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Accordingly, previous caries was required among the baseline variables to predict 2-year caries increment at a reasonable level (R2 = 56.7%, Q2 = 21%, M2).
We used these baseline variables to predict QLACS scores at FU1. to change was examined using change in QLACS domain scores 1-yr.
Hierarchical multiple linear regression was used to assess the ability of baseline variables to predict depression (CDSS total score) at 12-month follow-up.
When using baseline variables to predict outcome (metabolic control history, YSR/YASR scores, functional outcome), CBCL Internalizing and Externalizing scores were included separately from CBCL Total problem scores for the same reason.
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Baseline characteristics were chosen for independent variables to predict factors on PCS, MCS, and the CLDQ via multiple linear regression.
There are too many variable to predict what direction things will take.
Regression models were constructed to determine baseline variables that predict physical function on follow up.
Regression analysis, including logistic regression, will be used to identify baseline variables that predict successful outcomes and adherence.
A methodological strength in the present study is the use of changes over time to analyze the associations of two variables, instead of analyzing the association by using a level of a variable at baseline to predict a level of another variable at follow-up.
Table 6 Univariate and adjusted logistic regression for variables at baseline to predict HAH.
Receiver operating characteristics (ROC) curves were constructed to evaluate the ability of tissue oxygenation variables at baseline to predict an increase of VO2 ≥ 15%% after VE in fluid-responders' group.
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