Sentence examples for main analysis model from inspiring English sources

Our main analysis model (Main) therefore included age, sex, education, BMI, LDL-C, HDL-C, smoking status, pack-years of smoking, ETS, physical activity, indicator variables for summer and fall (winter and spring served as reference), humidity, plus city, which was included to capture spatial (unmeasured) confounding.

We first describe the modelling framework for our main analysis, model A. This model brings together IPD and AD into the same synthesis model, with both types of evidence contributing to the estimation of all key models parameters (i.e. absolute and relative effectiveness estimates).

To evaluate the robustness of our main analysis model, we performed a series of models that included additional covariates.

The main analysis model was modified in such a way that the individual EQ-5D dimension was employed instead of the EQ-5D Index.

The remaining variables were entered into the main analysis model in six steps: demographic factors; life time cardiovascular and affective conditions; the additional explanatory value of other health related indicators; contextual factors; two-way interaction terms; three-way interaction terms.

Furthermore we added covariates known to be associated with cardiovascular disease or with systemic inflammation such as hypertension, diabetes, and intake of statins to investigate the robustness of our main analysis model.

The main analysis modelling was limited to 10 years, in the absence of robust longer-term risk models.

Because no confounding of the association between SNPs and the examined traits is expected by CP risk factors, the main analysis models were not adjusted for smoking or diabetic status.

The impact of individual demographic factors as well as theoretical mediators (self-efficacy, social support, intrinsic motivation and self-regulation) on the intervention effect using interaction terms included in the main analysis models will be examined.

The cumulative total score (0 to 5) was used in the main analysis (modeling), but a three-level variable is presented for the purpose of descriptively highlighting the percentage of women with and without any stressor experienced during pregnancy.

Linear and mixed effects regressions using biomarker quartiles as the exposure were selected for the main analysis after model diagnostics were applied to regressions using exposures as continuous variables and substantial violations of key model assumptions were identified.