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Association between a metric outcome variable and a binary predictor variable was assessed using the Mann–Whitney U test.
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The predictor variable was a binary variable corresponding to randomization into the treatment or control arm.
We consider here the prediction setting where each predictor variable is binary.
Under both methods, a binary predictor variable will be used to indicate intervention versus control status and period of study, gender and age will be adjusted for.
In the first stage of the modelling process, the relationship between the EQ-5D index (as a binary response variable) with observed predictor variables was determined using a mixed effects logistic model.
For our purposes, ethnic/racial data were re-aggregated a priori into a binary predictor variable of Caucasian or non-Caucasian.
Predictor variables were transformed to binary categories representing whether or not refeeding syndrome had been diagnosed.
All other behaviors described in Table 1 (non-modification for the first, non-use for the second) were grouped into a single "other" category, creating a binary predictor variable and a binary response variable.
A binary logistic regression model was also used, since the predictor variables were a combination of continuous and categorical variables and not normally distributed.
Correlations between K10 and potential predictor variables were examined using Spearman's rho correlation coefficients (rs) and univariate binary logistic regression analyses were used to determine the potential predictors for QOL.
Predictor variables were backwards-eliminated at P<0.25.
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