Table 4.

Validity measures of the predictive rule

Measure	Estimates in training sample	Estimates with 1,000 bootstrap resamples
Sensitivity% (95% CI)	82.1 (64.4-92.1)	82.1 (66.7-95.8*)
Specificity% (95% CI)	71.7 (62.8-79.2)	71.7 (62.8-79.8*)
Positive predictive value% (95% CI)	41.8 (29.7-55.0)	41.8 (29.1-55.8*)
Negative predictive value% (95% CI)	94.2 (87.1-97.5)	94.2 (88.8-98.8*)
Positive likelihood ratio (95% CI)	2.90 (2.06-4.08)	2.90 (1.81-4.74*)
Negative likelihood ratio (95% CI)	0.25 (0.11-0.57)	0.25 (0.11-0.58*)
Area under ROC curve (95% CI)	0.77 (0.69-0.85)	0.77 (0.69-0.85*)

• *95% asymptotic confidence intervals.

Sensitivity: number of participants classified at risk both by the PR and the post-operative WOMAC score divided by all participants classified at risk by the post-operative WOMAC score (actual outcome).

Specificity: number of participants classified not at risk by the PR and the post-operative WOMAC score divided by all participants classified not at risk by the post-operative WOMAC score (actual outcome).

Positive predictive value: number of participants classified at risk by the PR and the post-operative WOMAC score divided by all participants classified at risk by the PR (predicted outcome).

Negative predictive value: number of participants classified not at risk by the PR and the post-operative WOMAC score divided by all participants classified not at risk by the PR (predicted outcome).

Positive likelihood ratio: sensitivity/(1-specificity).

Negative likelihood ratio: (1-sensitivity)/specificity.

Area under the ROC curve is defined as the area under the sensitivity vs. 1-specificity curve.