Sentence examples for multinomial regression framework from inspiring English sources

Within the multinomial regression framework, we can also perform formal tests of heterogeneity in allelic odds ratios between sub-phenotypes.

We have presented the multinomial regression framework as a powerful approach to the analysis of sub-phenotypes.

Again, this is not unexpected since the controls do not contribute to our proposed test of heterogeneity derived within the multinomial regression framework.

We have developed a novel test of disease association with SNPs, allowing for heterogeneity in allelic odds ratios between sub-phenotypes, within a multinomial regression framework.

HETEROGENEITY: test of heterogeneity of the effect of the causal SNP between sub-phenotypes within a multinomial regression framework (2,000 cases against 2,000 controls).

disease association within a multinomial regression framework (i.e. controls against obese and non-obese T2D sub-phenotypes); disease association within a logistic regression framework (i.e. controls against obese and non-obese T2D cases combined); heterogeneity of effects between obesity sub-pheno-types within a multinomial regression framework.

This framework requires fewer parameters than the multinomial regression model, and thus may offer greater power to detect association if sub-phenotypes can be appropriately ordered.

To address this issue, we have developed a novel test of association within a multinomial regression modeling framework, allowing for heterogeneity of genetic effects between sub-phenotypes.

SMLR is an efficient implementation of a true multiclass probabilistic classifier based on the well-studied multinomial logistic regression framework.

The multinomial logistic regression framework described above is extremely flexible and can be easily extended to allow for non-multiplicative disease risks, for example, by including an additional indicator I G i = 1) of dominance in equation (1).

First, the multinomial regression-based analysis performs well in comparison to existing methods formulated in a logistic regression framework over a range of models incorporating heterogeneity of genetic effects between sub-phenotypes.