Comparing the natural history assumptions between the models proves difficult.
Predicting long-term climatic behavior by using computer models has proved difficult, and the notion of fiddling with the planet's climate based on the results generated by those models worries even scientists who are fully engaged in the research.
The category of "unknown" race was a significant univariate predictor, but was not included in the final model as the category was not large enough (5%) to statistically influence the final multivariable model and it proved difficult to act upon prospectively in identifying new, at-risk patients.
While costs may be better analysed using generalised gamma models (to handle skewness), it proved difficult to run such models within the context of this study given the need to deal with simultaneous issues of missing data, clustering and high proportions of patients with zero costs for many individual resource use items.
Longer probe delays, required for improved dynamic range in regions of high temperature fluctuations, proved difficult to model as a result of a nontrivial decay in the O2 Raman coherence arising from complexities associated with the triplet ground electronic state of the O2 molecule.
This involves direct construction and calibration of a suitable inverse crank dynamic model, which owing to singular behaviour at top-dead-centre (TDC), has proved difficult via physical model construction, calibration, and inversion.
Regarding autoimmune diseases in general, and Sjögren's syndrome (SS) in particular, verifying and expanding such models is desirable, because it has proved difficult to extrapolate findings to existing models that were often developed in different contexts [ 1- 3].
Car seats, formula and BPA-free bottles all require a strong R&D and innovation position a model that proves difficult for private label challengers.
However, finding such a model has proven difficult.
This has proved difficult in animal models.
