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When multiple, partially independent factors measured with variable accuracy on different scales are associated with an outcome, the most accurate predictions can only be achieved by multivariate prediction models.
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Multivariate prediction models were developed to predict optimal cup stem orientations for the chosen design parameters of 12 hip implants.
In this study, multivariate prediction models achieved a higher level of accuracy than narrow-band vegetation indices, making multivariate modeling the best choice for mapping.
Multivariate prediction models need to be evaluated in cohorts distinct from the derivation set to determine external validity.
Thus, for the aforementioned issues, we consider both UPMs and multivariate prediction models inappropriate for industrial accident forecasting.
Multigene signatures introduced an important concept into prognostic marker research: the need for multivariate prediction models.
Unfortunately, the human mind is not well suited for rapid and reproducible calculation of multivariate prediction models.
The tests primarily rely on quantification of ER and proliferation-related genes and combine these into multivariate prediction models.
Despite close to four decades of IHC literature, very few papers describe correctly trained and independently validated multivariate prediction models.
The multivariate PLS method relates two data matrices, X and Y, to each other by a linear multivariate prediction model.
The last step (Step 3) was the multivariate prediction model construction.
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