Serum protein changes over time
For each analyte, a mixed-effect model with year, gender, and race as
fixed covariates was applied to the log2-transformed analyte
concentration. Comparisons of each analyte at each time point (Y1 to Y2,
Y1 to Y5, and Y2 to Y5) were made and significance assessed using the
Tukey Multiple Comparison Test. Volcano plots were generated and used to
identify changes from Y5 to Y1 and from Y2 to Y1 (p -values
<0.05 and fold change >1.5X or <-1.5X).
Prediction model of SCORAD change
The prediction model of the change in SCORAD at Y5 to Y1 with all the
baseline (Y1) analytes concentrations was built using JMP [13]. With
forward selection, starting with null model and add the analyte most
predicted the most on dependent measure (e.g. smallest p -values)
one by one until including analyte predictors provided no additional
predictive power. The stopping criteria is based on minimum corrected
Akaike Information Criterion (AIC) [14]. To evaluate the performance
of prediction model, we calculate the R squares and RMSE (Root Mean
Square Error) between predicted values and observed values for all
patients and RMSE. Furthermore, the cross validation on the proposed
prediction model with selected markers was applied with 100 iterations
[15]. At each iteration, the entire baseline patient set was split
into training set (75%) and testing test (25%). The training set data
was used to build the model and estimate model parameters. The model was
then used to predict the testing set data. The R squares and RMSE
between predicted values and observed values were calculated for each
iteration to assess the robustness of prediction model.