Again, the results for the linear model with adjustment are very similar to those for the exponential model with adjustment.
Results from the backwards elimination multiple linear regression model without LDL and for this model with adjustment for LDL are presented in Table 2.
First, RSV positive samples were compared to controls using a multivariate linear regression model with adjustment for the RNA integrity number (RIN).
The prognostic impact of each immune marker was studied using Cox model with adjustment for clinicopathological prognostic factors previously identified.
Comparisons between groups were performed with general linear model with adjustment for covariates or Student t test.
The mean change scores in the instruments were compared to the categories in the anchor by using ANCOVA (General Linear Model) with adjustment for baseline scores.
Genotype phenotype association was tested using a linear regression model with adjustment for the first 10 PCs.
These were also analysed using a general linear model with adjustment for baseline TAF or TAE values, recruiting hospital and type of surgery.
In a final model with adjustment for age, sex, education, BMI, smoking, PA, alcohol consumption, estimated reported energy intake and hypertension, the association was attenuated but remained significant (HR 1.29, 95% CI 1.09 to 1.53).
In a model with adjustment for age and centre, low SRH was associated with a higher hazard of T2DM (HR 1.67, 95% CI 1.48 to 1.88; table 4).
The treatment effects for the dichotomous outcomes were assessed using OR estimates from a multiple logistic regression model with adjustment for factors as listed in the table footnotes or additional medication or cover use.
