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In the linear regression model, values for CK reached significance on POD1 in the unadjusted (difference, -258; CI, -434 to -83; P < 0.01), as well as in the adjusted model (without adjustment for ACC) (difference, 169; CI, -331 to -8; P = 0.04) (Table 2).
The final model of PFS has a cumulative R2 of 34.3% and the set of genetic factors included in the model without adjustment for age and ISS has a cumulative R2 of 18.4%.
The final model of OS has a cumulative R2 of 46.5% and the set of genetic factors included in this model without adjustment for age and ISS have a cumulative R2 of 25.2%.
Each SES indicator was entered as a continuous variable in each model, without adjustment, and then following forced-entry covariate adjustment for potential confounding variables age, sex, body mass index (BMI), diabetes, smoking status, and alcohol consumption.
We therefore report the results of the model without adjustment for follow-up from now on (table 2).
A generalised linear model (without adjustment for covariates) using robust standard error estimates will be used to carry out this analysis.
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To do so, we run our model without adjustments applied to the VMs resources.
We now compare subgroups of participants to controls to hone in on the effects of the program for those who were most likely to benefit on the basis of more stringent definitions of adoption, using the basic model without adjustments.
First, the contribution of birth weight and CRW on BP was examined by simple regression models (without adjustment) respectively; second, covariates were adjusted in each model; third, we included CRW and birth weight in one model to see their total contributions on BP; finally, the interaction between CRW and birth weight was tested.
In the models without adjustment for city (models 1 3), estimates for PMALL and PMIND were lower.
The results of the models without adjustment for socioeconomic position were not materially different.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com