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In PD models, PD-like symptoms are not observed with 42 58% loss of striatal DA [10].
In addition, the mixture PD models provided different conclusions about the relationships between PD onset points and proficiency.
Across all mixture PD models and across both groups, for the no-PD classes, the EAP score estimates of the mixture PD models appeared to be almost identical to those of the 2PLM.
The mixture PD models consistently indicated that the mathematics test was affected by PD, as all of the mixture PD models fitted the data better than a standard 2PLM.
We expected this result to hold for all mixture PD models, as Suh et al. (2012) found that most mixture PD models showed quite similar behavior in this respect.
The mixture PD models are based on different assumptions on how PD affects test-taking behavior.
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PK-PD models were developed in NONMEM.
PK-PD models of vital signs.
PK-PD models for SERT and NET uptake inhibition biomarkers.
PK-PD models for SERT occupancy, NET inhibition, and DHPG.
So how does one mathematically formulate network biology-based PK-PD models?
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