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The best model from each region included 3 main effect predictors; 5 out of the 7 possible main effect variables were selected by at east one of the three regional optimizations.
Model 1 includes only main effect predictors; model 2 adds the interaction term between percentage of female authors and percentage of international authors.
An increased utilization rate of benefits (OR = 24.2) was a strong main effect predictors of the high LTC expenditures.
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These observations are consistent with the use of a binary indicator of a past hospitalization from the nursing home both as a main and interaction effect predictor and the log of the number of past hospitalizations (if any) just as a main effect predictor in the time between hospitalization model.
We coded the main-effect predictors by using the Cockerham genetic model.
Main-effect predictors were exercise group (Runners Association/communityy controls), gender, baseline age, baseline body mass index (BMI; kg/m), years of education, number of hospital days in the past year, and dichotomous variables for smoking, and history of arthritis, fractures, and cancer at baseline.
Most reviewed studies examined the main effect of genetic predictors, but some studies investigated whether SNP-by-SNP interaction or SNP-by-phenotype interaction can affect various ADNI phenotypes.
The analyses were conducted in the following way: First, the main effect of each predictor was explored (Model A).
Moreover, there was a significant main effect of the control predictor vowel quality.
Our results demonstrated that an increase in relative utilization rate of insurances benefits as main effect was a strong predictor of the higher LTC expenditures.
The main effects of all predictors were independently explored in base models.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com