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

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

Independent variables^a	Coefficient (β)	Standard error	Wald χ²	Pvalue	Odds ratio^b	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						0.752^c
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						0.771^c
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)

^a All variables are defined as in Table 2.
^b Values in parentheses show 95% confidence intervals. For the models with more than one independent variable, adjusted odds ratios are shown.
^c This ROC area applies to the full multivariate model.

ISSN: 1471-2288