3. Inclusion and exclusion criteria For each experimental group, report any animals, experimental units, or data points not included in the analysis and explain why. If there were no exclusions, state so. explanation

Animals, experimental units, or data points that are unaccounted for can lead to instances where conclusions cannot be supported by the raw data [1]. 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 Item 3a) 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).

Most statistical analysis methods are extremely sensitive to outliers and missing data. In some instances, it may be scientifically justifiable to remove outlying data points from an analysis, such as obvious errors in data entry or measurement with readings that are outside a plausible range. Inappropriate data cleaning has the potential to bias study outcomes [2]; providing the reasoning for removing data points enables the distinction to be made between responsible data cleaning and data manipulation. Missing data, common in all areas of research, can impact the sensitivity of the study and also lead to biased estimates, distorted power and loss of information if the missing values are not random [3]. Analysis plans should include methods to explore why data are missing. It is also important to consider and justify analysis methods that account for missing data [4,5].

There is a movement towards greater data sharing (see item 20 – Data access), 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 methods were defined before any data were collected.

Report all animal exclusions and loss of data points, along with the rationale for their exclusion. For example, this information can be summarised as a table or a flowchart describing attrition in each treatment group. Accompanying this information 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 [6]). Explicitly state where built-in models in statistics packages have been used to remove outliers (e.g. GraphPad Prism’s outlier test). 

References

1. Kafkafi N, Agassi J, Chesler EJ, Crabbe JC, Crusio WE, Eilam D, Gerlai R, Golani I, Gomez-Marin A, Heller R, Iraqi F, Jaljuli I, Karp NA, Morgan H, Nicholson G, Pfaff DW, Richter SH, Stark PB, Stiedl O, Stodden V, Tarantino LM, Tucci V, Valdar W, Williams RW, Würbel H and Benjamini Y (2018). Reproducibility and replicability of rodent phenotyping in preclinical studies. Neuroscience & Biobehavioral Reviews. doi: 10.1016/j.neubiorev.2018.01.003
2. Scott S, Kranz JE, Cole J, Lincecum JM, Thompson K, Kelly N, Bostrom A, Theodoss J, Al‐Nakhala BM, Vieira FG, Ramasubbu J and Heywood JA (2008). Design, power, and interpretation of studies in the standard murine model of ALS. Amyotrophic Lateral Sclerosis. doi: 10.1080/17482960701856300
3. Kang H (2013). The prevention and handling of the missing data. Korean J Anesthesiol. doi: 10.4097/kjae.2013.64.5.402
4. Allison PD (2001). Missing data. SAGE Publications. doi: 10.4135/9781412985079
5. Jakobsen JC, Gluud C, Wetterslev J and Winkel P (2017). When and how should multiple imputation be used for handling missing data in randomised clinical trials - a practical guide with flowcharts. BMC Med Res Methodol. doi: 10.1186/s12874-017-0442-1
6. Holman C, Piper SK, Grittner U, Diamantaras AA, Kimmelman J, Siegerink B and Dirnagl U (2016). Where have all the rodents gone? The effects of attrition in experimental research on cancer and stroke. PLoS Biol. doi: 10.1371/journal.pbio.1002331