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In conclusion, the frailty model approach allows the simultaneous analysis of all successive events intervening after a first breast cancer.
The mixed model approach allows for patients with incomplete data to be included and utilizes the data that is available for all patients.
This linear model approach allows rigorous quantification of the error incurred in the normalisation due to unstable housekeeping gene expression and variable sample efficiencies through the simultaneous estimation of VR and VE.
This "modified" two-part model approach allows the conventional, separate modeling of the process for generating zeros and a count data model for users that explicitly accounts for overdispersion.
The use of a combined model approach allows us to effectively combine tools of analysis on one hand with a handle on process complexity on the other (this is especially true of the TASEP/PBN/ODE models).
The random effect model approach allows for estimating the effects of individual- and center-level characteristics, and their impact on the estimates of between-center variation in the bias of nutritional assessment [ 14].
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The model approach allowed us to study a variety of different pharmacological combination effects between TKIs and IFN α as well as different treatment regimens and to gain a systematic understanding of the combination therapy with the ultimate goal to guide clinical applications.
Our proposed modelling approach allows the estimation of aesthetic values in spatial and qualitative terms for most viewpoints in the European Alps.
This efficient modelling approach allows for a full analysis of the anchorage zone based solely on the geometrical and material properties known at the design stage.
Wet lab experiments proved that the proposed modelling approach allows to predict novel biologically relevant interactions.
Using a stochastic simulation modelling approach allows outbreaks with a variety of characteristics that are epidemiologically plausible to be generated.
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