Multivariate analyses were conducted by using general linear regression models fitted to the logit transformed β methylation values to assess the association between methylation at each CpG locus and clinical or tumor covariates, adjusting for age, race, menopausal status, and stage as appropriate.
By holding this assumption in mind, we will use general regression neural network (GRNN) as a prediction model to bridge the gap between the measured structural parameters of each absorber and its sound absorption coefficient.
The associations between genotypes/haplotypes, emphysema, and lung function parameters (FEV1, FVC, FEV1/FVC and MEF50) were evaluated by using general linear model, whereas logistic regression analysis was used to evaluate the potential confounders and to further study the risk for emphysematous changes and their severity with certain genotype.
The statistical significance of the temporal trends observed was tested using general linear regression.
If there are additional residents available at a RCF (ie, more than one person with HD participating at a RCF), they can also be included in the study and the final statistical analysis will take into account the clustering by using logistic regression with general estimating equation.
This study used general and logistic regression analyses in the evaluation.
We analyzed possible relationships between the SOC variable and the SF-36 subdimensions when controlling for age, sex, marital status, education and comorbidity by using multiple regression in the general linear model procedure of SPSS for Windows (version 14.0).
Next, we statistically estimate the fraction of general merchandise workers below 200percentt FPL by using linear regression.
By using logistic regression, satisfaction with pain management was predicted by general pain in the last 24 hours (odds ratio = 4.02), pain-related interference with mood (odds ratio = 7.31), and age (odds ratio = 1.8).
We took the within-family correlation into account by using conditional logistic regression and general estimation equations (GEE, version 4.13) [ 29] in our analysis.
We examined the associations between occurrence of obesity and general health questionnaire caseness at the end of follow-up (phase 7) by using logistic regression analysis.
