ARRIVE Essential - Inclusion and exclusion criteria
DISCLAIMER: Information on this and related pages is based on or copied directly from the ARRIVE guidelines 2019 (please see the original guidelines for more information, references and examples that are not included on these pages):
ARRIVE Essential 10 - Item 3 - Inclusion and exclusion criteria
3a. Describe any criteria established a priori for including or excluding animals (or experimental units) during the experiment, and data points during the analysis.
Inclusion and exclusion criteria define the eligibility or disqualification of animals and data once the study has commenced. To ensure scientific rigour, the criteria should be defined before the experiment starts and data are collected. Inclusion criteria should not be confused with animal characteristics (see item 8 – Experimental animals) but can be related to these (e.g. body weights must be within a certain range for a particular procedure) or related to other study parameters (e.g. task performance has to exceed a given threshold). Exclusion criteria may result from technical or welfare issues such as complications anticipated during surgery, or circumstances where test procedures might be compromised (e.g. development of motor impairments that could affect behavioural measurements). Criteria for excluding samples or data include failure to meet quality control standards, such as insufficient sample volumes, unacceptable levels of contaminants, poor histological quality, etc. Similarly, how the researcher will define and handle data outliers during the analysis should be also decided before the experiment starts (see subitem 3b below for guidance on responsible data cleaning).
Exclusion criteria may also reflect the ethical principles of a study in line with its humane endpoints. For example, in cancer studies an animal might be dropped from the study and euthanised before the predetermined time point if the size of a subcutaneous tumour exceeds a specific volume. If losses are anticipated, these should be considered when determining the number of animals to include in the study (see item 2 – Sample size).
Best practice is to include all a priori inclusion and exclusion/outlier criteria in a pre-registered protocol (see 2.1.11 Preregistration). At the very least these criteria should be documented in a lab notebook and reported in manuscripts, explicitly stating that the criteria were defined before any data was collected.
3b. For each experimental group, report any animals, experimental units or data points not included in the analysis and explain why.
Animals, experimental units, or data points that are unaccounted for can lead to instances where conclusions cannot be supported by the raw data. Reporting exclusions and attritions provides valuable information to other investigators evaluating the results, or who intend to repeat the experiment or test the intervention in other species. It may also provide important safety information for human trials (e.g. exclusions related to adverse effects).
There are many legitimate reasons for experimental attrition, some of which are anticipated and controlled for in advance (see subitem 3a on defining exclusion and inclusion criteria) but some data loss might not be anticipated. For example, data points may be excluded from analyses due to an animal receiving the wrong treatment, unexpected drug toxicity, infections or diseases unrelated to the experiment, sampling errors (e.g. a malfunctioning assay that produced a spurious result, inadequate calibration of equipment), or other human error (e.g. forgetting to switch on equipment for a recording).
In some instances, it may be scientifically justifiable to remove outlier data points from an analysis, such as readings that are outside a plausible range. Providing the reasoning for removing data points enables the distinction to be made between responsible data cleaning and data manipulation. When reasons are not disclosed the reliability of the conclusions is in question, as inappropriate data cleaning has the potential to bias study outcomes.
There is a movement towards greater data sharing, along with an increase in strategies such as code sharing to enable analysis replication. These practices, however transparent, still need to be accompanied by a disclosure on the reasoning for data cleaning, and whether it was pre-defined.
Report all animal exclusions and loss of data points, along with the rationale for their exclusion. Accompanying these criteria should be an explicit description of whether researchers were blinded to the group allocations when data or animals were excluded (see item 5 – Blinding), and whether these criteria were decided prior to the experiment. Explicitly state where built-in models in statistics packages have been used to remove outliers (e.g. GraphPad Prism’s outlier test).
3c. For each analysis, report the exact value of N in each experimental group.
The exact number of experimental units analysed in each group (i.e. the N number) is essential information for the reader to interpret the analysis, it should be reported unambiguously. All animals and data used in the experiment should be accounted for in the data presented. Sometimes, for good reasons, animals may need to be excluded from a study (e.g. illness or mortality), and data points excluded from analyses (e.g. biologically implausible values). Reporting losses will help the reader to understand the experimental design process, replicate methods, and provide adequate tracking of animal numbers in a study, especially when sample size numbers in the analyses do not match the original group numbers.
Indicate numbers clearly within the text or on figures, and provide absolute numbers (e.g. 10/20, not 50%). For studies where animals are measured at different time points, explicitly report the full description of which animals undergo measurement, and when.
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