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A second important issue concerns the effects of pooling behavioural data from different recruiting centres and different countries.
This provides a unique opportunity to employ a substantial dataset to investigate the effects of pooling datasets on classifier accuracy, signature stability and enrichment of functional categories.
To control for the confounding effects of pooling active comparators, the present analysis also examined the effects of sitagliptin versus placebo on cardiovascular safety and found no increased risk of cardiovascular events with sitagliptin.
For this we fitted a linear model with the main effects of pooling approach, trait, P and N p and all possible interactions among them (P and N p were fitted as numerical predictor variables) and the replication as blocking factor.
To analyze the effects of pooling, we extended our model of phenotype sequencing to take sequencing error into account.
The effects of pooling on Se and Sp of tests have been evaluated previously [ 8, 17].
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That combines the network effects of pooled data across games with a huge market to conquer.
We next examined the effect of pooling different numbers of tagged libraries per sequencing lane, for a phenotype sequencing experiment of 32 strains.
These apparently dissimilar results are likely an effect of pooling the samples in the previous study where one or more of the samples pooled may have had an abundance of Staphylococcus, which would have decreased the representation of the other genera in the pool.
Therefore, scale scores could be computed for >96% of patients, which was slightly higher than the individual country analysis owing to the effect of pooling the datasets.
We explored the effect of pooling artificial datasets on the classification performance (double loop cross validation), independent validation, and signature size.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com