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The objective of this study was to evaluate the best age threshold for stage grouping by comparing multivariable models designed to evaluate the independent impact of various prognostic factors, including age based stage grouping, on the disease specific survival (DSS) of our population-based cohort.
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SUPPORT is a multivariable model designed to estimate probability of survival for seriously ill hospitalized patients over a period of the subsequent 180 days.
Final multivariable models were designed by using a saturated model including all potential risk factors and manual backwards elimination.
†Final multivariable models were designed that used a saturated model including all potential risk factors (see Methods) and manual backwards elimination.
To this end, a novel grey multivariable model is designed in this paper.
The industrial model predictive controllers are designed using large dimensional multivariable models and they are often identified using ad-hoc single input bump tests.
Integral controllability (IC) is a desired property of multivariable models used in robust controller design.
All multivariable models accounted for the cluster-design using GEE.
The multivariable models were adjusted for the sampling design (stratified sampling without replacement), as described previously [ 12].
However, the full impact of disparities in potential confounders including adiposity measures, smoking, alcohol consumption and angiotensin converting enzyme inhibitor use [ 16, 66- 68] on the association of RA status with circulating interleukin-6 concentrations may not have been accounted for in multivariable models, particularly given the cross-sectional design of our study.
The multivariable model is used to design an H∞ controller for the experimental structure to suppress vibrations.
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