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

Kianoush Fathi Vajargah,1 Homayoun Sadeghi-Bazargani,2,3 Robab Mehdizadeh-Esfanjani,4 Daryoush Savadi-Oskouei,4 Mehdi Farhoudi41Department of Statistics, Islamic Azad University, Tehran, North Branch, 2Neuroscience Research Center, Department of Statistics and Epidemiology, Tabriz University of Medical Sciences, Tabriz, Iran; 3Public Health Department, Karolinska Institute, Stockholm, Sweden; 4Neuroscience Research Center, Tabriz University of Medical Sciences, Tabriz, IranAbstract: 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, trans cranial doppler, partial least squares regression, orthogonal projections to latent structures, multicolinearity