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The cut-off values used for defining the two patient groups in the second part of the study were chosen to distinguish clearly between the two groups in order to allow for an optimal comparison of the effects of B-glucose.
It is challenging to quantify lifestyle in an optimal comparison group for, for example, centenarians.
Secondly, the dataset does not cover all the information needed to perform an optimal comparison of cost-effectiveness.
Knowing that the sensitivity and specificity differ from one array platform to another, this may not be an optimal comparison.
However, the current design was chosen to allow an optimal comparison between the healthy volunteers and the normoinsulinemic hyperglycemic patients with diabetes.
One of the most challenging issues in the design of case control studies is the selection of an optimal comparison group that minimizes selection bias, while allowing cost-efficient and feasible recruitment.
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A single state may not be the optimal comparison population, and Minnesota in particular has a highly educated population and a proactive public health infrastructure with strong ties to the healthcare delivery system.
Secondly, we calculate the feature vector R by using an iterative approach denoted as in Algorithm 2. Thirdly, we extract optimal comparison by interpreting R as encoding a bipartite graph and finding the maximum weight bipartite matching, which is denoted as in Algorithm 3. In Algorithm 1 we have the following.
Hence, for optimal comparison of the NCEP projects' effectiveness a CEP that accounts for the relative importance of different outcomes is required.
The bar was introduced for optimal comparison.
Finally, more work needs to be done to identify the optimal comparison paradigm for assessment of SALI.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com