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Table 3 Simple and multiple logistic-regression models (dependent variable: complete loss to follow-up)

From: Predicting complete loss to follow-up after a health-education program: number of absences and face-to-face contact with a researcher

Independent variablesa Coefficient (β) Standard error Wald χ2 Pvalue Odds ratiob ROC curve area
Four models, each with one independent variable
Intercept -2.91 0.25 - - -  
Number of absences 0.58 0.09 41.54 < 0.001 1.78 (1.49-2.12) 0.723
Intercept -1.68 0.19 - - -  
Contact -0.67 0.31 4.58 0.032 0.51 (0.28-0.95) 0.582
Intercept -0.65 0.52 - - -  
Age -0.03 0.01 6.59 0.010 0.97 (0.95-0.99) 0.623
Intercept -3.06 0.59 - - -  
Connective tissue disease -1.22 0.61 4.01 0.045 0.29 (0.09-0.98) 0.559
One model with three independent variables       0.752c
Intercept -1.60 0.61 - - -  
Number of absences 0.55 0.09 35.58 < 0.001 1.73 (1.44-2.07)  
Contact -0.59 0.34 3.02 0.083 0.56 (0.29-1.08)  
Age -0.02 0.01 3.49 0.062 0.98 (0.96-1.00)  
One model with four independent variables       0.771c
Intercept -1.31 0.63 - - -  
Number of absences 0.54 0.09 33.42 < 0.001 1.72 (1.43-2.06)  
Contact -0.73 0.34 4.53 0.033 0.48 (0.25-0.94)  
Age -0.02 0.01 3.81 0.051 0.98 (0.95-1.00)  
Connective tissue disease -1.40 0.64 4.73 0.030 0.25 (0.07-0.87)  
  1. a All variables are defined as in Table 2.
  2. b Values in parentheses show 95% confidence intervals. For the models with more than one independent variable, adjusted odds ratios are shown.
  3. c This ROC area applies to the full multivariate model.