# Science Forum: Unit of analysis issues in laboratory-based research

1. Nick R Parsons 1. University of Warwick, United Kingdom
2. University of Sheffield, United Kingdom
3. University of Birmingham, United Kingdom
Feature Article
4 figures and 3 tables

## Figures

Figure 1 A strip plot showing observed lymph node size data by subject (1-12) and sample, after none and a short course of radiotherapy (Short RT). https://doi.org/10.7554/eLife.32486.004
Figure 2 Boxplots of residuals (observed values - fitted values) for each subject; symbols (∙) are medians, boxes are interquartile ranges (IQR), whiskers extend to 1.5×IQR and symbols (∘) outside these are suspected outliers (a). Quantile-quantile (Q–Q) plot of the model residuals (∘) on the horizontal axis against theoretical residuals from a Normal distribution on the vertical axis (b). https://doi.org/10.7554/eLife.32486.005
Appendix 1—figure 1 Naive use of a conventional t-test on correlated (grouped by subject) data, ρ = 0 (black circle ), ρ = 0.2 (red circle) ρ = 0.5 (blue circle) and ρ = 0.8 (green circle), inflates the type I error rate (set at 5%). (a). The type I error rate can be controlled to the required level by randomly selecting a single measurement for each subject, ρ = 0 (black circle), ρ = 0.2 (red circle), ρ = 0.5 (blue circle) and ρ = 0.8 (green circle), or made conservative (≤5%) by taking the mean of the measurements for each subject, ρ = 0 (black open circle), ρ = 0.2 (red open circle), ρ = 0.5 (blue open circle) and ρ = 0.8 (green open circle) (b). The relative efficiency of treatment effect estimates declines as the number of clusters become smaller and is always higher for the mean than the randomly selected single measurement strategy (c). The scenarios (i) – (vi) are as described in the text. https://doi.org/10.7554/eLife.32486.008
Appendix 2—figure 1 Design options for a putative laboratory study testing n samples of experimental material. https://doi.org/10.7554/eLife.32486.010

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