Table 4.

Final multivariate post-treatment pain-prediction models and performance metrics^a

	Responders (N = 249/93)b			Future pain intensity (N = 262/93)b
Independent variables	OR	(95 % CI)	P-value		β	(95 % CI)	P-value
Dose (per 6 spinal manipulation visits)	1.14	(0.95, 1.37)	0.150		−0.07	(−1.35, 1.21)	0.910
Time (in weeks)	1.08	(1.02, 1.14)	0.004
Pain/Disability
Pain intensity	0.64	(0.51, 0.80)	<0.001		10.7	(8.84, 12.56)	<0.001
Days with pain (last 4 weeks)	0.57	(0.46, 0.70)	<0.001
Objective Physical Exam
LBP: right – left lateral bending	0.76	(0.63, 0.92)	0.005
LBP: right lateral bending					2.95	(1.21, 4.69)	0.001
Performance metricsc	AUC	(95 % CI)		RMSE	(95 % CI)	R2	(95 % CI)
Training set	0.750			16.3		.366
Test set	0.665	(0.58, 0.74)		17.5	(15.0, 20.1)	.261	(7.5, 43.2)

OR Odds ratio, PC part correlation, β regression coefficient, ROM range of motion, AUC Area under the curve (receiver operating characteristic curve), RMSE root mean squared error (SD of prediction error), R ² coefficient of determination, LBP low back pain

^aVariables were selected into the regression models using forward selection among variables with p < .05 in the univariate analysis; dose was forced into the models. Independent variables were standardized except for dose (scale unit = 6 visits) and time (scale unit = 1 week). Lower scores were favorable for pain and days with pain

^bThe first number is the sample size for the model in the training set and the second number is the N for the test set

^cChance performance is indicated by 0.5 for AUC. RMSE is the standard deviation of the error in prediction of future pain intensity evaluated on the 0 – 100 pain scale. R ² is the proportion of the variance in pain intensity explained by the independent variables in the model. Confidence intervals are given for the test set only