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Finally, the use of multi-level modeling allowed us to control for clustering within practices and to more clearly separate patient, provider and practice factors.
Hybrid modeling allowed us to compute the necessary and sufficient conditions for the existence of different behaviors.
The combination of habitat and demographic modeling allowed us to create models that address processes driving these populations at all scales, which is critical to understanding how regional populations respond to landscape processes such as habitat loss and fragmentation.
In addition to the parameters described above, Bayesian modeling allowed us to compute the latent disease status of the sampled animals.
Using prospectively collected data, modeling allowed us to explore the impact of various clinical case definitions and laboratory testing strategies in the containment of influenza during mass gatherings, and to offer new insights into epidemic testing strategies.
Additionally, protein modeling allowed us to relate the results of phylogenetic studies with the predicted structures.
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Agent-based modeling allows us to study the resulting global performance metrics of the simulator kernel.
Additionally, modeling allows us to assign exposures specific to biennially updated residential addresses for the entire period of follow-up.
However, using GEE models allowed us to analyze data from all 143 subject visits, and we identified several significant associations.
The use of similarity-based models allowed us to obtain better positive predictive values in some sets.
This coarse−grained model allowed us to simulate 10 transitions of a calcein molecule with reasonable use of CPU time.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com