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A final model was achieved by entering the variables retained in the backward selection model.
The multivariate analyses were performed by entering the variables that had a p-value of < 0.1 with subsequent removal of the least significant variables in a stepwise fashion.
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Tests for trends were performed by treating each categorized variable as a continuous term and by entering the variable into the fully adjusted linear regression model.
Tests for trends were performed by entering the categorical variables as continuous variables in the model.
Tests for trend across exposure categories were performed by entering the categorical variables as a continuous variable in the model.
Tests for trend were performed by entering the categorical variables as continuous parameters in the models.
The result was compared with a backward logistic regression model by entering the same variables as the above model, and this led to a similar result.
Moderating effects of age, gender, and treatment setting on the relation between depression and anxiety will be explored by entering the moderating variables as predictors for depression/anxiety.
This was accomplished by entering the case variable (diagnosed FM versus matched control), number of sleep symptoms (none, one, and two or more), and the interaction term into a linear regression model (PROC SURVEYREG) for each summary and domain score.
Finally, spatial lag regression analyses were conducted by entering the significant socioeconomic variables from the linear regression models.
The third condition was evaluated by entering the potential mediating variable as one of the independent variables in the multivariable logistic regression models used to examine the relationships between collectivization and study outcomes.
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