Confounding adjustment performance of ordinal analysis methods in stroke studies

PLoS ONE, vol. 15, pp. e0231670

Abstract

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.

Authors 8

1. Thomas P. Zonneveld

   Amsterdam University Medical Centers · University of Amsterdam

   Affiliation as printed

   Department of Neurology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands

2. Annette Aigner

   Charité - Universitätsmedizin Berlin

   Affiliation as printed

   Institute of Public Health, Charité-Universitätsmedizin Berlin, Berlin, Germany

3. Rolf H. H. Groenwold Aachen Department of Biomedical Data Sciences Department of Clinical Epidemiology

   Leiden University · Leiden University Medical Center

   Affiliation as printed

   Department of Biomedical Data Sciences, Leiden University Medical Centre, Leiden, The Netherlands

   Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, The Netherlands

4. Ale Algra

   Utrecht University · University Medical Center Utrecht

   Affiliation as printed

   Department of Neurology and Neurosurgery, Brain Center Rudolf Magnus, University Medical Center Utrecht, Utrecht, The Netherlands

   Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands

5. Paul J. Nederkoorn

   Amsterdam University Medical Centers · University of Amsterdam

   Affiliation as printed

   Department of Neurology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands

6. Ulrike Grittner

   Berlin Institute of Health at Charité - Universitätsmedizin Berlin · Center for Stroke Research Berlin · Charité - Universitätsmedizin Berlin

   Affiliation as printed

   Berlin Institute of Health, Charité-Universitätsmedizin Berlin, Berlin, Germany

   Center for Stroke research Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany

7. Nyika D. Kruyt Aachen Department of Neurology and Clinical Neuropsychology

   Leiden University Medical Center

   Affiliation as printed

   Department of Neurology and Clinical Neuropsychology, Leiden University Medical Center, Leiden, The Netherlands

8. Bob Siegerink corresponding Aachen Department of Clinical Epidemiology

   Leiden University · Leiden University Medical Center · Center for Stroke Research Berlin · Charité - Universitätsmedizin Berlin

   Affiliation as printed

   Center for Stroke research Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany

   Department of Clinical Epidemiology, Leiden University Medical Centre, Leiden, The Netherlands