Department of Neurology, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands.
Institute of Public Health, Charité-Universitätsmedizin Berlin, Berlin, Germany.
PLoS One. 2020 Apr 16;15(4):e0231670. doi: 10.1371/journal.pone.0231670. eCollection 2020.
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.
Our aim is to quantify the performance of OLR and MWS in different confounding variable settings.
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.
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.
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.
在中风研究中，常使用有序逻辑回归（OLR）分析改良 Rankin 量表（mRS）的结果，而非参数优势 Mann-Whitney 检验（MWS）也被提出。在需要进行混杂调整时，尚不清楚这两种方法的表现如何。
本研究旨在量化 OLR 和 MWS 在不同混杂变量设置下的表现。
我们进行了一项模拟研究，设置了三种不同的场景：（1）二分类混杂变量，（2）连续混杂变量，（3）模拟中风后功能结局研究的混杂变量设置。我们比较了调整后的有序逻辑回归（aOLR）和分层 Mann-Whitney 优势检验（sMWS），还使用倾向评分对 MWS 进行分层（psMWS）。为了可比性，将 OLR 估计值转换为 MWS。我们报告偏倚，即产生估计值偏差超过 0.05 点的运行次数百分比（估计值变化）和覆盖率。
在场景 1 中，sMWS 和 aOLR 均无偏倚，估计值变化和覆盖率相似。在场景 2 中，sMWS 导致更大的偏倚（0.04 对 0.00）和更高的估计值变化（41.6% 对 3.3%），但覆盖率相似。在场景 3 中，两种方法均无偏倚，sMWS 的估计值变化更高（6.7% 对 aOLR 的 1.1%），覆盖率为 0.98（sMWS）对 0.95（aOLR）。使用 psMWS，偏倚仍为 0.00，估计值变化更小（1.5%），覆盖率为 0.95。
在我们的中风模拟场景中，两种调整方法的偏倚相似，MWS 的较高估计值变化随着倾向评分分层而改善。仅当分层数与观察数的比例相对较低时，分层 MWS 才是调整后 OLR 的有效替代方法，但倾向评分分层扩展了 MWS 的应用范围。
