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When we introduced the ten factors above into a multifactorial logistic model, only the Pp genotype and C-MMSE scores were negatively correlated (y = 4.51 × 1.043 − 2.22, r = −0.108, 95% CI 0.014 ~ 0.883, P = 0.033).
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In conclusion, by studying the association between a limited panel of genetic variants and nephropathy risk, we developed a robust multifactorial logistic regression model to predict nephropathy in our study populations.
More recently, Easton and colleagues have provided multifactorial logistic regression models to classify UVs in the BRCA genes [ 14].
The approach we use here is very similar to the classical approach based on multifactorial logistic regression, which consists of: Step 1: Choice of a model H0 against which the data is compared.
LASSO was then applied to these pathway PC1s to obtain a logistic model with pathway predictors.
Bio-Plex Manager software version 6.1 was used to generate standard curves (4 parameter logistic model/5 parameter logistic model) and the biomarker concentration was derived from appropriate standards.
To verify presented hypotheses logistic model was used.
A multiple logistic model was applied with two causes of death, cardiovascular disease and diabetes mellitus, as outcome variables.
two-parameter logistic model.
For α=β=1, we obtain the logistic model.
(iii) four parameter versions of the Logistic model (Eqn. 14.2).
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