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 adjustment...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2020-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0231670 |
| _version_ | 1818587141766119424 |
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| author | Thomas P Zonneveld Annette Aigner Rolf H H Groenwold Ale Algra Paul J Nederkoorn Ulrike Grittner Nyika D Kruyt Bob Siegerink |
| author_facet | Thomas P Zonneveld Annette Aigner Rolf H H Groenwold Ale Algra Paul J Nederkoorn Ulrike Grittner Nyika D Kruyt Bob Siegerink |
| author_sort | Thomas P Zonneveld |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-16T09:04:09Z |
| format | Article |
| id | doaj.art-dab4359e382c4eaab3bbccde83dc6eec |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-16T09:04:09Z |
| publishDate | 2020-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-dab4359e382c4eaab3bbccde83dc6eec2022-12-21T22:37:07ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-01154e023167010.1371/journal.pone.0231670Confounding adjustment performance of ordinal analysis methods in stroke studies.Thomas P ZonneveldAnnette AignerRolf H H GroenwoldAle AlgraPaul J NederkoornUlrike GrittnerNyika D KruytBob SiegerinkBACKGROUND: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.https://doi.org/10.1371/journal.pone.0231670 |
| spellingShingle | Thomas P Zonneveld Annette Aigner Rolf H H Groenwold Ale Algra Paul J Nederkoorn Ulrike Grittner Nyika D Kruyt Bob Siegerink Confounding adjustment performance of ordinal analysis methods in stroke studies. PLoS ONE |
| 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 |
| url | https://doi.org/10.1371/journal.pone.0231670 |
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