Table 3:
Stepwise linear regression results for multivariate functional principal component modes in the sagittal (sag), coronal (cor), and transverse (tran) planes, age, gender, and BMI as independent variables and mean T1ρ relaxation times in the femoral cartilage (A), T2 relaxation times in the femoral cartilage (B), T1ρ relaxation times in the acetabular cartilage (C), and T2 relaxation times in the acetabular cartilage (D) as dependent variables.
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Table 3A: Femoral cartilage T1ρ regression model | ||||
|---|---|---|---|---|
| Estimate | SE | tStat | P value | |
|
| ||||
| (Intercept) | 26.982 | 2.038 | 13.243 | < 0.001 |
| tran_PC3 | -0.020 | 0.008 | -2.458 | 0.015 |
| tran_PC5 | 0.034 | 0.017 | 2.016 | 0.046 |
| BMI | 0.400 | 0.083 | 4.814 | < 0.001 |
| Model Summary: | ||||
|
Degrees of freedom: 140, RMSE: 3.258, Adjusted R2: 0.169, F-statistic: 10.670, P-value: < 0.001 | ||||
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Table 3B: Femoral cartilage T2 regression model | ||||
|---|---|---|---|---|
| Estimate | SE | tStat | P value | |
|
| ||||
| (Intercept) | 20.880 | 2.900 | 7.200 | < 0.001 |
| tran_PC2 | 0.026 | 0.009 | 2.831 | 0.005 |
| BMI | 0.495 | 0.118 | 4.182 | < 0.001 |
| Model Summary: | ||||
|
Degrees of freedom: 141, RMSE: 4.657, Adjusted R2: 0.124, F-statistic: 11.090, P-value: < 0.001 | ||||
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Table 3C: Acetabular cartilage T1ρ regression model | ||||
|---|---|---|---|---|
| Estimate | SE | tStat | P value | |
|
| ||||
| (Intercept) | 24.273 | 1.984 | 12.232 | < 0.001 |
| sag_PC1 | 0.008 | 0.003 | 2.516 | 0.013 |
| BMI | 0.506 | 0.081 | 6.244 | < 0.001 |
| Model Summary: | ||||
|
Degrees of freedom: 141, RMSE: 3.222, Adjusted R2: 0.243, F-statistic: 23.910, P-value: < 0.001 | ||||
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Table 3D: Acetabular cartilage T2 regression model | ||||
|---|---|---|---|---|
| Estimate | SE | tStat | P value | |
|
| ||||
| (Intercept) | 18.778 | 2.749 | 6.830 | < 0.001 |
| BMI | 0.549 | 0.112 | 4.895 | < 0.001 |
| Model Summary: | ||||
|
Degrees of freedom: 142, RMSE: 4.475, Adjusted R2: 0.138, F-statistic: 23.960, P-value: < 0.001 | ||||
SE = standard error; tStat = t statistic; RMSE = root mean squared error.