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Confounding adjustment performance of ordinal analysis methods in stroke studies

BACKGROUND: In stroke studies, ordinal logistic regression (OLR) is often used to analyze outcome on the modified Rankin Scale (mRS), whereas the non-parametric Mann-Whitney measure of superiority (MWS) has also been suggested. It is unclear how these perform comparatively when confounding adjustmen...

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Autores principales: Zonneveld, Thomas P., Aigner, Annette, Groenwold, Rolf H. H., Algra, Ale, Nederkoorn, Paul J., Grittner, Ulrike, Kruyt, Nyika D., Siegerink, Bob
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7162480/
https://www.ncbi.nlm.nih.gov/pubmed/32298347
http://dx.doi.org/10.1371/journal.pone.0231670
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author Zonneveld, Thomas P.
Aigner, Annette
Groenwold, Rolf H. H.
Algra, Ale
Nederkoorn, Paul J.
Grittner, Ulrike
Kruyt, Nyika D.
Siegerink, Bob
author_facet Zonneveld, Thomas P.
Aigner, Annette
Groenwold, Rolf H. H.
Algra, Ale
Nederkoorn, Paul J.
Grittner, Ulrike
Kruyt, Nyika D.
Siegerink, Bob
author_sort Zonneveld, Thomas P.
collection PubMed
description BACKGROUND: In stroke studies, ordinal logistic regression (OLR) is often used to analyze outcome on the modified Rankin Scale (mRS), whereas the non-parametric Mann-Whitney measure of superiority (MWS) has also been suggested. It is unclear how these perform comparatively when confounding adjustment is warranted. AIMS: Our aim is to quantify the performance of OLR and MWS in different confounding variable settings. METHODS: We set up a simulation study with three different scenarios; (1) dichotomous confounding variables, (2) continuous confounding variables, and (3) confounding variable settings mimicking a study on functional outcome after stroke. We compared adjusted ordinal logistic regression (aOLR) and stratified Mann-Whitney measure of superiority (sMWS), and also used propensity scores to stratify the MWS (psMWS). For comparability, OLR estimates were transformed to a MWS. We report bias, the percentage of runs that produced a point estimate deviating by more than 0.05 points (point estimate variation), and the coverage probability. RESULTS: In scenario 1, there was no bias in both sMWS and aOLR, with similar point estimate variation and coverage probabilities. In scenario 2, sMWS resulted in more bias (0.04 versus 0.00), and higher point estimate variation (41.6% versus 3.3%), whereas coverage probabilities were similar. In scenario 3, there was no bias in both methods, point estimate variation was higher in the sMWS (6.7%) versus aOLR (1.1%), and coverage probabilities were 0.98 (sMWS) versus 0.95 (aOLR). With psMWS, bias remained 0.00, with less point estimate variation (1.5%) and a coverage probability of 0.95. CONCLUSIONS: The bias of both adjustment methods was similar in our stroke simulation scenario, and the higher point estimate variation in the MWS improved with propensity score based stratification. The stratified MWS is a valid alternative for adjusted OLR only when the ratio of number of strata versus number of observations is relatively low, but propensity score based stratification extends the application range of the MWS.
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spelling pubmed-71624802020-04-21 Confounding adjustment performance of ordinal analysis methods in stroke studies Zonneveld, Thomas P. Aigner, Annette Groenwold, Rolf H. H. Algra, Ale Nederkoorn, Paul J. Grittner, Ulrike Kruyt, Nyika D. Siegerink, Bob PLoS One Research Article BACKGROUND: In stroke studies, ordinal logistic regression (OLR) is often used to analyze outcome on the modified Rankin Scale (mRS), whereas the non-parametric Mann-Whitney measure of superiority (MWS) has also been suggested. It is unclear how these perform comparatively when confounding adjustment is warranted. AIMS: Our aim is to quantify the performance of OLR and MWS in different confounding variable settings. METHODS: We set up a simulation study with three different scenarios; (1) dichotomous confounding variables, (2) continuous confounding variables, and (3) confounding variable settings mimicking a study on functional outcome after stroke. We compared adjusted ordinal logistic regression (aOLR) and stratified Mann-Whitney measure of superiority (sMWS), and also used propensity scores to stratify the MWS (psMWS). For comparability, OLR estimates were transformed to a MWS. We report bias, the percentage of runs that produced a point estimate deviating by more than 0.05 points (point estimate variation), and the coverage probability. RESULTS: In scenario 1, there was no bias in both sMWS and aOLR, with similar point estimate variation and coverage probabilities. In scenario 2, sMWS resulted in more bias (0.04 versus 0.00), and higher point estimate variation (41.6% versus 3.3%), whereas coverage probabilities were similar. In scenario 3, there was no bias in both methods, point estimate variation was higher in the sMWS (6.7%) versus aOLR (1.1%), and coverage probabilities were 0.98 (sMWS) versus 0.95 (aOLR). With psMWS, bias remained 0.00, with less point estimate variation (1.5%) and a coverage probability of 0.95. CONCLUSIONS: The bias of both adjustment methods was similar in our stroke simulation scenario, and the higher point estimate variation in the MWS improved with propensity score based stratification. The stratified MWS is a valid alternative for adjusted OLR only when the ratio of number of strata versus number of observations is relatively low, but propensity score based stratification extends the application range of the MWS. Public Library of Science 2020-04-16 /pmc/articles/PMC7162480/ /pubmed/32298347 http://dx.doi.org/10.1371/journal.pone.0231670 Text en © 2020 Zonneveld et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Zonneveld, Thomas P.
Aigner, Annette
Groenwold, Rolf H. H.
Algra, Ale
Nederkoorn, Paul J.
Grittner, Ulrike
Kruyt, Nyika D.
Siegerink, Bob
Confounding adjustment performance of ordinal analysis methods in stroke studies
title Confounding adjustment performance of ordinal analysis methods in stroke studies
title_full Confounding adjustment performance of ordinal analysis methods in stroke studies
title_fullStr Confounding adjustment performance of ordinal analysis methods in stroke studies
title_full_unstemmed Confounding adjustment performance of ordinal analysis methods in stroke studies
title_short Confounding adjustment performance of ordinal analysis methods in stroke studies
title_sort confounding adjustment performance of ordinal analysis methods in stroke studies
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7162480/
https://www.ncbi.nlm.nih.gov/pubmed/32298347
http://dx.doi.org/10.1371/journal.pone.0231670
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