Cargando…
OPLS statistical model versus linear regression to assess sonographic predictors of stroke prognosis
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. T...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove Medical Press
2012
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3433323/ https://www.ncbi.nlm.nih.gov/pubmed/22973104 http://dx.doi.org/10.2147/NDT.S33991 |
_version_ | 1782242297044271104 |
---|---|
author | Vajargah, Kianoush Fathi Sadeghi-Bazargani, Homayoun Mehdizadeh-Esfanjani, Robab Savadi-Oskouei, Daryoush Farhoudi, Mehdi |
author_facet | Vajargah, Kianoush Fathi Sadeghi-Bazargani, Homayoun Mehdizadeh-Esfanjani, Robab Savadi-Oskouei, Daryoush Farhoudi, Mehdi |
author_sort | Vajargah, Kianoush Fathi |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-3433323 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Dove Medical Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-34333232012-09-12 OPLS statistical model versus linear regression to assess sonographic predictors of stroke prognosis Vajargah, Kianoush Fathi Sadeghi-Bazargani, Homayoun Mehdizadeh-Esfanjani, Robab Savadi-Oskouei, Daryoush Farhoudi, Mehdi Neuropsychiatr Dis Treat Original Research 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. Dove Medical Press 2012 2012-08-30 /pmc/articles/PMC3433323/ /pubmed/22973104 http://dx.doi.org/10.2147/NDT.S33991 Text en © 2012 Fathi Vajargah et al, publisher and licensee Dove Medical Press Ltd. This is an Open Access article which permits unrestricted noncommercial use, provided the original work is properly cited. |
spellingShingle | Original Research Vajargah, Kianoush Fathi Sadeghi-Bazargani, Homayoun Mehdizadeh-Esfanjani, Robab Savadi-Oskouei, Daryoush Farhoudi, Mehdi OPLS statistical model versus linear regression to assess sonographic predictors of stroke prognosis |
title | OPLS statistical model versus linear regression to assess sonographic predictors of stroke prognosis |
title_full | OPLS statistical model versus linear regression to assess sonographic predictors of stroke prognosis |
title_fullStr | OPLS statistical model versus linear regression to assess sonographic predictors of stroke prognosis |
title_full_unstemmed | OPLS statistical model versus linear regression to assess sonographic predictors of stroke prognosis |
title_short | OPLS statistical model versus linear regression to assess sonographic predictors of stroke prognosis |
title_sort | opls statistical model versus linear regression to assess sonographic predictors of stroke prognosis |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3433323/ https://www.ncbi.nlm.nih.gov/pubmed/22973104 http://dx.doi.org/10.2147/NDT.S33991 |
work_keys_str_mv | AT vajargahkianoushfathi oplsstatisticalmodelversuslinearregressiontoassesssonographicpredictorsofstrokeprognosis AT sadeghibazarganihomayoun oplsstatisticalmodelversuslinearregressiontoassesssonographicpredictorsofstrokeprognosis AT mehdizadehesfanjanirobab oplsstatisticalmodelversuslinearregressiontoassesssonographicpredictorsofstrokeprognosis AT savadioskoueidaryoush oplsstatisticalmodelversuslinearregressiontoassesssonographicpredictorsofstrokeprognosis AT farhoudimehdi oplsstatisticalmodelversuslinearregressiontoassesssonographicpredictorsofstrokeprognosis |