Indicate the inclusion and exclusion criteria

Information crucial to the internal validity of the experiment is missing. The following three fields are considered compulsory

In the experiment node:

  • Inclusion criteria
  • Exclusion criteria

In the analysis node:

  • Criteria for excluding data points from the analysis

Inclusion and exclusion criteria define how you will decide the eligibility or disqualification for animals (or experimental units), samples and data once a study has started. It is important to decide these before your experiment begins to prevent you from unconsciously biasing your results by deciding as the experiment progresses.

Inclusion criteria

Inclusion criteria define the prerequisites animals (or experimental units) and samples must meet to be eligible for inclusion in the experiment.

Examples include:

  • A tumour must reach a specific minimum size
  • Animals must meet a specific training threshold
  • Body weight must be within a certain range to be able to undergo a procedure
  • A minimum deficit for an induced experimental model to adequately model a specific aspect of a disease. 

If you are not setting inclusion criteria for your study explicitly state this (e.g. “no a priori inclusion criteria”).

Exclusion criteria

Exclusion criteria define the criteria that will disqualify animals (or experimental units) and samples from being included or staying in the experiment. 

Examples include:

  • If any animals develop a motor impairment that would affect a behavioural measurement, they will be removed from the study.
  • If any animals develop complications from a surgical procedure, they will be removed from the study.
  • Animals reach a humane endpoint, they will be removed from the study and euthanised (e.g. a maximum tumour volume).
  • If any samples are damaged during sample collection or preparation the sample will be excluded (e.g. if a histological section is badly damaged during processing and cannot be used).
  • If any sample volumes are not large enough to be reliably measured the sample will be excluded from the study (e.g. a blood sample is too small to accurately measure a specific metabolite).
  • Any samples that fail to meet prespecified quality control standards or that have unacceptable levels of contamination will not be used.

For criteria to exclude data from the analysis use the field ‘Criteria for excluding data points from the analysis’ in the analysis node. 

If losses are anticipated, these should be taken into account when determining the number of animals to include in the study. For example, if your experiment involves a surgical intervention that has a 10% attrition rate, add an extra 10% to your calculated sample size account for expected attrition.

If you are not setting exclusion criteria for your study explicitly state this (e.g. “no a priori exclusion criteria”).

Criteria for excluding data points from the analysis

Criteria that would disqualify data from being included in the analysis should be defined before you start your study to prevent subconscious bias influencing decisions.

Examples include:

  • Data values outside a biologically plausible range will not be included in the analysis (e.g. a negative value for body weight).
  • Data values resulting from an error during data collection will not be included in the analysis (e.g. someone forgets to switch on equipment for a recording).
  • Typos in data values will not be included in the analysis (e.g. if errors are transcribed when copying from the original data source).
  • Outliers will be removed using a built-in model in a statistical package (e.g. Grubbs’ method in GraphPad Prism).

If are not setting exclusion criteria for the data in your study explicitly state this (e.g. “no a priori exclusion criteria for data”).

See ARRIVE guidelines item 3b for guidance on responsible data cleaning.