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. Author manuscript; available in PMC: 2024 Feb 1.
Published in final edited form as: Parkinsonism Relat Disord. 2022 Dec 22;107:105251. doi: 10.1016/j.parkreldis.2022.105251

Table 2.

Multiple regression analysis of PCA correlates of each PIGD motor features with post-hoc adaptive Holm adjustment for multiple testing (shown in bold font).

Wald (χ2) P-value OR 95% CI
Lower Upper
Imbalance PCA1 9.237 0.002 1.710 1.210 2.416
PCA2 0.182 0.670 1.070 0.785 1.457
PCA3 0.482 0.487 1.110 0.827 1.491
PCA4 0.105 0.746 0.954 0.716 1.271
PCA5 3.756 0.053 0.637 0.404 1.005
Slow walking PCA1 12.275 0.0005 1.678 1.256 2.241
PCA2 2.821 0.093 0.745 0.529 1.050
PCA3 1.124 0.289 1.202 0.855 1.691
PCA4 0.016 0.898 1.020 0.759 1.370
PCA5 0.116 0.734 0.931 0.618 1.403
Falls PCA1 3.315 0.069 1.226 0.985 1.527
PCA2 1.700 0.192 1.218 0.906 1.638
PCA3 0.240 0.624 1.074 0.808 1.426
PCA4 0.633 0.426 1.124 0.843 1.499
PCA5 0.194 0.660 0.921 0.639 1.328
FOG PCA1 10.440 0.001 1.579 1.197 2.082
PCA2 1.459 0.227 0.779 0.519 1.168
PCA3 1.450 0.229 1.314 0.843 2.048
PCA4 0.334 0.563 0.899 0.626 1.291
PCA5 3.286 0.070 0.631 0.383 1.038

PCA: Principal Component Analysis; PCA1: motor performance and self-efficacy of mobility; PCA2: Small sensory nerve fiber; PCA3: Large sensory nerve fiber; PCA4: Deep tendon reflexes; PCA5: Postural and balance control.