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Structural equation modeling allows a prespecified model to be tested; however, a major advantage of DCM over other connectivity approaches is the evidence-based model comparison procedure.
The detection and characterization of nonlinearities provided by population modeling allows a better understanding of how a drug should be used in clinical practice.
By considering the cross-talking of multiple pathways, network modeling allows a more comprehensive analysis of a complex system than the pathway-centric approach.
When repeated measurements are made on several related statistical units, mixed-effects modeling allows a more robust estimation of model parameters than using only available individual data [ 4– 6].
Multi-state modeling allows a more comprehensive understanding of the data and also enables analysis from the standpoint of the entire clinical history rather than focusing on the outcome of the joint replacement procedure in isolation.
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GEMANOVA modeling allowed a simple visualization of the entire experimental space.
This modeling approach, combining computational fluid dynamics (CFD) with biofilm models allows a systematic study of biofouling in membrane systems.
In theoretical investigations, this model allows a simplified analysis.
The model allows a tradeoff between memory usage and training time.
An in ovo model allows a rapid development and vascularization of forming tumors.
The model allows a user to choose which providers to activate in the system.
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