Input parameter Explanation SEED Random number seed (should be a positive integer) ALEVEL Significance level of the statistical test (Type I error) P Event proportion (response probability) NITER Number of iterations performed N_REPEAT Number of iterations performed *NITER and N_REPEAT should be the same number PATH Directory in which results are saved TABLE Table name for saved results R Number of categorized groups Example: continuous = 1, median = 2, tertile = 3, quantile = 4 *If model includes nominal variables, R should be >1 CHANGE_POINT Change point (see Figure 1) Regression coefficients for the covariates in the full model, except for predictors and intercept, specified as: MODEL_1 %NRSTR(α1*X1+, , + βiXi) MODEL_2 %NRSTR(α1*(the value of change_point) + α2*(X1 − (the value of change_point) +, , + βiXi) α and β are the given regression coefficient values *If model is linear, the regression coefficients α1 and α2 are the same Sample size, mean, standard deviation, skewness, kurtosis, and correlation are specified as: Example DATA a (type=CORR); LENGTH _TYPE_ $40; INPUT _NAME_$_TYPE_\$ X1　X2 ; IF TRIM(LEFT(_TYPE_))=’N’ THEN call symput(‘NSP’, X1); CARDS; . MEAN 70 50 . STD 4 5 . N 300 300 X1 CORR 1 0 X2 CORR 0 1 ; RUN; *If only one covariate is defined, the correlation should be set to 1. The sample size of all covariates should be the same. SKW_KRT %NRSTR ({skewness 1 kurtosis 1, skewness 2 kurtosis 2, }) *If covariates are normally distributed, both skewness and kurtosis are set to 0. LIST_VARNAME %NRSTR (X1, X2, , Xi); list of variable names in A of above dataset MIN Minimum value of a continuous variable MAX Maximum value of a continuous variable SUB_GROUP Number of subgroups CATEGORIZATION %NRSTR (list of covariates to be categorized) CATEGORIZATION_R %NRSTR (list of new covariate names after categorization) CONTI_MODEL %NRSTR (list of covariates in a continuous logistic regression model) *Even if some parameters are not needed, please assign all parameters and specify necessary variables in a logistic regression model.