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. 2012:8:387-92.
doi: 10.2147/NDT.S33991. Epub 2012 Aug 30.

OPLS statistical model versus linear regression to assess sonographic predictors of stroke prognosis

Kianoush Fathi Vajargah  1 , Homayoun Sadeghi-Bazargani, Robab Mehdizadeh-Esfanjani, Daryoush Savadi-Oskouei, Mehdi Farhoudi
Affiliations

OPLS statistical model versus linear regression to assess sonographic predictors of stroke prognosis

Kianoush Fathi Vajargah et al. Neuropsychiatr Dis Treat. 2012.

Abstract

The objective of the present study was to assess the comparable applicability of orthogonal projections to latent structures (OPLS) statistical model vs traditional linear regression in order to investigate the role of trans cranial doppler (TCD) sonography in predicting ischemic stroke prognosis. The study was conducted on 116 ischemic stroke patients admitted to a specialty neurology ward. The Unified Neurological Stroke Scale was used once for clinical evaluation on the first week of admission and again six months later. All data was primarily analyzed using simple linear regression and later considered for multivariate analysis using PLS/OPLS models through the SIMCA P+12 statistical software package. The linear regression analysis results used for the identification of TCD predictors of stroke prognosis were confirmed through the OPLS modeling technique. Moreover, in comparison to linear regression, the OPLS model appeared to have higher sensitivity in detecting the predictors of ischemic stroke prognosis and detected several more predictors. Applying the OPLS model made it possible to use both single TCD measures/indicators and arbitrarily dichotomized measures of TCD single vessel involvement as well as the overall TCD result. In conclusion, the authors recommend PLS/OPLS methods as complementary rather than alternative to the available classical regression models such as linear regression.

Keywords: Prognostic study; multicolinearity; orthogonal projections to latent structures; partial least squares regression; trans cranial doppler.

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Figures

Figure 1
Figure 1
The normal probability plot of residuals. Note: The normal probability plot of residuals of the regression model run using orthogonal projections to latent structure to investigate predictors of stroke prognosis.
Figure 2
Figure 2
Plot of coefficients of the OPLS regression model. Notes: Bars indicate the confidence intervals of the coefficients. The coefficient is significant when the confidence interval does not cross zero. The plot of coefficients of the OPLS regression model run to detect the predictors of stroke prognosis including TCD finding. Abbreviations: OPLS, orthogonal projections to latent structures; TCD, transcranial doppler.
Figure 3
Figure 3
The loadings plot of the OPLS model. Note: The loadings plot of the OPLS model to assess the predictors of 6th month UNSS score as a surrogate of stroke prognosis. Abbreviations: OPLS, orthogonal projections to latent structures; UNSS, Unified Neurological Stroke Scale.
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