Centre for Medical Decision Making, Department of Public Health, Erasmus MC - University Medical Center, PO Box 2040, 3000, CA, Rotterdam, The Netherlands.
Department of Neurology, Stroke Center, Erasmus MC - University Medical Center, Rotterdam, The Netherlands.
BMC Med Res Methodol. 2021 Jan 6;21(1):4. doi: 10.1186/s12874-020-01185-7.
There is a growing interest in assessment of the quality of hospital care, based on outcome measures. Many quality of care comparisons rely on binary outcomes, for example mortality rates. Due to low numbers, the observed differences in outcome are partly subject to chance. We aimed to quantify the gain in efficiency by ordinal instead of binary outcome analyses for hospital comparisons. We analyzed patients with traumatic brain injury (TBI) and stroke as examples.
We sampled patients from two trials. We simulated ordinal and dichotomous outcomes based on the modified Rankin Scale (stroke) and Glasgow Outcome Scale (TBI) in scenarios with and without true differences between hospitals in outcome. The potential efficiency gain of ordinal outcomes, analyzed with ordinal logistic regression, compared to dichotomous outcomes, analyzed with binary logistic regression was expressed as the possible reduction in sample size while keeping the same statistical power to detect outliers.
In the IMPACT study (9578 patients in 265 hospitals, mean number of patients per hospital = 36), the analysis of the ordinal scale rather than the dichotomized scale ('unfavorable outcome'), allowed for up to 32% less patients in the analysis without a loss of power. In the PRACTISE trial (1657 patients in 12 hospitals, mean number of patients per hospital = 138), ordinal analysis allowed for 13% less patients. Compared to mortality, ordinal outcome analyses allowed for up to 37 to 63% less patients.
Ordinal analyses provide the statistical power of substantially larger studies which have been analyzed with dichotomization of endpoints. We advise to exploit ordinal outcome measures for hospital comparisons, in order to increase efficiency in quality of care measurements.
We do not report the results of a health care intervention.
基于结果指标，人们对医院护理质量评估的兴趣日益浓厚。许多护理质量比较依赖于二分类结果，例如死亡率。由于数量较少，观察到的结果差异部分受到机会的影响。我们旨在通过对医院比较进行有序而不是二分结果分析来量化效率的提高。我们以创伤性脑损伤（TBI）和中风患者为例进行分析。
我们从两项试验中抽取患者。我们模拟了有序和二分结果，基于改良Rankin 量表（中风）和格拉斯哥结局量表（TBI），模拟了医院之间在结局方面存在和不存在真实差异的情况。有序逻辑回归分析有序结果的潜在效率增益，与二项逻辑回归分析二分结果进行比较，表达为在保持相同检测异常值的统计功效的情况下，减少样本量的可能性。
在 IMPACT 研究（265 家医院的 9578 名患者，每家医院平均患者数为 36）中，与将结果二分化（“不良结局”）的分析相比，对有序量表的分析允许在不降低功效的情况下，分析中减少多达 32%的患者。在 PRACTISE 试验（12 家医院的 1657 名患者，每家医院平均患者数为 138）中，有序分析允许减少 13%的患者。与死亡率相比，有序结果分析允许减少多达 37%至 63%的患者。
有序分析提供了通过二分化终点进行分析的更大规模研究的统计功效。我们建议利用有序结果测量来进行医院比较，以提高护理质量测量的效率。
我们未报告医疗干预的结果。
