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The present study therefore aimed to further explore the psychometric properties of the EAI by combining the datasets of a number of surveys carried out in five different countries (Hungary, UK, Spain, US, and Denmark).
However, most studies using administrative data have examined claims data from either traditional fee-for-service Medicare, or health maintenance organizations, but not both due to the complexity involved in obtaining and combining the datasets.
The present study aimed to further explore the psychometric properties of the EAI by combining the datasets of a number of surveys carried out in five different countries (Denmark, Hungary, Spain, UK, and US) that have used the EAI with a total sample size of 6,031 participants.
We evaluated a number of possibilities for combining the datasets including cohort-specific thresholds (see Methods S1), however this made little difference to the final CNA pattern and a standard threshold of +/− 0.3 (log2) was applied universally as previously described by us [17] and others [10].
Combining the datasets resulted in 280 differentially expressed genes.
Combining the datasets was necessary in order to achieve the necessary sample size for mean comparison tests to be meaningful.
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Both studies included strain 493-89 and we combined the datasets from these 2 studies.
There was never an intention to combine the datasets.
Next, to compare populations in the two datasets directly, we combined the datasets.
To combine the datasets, the German data was temporarily converted to completed weeks.
MolClass combines the datasets to generate a computational model for each screen submitted (Fig. 1B).
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