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Second branch criterion was a 2-point score decrease in the PANSS excitement item (Week 2).
First branch criterion was a 2-point score decrease in at least 2 of 5 PANSS positive items (Week 2).
Of the 1049 patients who did not meet the first branch criterion, 755 ultimately did not respond (NPV = 72%).
The NPV could be improved further by using the second branch criterion to separate the 1049 patients into likely non-responders and not predictable.
Patients who did not meet this criterion were further partitioned based on whether they had improved by at least 2 points on the excitement item (4-Excitement) at Week 2. Patients who met neither the first nor second level criteria were identified as likely non-responders; patients who did not meet the first branch criterion, but met the second branch criterion were identified as not predictable.
The decision tree applied to the learning data set is illustrated in Figure 2. Of the 1494 patients in the learning data set, 644 (43%) ultimately demonstrated response, defined as a ≥30% improvement in PANSS Total score at Week 8. Using the first branch criterion, 445 patients were identified as likely responders, of which 352 actually responded (PPV = 79%).
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Of the 929 patients who did not meet first and second branch criteria at Week 2, 698 did not respond (NPV = 75%).
"Likely responders" met the first branch criteria; "likely non-responders" did not meet first or second criterion; "not predictable" patients did not meet the first but did meet the second criterion.
The module testing with branch coverage criteria required fewer test cases if the module has higher testability.
The proposed testability can also be used to demonstrate whether the test cases based on uniform partitions, such as branch coverage criteria, result in homogeneous partitions that is known to be more effective than random testing.
The personalized urban multi-criteria quasi-optimum path problem (PUMQPP) is a branch of multi-criteria shortest path problems (MSPPs) and it is classified as a NP-hard problem.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com