We used a cohort of 1642 consecutive surgical patients (development sample) that were treated in the UMC Utrecht to develop prediction models.
We as R.A. researchers are collaborating to help develop prediction tools just for the purpose of matching the right level of care to the right person.
To develop prediction models for long-term respiratory morbidity.
However, there are several alternative methods to develop prediction models.
In our study, we chose to develop prediction envelopes to represent this facet of uncertainty.
The partial least squares regression (PLSR) method was applied to develop prediction models.
Modified partial least squares regression with cross-validation was used to develop prediction models.
Regression analysis was applied to develop prediction equations for the estimation of blast induced ground vibration.
In this context, the objective of this study is to develop prediction models to better understand causal factors inducing SCs.
There were actually some attempts to develop prediction models for CBR, but most of these models were essentially statistical correlations.
The data from the two groups were therefore pooled to develop prediction equations for daily CH4 emissions (g day−1).
