Table 3.
Generalized PAT-based model | Complex individualized models | p value for comparison | |
---|---|---|---|
Systolic blood pressure | |||
Mean error, mmHg | −0.2 | −1.4 | |
Mean absolute error (SD), mmHg | 7.6 (5.3) | 6.5 (4.8) | <0.001* |
SD of errors, mmHg | 7.2 | 6.7 | <0.001** |
Median of absolute errors (IQR), mmHg | 5.3 (4.5–10.7) | 5.8 (4.7–7.3) | |
Repeated measures correlation coefficient | 0.23 | 0.39 | |
Correlation coefficient, all subjects pooled | 0.91 | 0.94 | |
Linear regression of aggregated data between model and reference***, R2 | 0.91 | 0.96 | |
Akaike’s information criterion*** | 173 | 154 | |
Bayesian information criterion*** | 175 | 156 | |
Diebold-Mariano comparison of predictive accuracy | Individualized model is significantly better | 0.001 | |
Diastolic blood pressure | |||
Mean error, mmHg | 0.2 | 0.0 | |
Mean absolute error, mean (SD), mmHg | 3.3 (3.3) | 3.1 (2.2) | <0.001* |
SD of errors, mmHg | −3.1 | 3.0 | 0.56** |
Median of absolute errors (IQR), mmHg | 2.7 (1.8–4.1) | 2.2 (1.7–3.5) | |
Repeated measures correlation coefficient | 0.29 | 0.33 | |
Correlation coefficient, all subjects pooled. | 0.94 | 0.94 | |
Linear regression of aggregated data between model and reference***, R2 | 0.94 | 0.94 | |
Akaike’s information criterion*** | 131 | 130 | |
Bayesian information criterion*** | 134 | 133 | |
Diebold-Mariano comparison of predictive accuracy | Individualized model is non-significantly better | 0.14 | |
Mean arterial pressure | |||
Mean error, mmHg | 0.1 | −0.1 | |
Mean absolute error, mean (SD), mmHg | 4.6 (3.2) | 4.0 (2.9) | <0.001* |
SD of errors, mmHg | 4.4 | 4.0 | <0.001** |
Median of absolute errors (IQR), mmHg | 3.3 (2.4–6.4) | 3.3 (2.5–4.5) | |
Repeated measures correlation coefficient | 0.25 | 0.37 | |
Correlation coefficient, all subjects pooled. | 0.93 | 0.95 | |
Linear regression of aggregated data between model and reference***, R2 | 0.93 | 0.95 | |
Akaike’s information criterion*** | 146 | 138 | |
Bayesian information criterion*** | 149 | 140 | |
Diebold-Mariano comparison of predictive accuracy | Individualized model is significantly better | 0.006 |
*Compared using non-parametric test of difference in means of all absolute errors between the two models. **Compared using variance comparison test of equality of standard deviations. ***Means of predicted BP from each model for each subject fitted in a linear regression model against reference BP.