

Reduce bias and save time using Benchling In Vivo automated Randomization:


Before you can add animals to groups, you must first define the groups that will be used during your study. This can be done during study design or at any time during your study within the "Treatment groups" section.

You must also define the maximum number of animals to be assigned to each group.

Animal Selection

To begin, select the animals to go through the randomization process. In Vivo offers exclusion criteria to automatically exclude animals that will not progress to randomization. All deceased animals, animals already assigned to groups or without any recorded measurements, will be automatically excluded in the first step and the number and reason for excluded animals is displayed alongside the number of animals that will proceed to distribution, outlier exclusion and randomization. 

Select date 

Since you cannot randomize animals if they are already in group, are deceased or do not have any measurements recorded any such animals will be automatically excluded at this step. 

Define whether you wish to use the latest measurements for all animals, this may span multiple dates, or only data from a defined date. 

Use the date picker to select the date and review the numbers of animals that will be included to the next step. 

Click Next to proceed.

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Set Study Groups

Define the number of animals that you would like to randomize to each group. The capacity of each group is determined by the maximum value mentioned in the pre-conditions above.

To the right of the screen we display the total number of animals initially selected, the number of animals to be assigned and the number of animals to be excluded.

Click Next to proceed.

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Define Exclusion Criteria

Studies will automatically exclude animals based on a metric selected by the user. To select this metric, click on the Exclude by dropdown menu at the top of the screen.

You will be presented with two box and whisker plots. The top plot displays a pre-exclusion overview and the bottom plot displays a post-exclusion overview.

By default, Studies uses the Auto mean setting. This is an optimal setting for excluding animals if you do not have a target value in mind.

Alternatively, you can deselect this option and define a Target mean. When this is entered, Studies will exclude animals whilst attempting to hit the target mean value with the remaining animals. This is a powerful feature if you have a large cohort with a large number of animals to be excluded. If you are excluding a small number of animals, it can be difficult to reach the target mean.

You can override both of these options and manually change the selection by scrolling to the bottom of the screen using the Show Animals button. As you add/remove animals, the plots and analytics will automatically update. If you make manual changes and would like to revert to Auto mean or Target mean, click the Undo Changes button in the blue banner.

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In Benchling Studies there are two methods of Randomization that you can choose from:

  1. Block Randomization: This is a deterministic method of allocating animals to groups based on one metric. The best case outcome will be generated by rank ordering the animals by metric and assigning them sequentially to evenly sized groups. Only one single outcome is possible and so you can only click "Randomize" one time.
    • This method is not suitable for unbalanced group sizes. If you are allocating animals across groups of differing capacities consider using clustered randomization and selecting a single metric instead. 
  2. Clustered Randomization: This is a non-deterministic method of allocating animals to groups that can take into consideration an attribute such as Sex or DOB, as well as multiple (up to 3) metrics for a bias free allocation using K-means clustering.

Here you can select an attribute and the metrics you would like to randomize by. Clicking the Randomize button will update your results in the table below. For Clustered randomization you can click this button as many times as necessary to generate the optimum distribution.

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Each of your study groups will be displayed with a breakdown of each animal to be assigned. Alongside each group, you can see the mean and either SD or SEM (depending on your treatment groups settings) for the metrics you selected as well as an ANOVA calculated P-value for comparing the means of the groups to greater confidence in these results

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Please review all metrics for the proposed randomized groups. Groups cannot be changed once the study has data associated with it. After your review, click Finish to confirm the groups.

Use more than one Attribute for randomization to groups

You can distribute animals across groups according to Sex, Donor ID, or Date of birth. If more than one of these attributes are required for randomization, for example Sex and DOB, two separate randomizations will be required.

  1. Apply a filter to the animals table to select only Female animals.
  2. Select all animals in the filtered table and use bulk action to Randomize to groups.
  3. Complete randomization for these animals using DOB as the attribute
  4. Return to the animals table and apply a filter for only Male animals.
  5. Repeat steps 2 and 3.

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Automated Recaging

After Randomizing your animals you can leverage the automatic recaging tool accessible from the bulk actions menu in the animals table to move animals into cages based on their new group. You can also see the details of the Randomization in an autogenerated report found under Randomizations in the study overview section.

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