Randomisation test approach

The randomisation test proposed by Wiklund et al. [34] assesses the statistical significance of each individual component that enters the model. This had been studied previously, e.g. using a t- or F-test (for instance, Wold s criterion seen above), but they are all based on unrealistic assumptions about the data, e.g. the absence of spectral noise see [34] for more advanced explanations and examples. A pragmatic data-driven approach is therefore called for and it has been studied in some detail recently [34,40]. We have included it here because it is simple, fairly intuitive and fast and it seems promising for many applications. 

A second reason for randomisation is that from a statistical perspective it ensures the validity of the standards approaches to statistical inference, t-tests, analysis of variance (ANOVA), etc. 

Randomised controlled trials have tested whether specific interventions are effective in reducing allergen exposure and whether this reduction leads to a reduction in sensitisation or disease. The second approach is only valid if the first is proven. Unfortunately, despite a huge amount of research, there is no simple allergen avoidance intervention designed to be prescribed or evaluated [214(NC)]. 

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