Abstract
Many studies in the biomedical research literature report analyses that fail to recognise important data dependencies from multilevel or complex experimental designs. Statistical inferences resulting from such analyses are unlikely to be valid and are often potentially highly misleading. Failure to recognise this as a problem is often referred to in the statistical literature as a unit of analysis (UoA) issue. Here, by analysing two example datasets in a simulation study, we demonstrate the impact of UoA issues on study efficiency and estimation bias, and highlight where errors in analysis can occur. We also provide code (written in R) as a resource to help researchers undertake their own statistical analyses.
Highlights
Defining the experimental unit is a key step in the design of any experiment
In the simplest possible experimental setting where each experimental unit provides a single outcome or observation, and only in this setting, the experimental unit is the same as both the unit of observation (i.e the unit described by the observed outcomes) and the unit of analysis (UoA)
Experimental designs are generally improved by increasing the number of experimental units, rather than increasing the number of observations within the unit beyond what is require to measure within unit variation with reasonable precision
Summary
Defining the experimental unit is a key step in the design of any experiment. The experimental unit is the smallest object or material that can be randomly and independently assigned to a particular treatment or intervention in an experiment (Mead et al, 2012). In the simplest possible experimental setting where each experimental unit provides a single outcome or observation, and only in this setting, the experimental unit is the same as both the unit of observation (i.e the unit described by the observed outcomes) and the unit of analysis (UoA) (i.e. that which is analysed) In general this will not always be the case, so care must be taken, both when planning and reporting research, to clearly define the experimental unit, and what data are being analysed and how these relate to the aims of the study. In laboratory based research in the biomedical sciences it is almost always the case that multiple observations or measurements are made for each experimental unit These multiple observations, which could be simple replicate measurements from a single sample or observations from multiple sub-samples taken from a single sample, allow the variability of the measure and the stability of the experimental setting to be assessed. Data within experimental units are likely to be dependent (correlated), whereas data from different experimental units are generally assumed to be independent, all other things being equal (i.e after removing the direct and indirect effects of the experimental interventions and setting)
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