Silber J H, Rosenbaum P R
Leonard Davis Institute of Health Economics, Department of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia, USA.
Med Care. 1997 Oct;35(10 Suppl):OS77-92. doi: 10.1097/00005650-199710001-00011.
When two outcome measures, such as mortality and complication rates, are intended to measure the same underlying quantity (in this case hospital quality of care), one expects they will be highly correlated. In addition, as data quality improves, one expects the correlation will increase. The authors show that these expectations are, in a significant way, mistaken.
The authors study two outcomes (hospital mortality and complication rates after surgery) using three predictive models that vary in adjustment for severity of illness.
Two hospital rankings, based on each of the two outcomes, are well correlated when not adjusted for severity. However, as clinical data are added to the models, the correlation tends to disappear. The authors explain this based on assumptions regarding the relative size of the partial correlations between mortality, complication rate, and severity covariates.
Before claims of construct validity can be made, investigators must show that correlations between outcomes purporting to measure quality of care are sustained after adequate correction for severity. Most importantly, it should be recognized that inadequately controlled confounding variables may lead to a spurious high correlation between an accepted and a new outcome measure, and a false sense of adequate construct validity.
当两种结局指标，如死亡率和并发症发生率，旨在衡量同一个潜在量（在此例中为医院护理质量）时，人们预期它们会高度相关。此外，随着数据质量的提高，人们预期相关性会增加。作者表明，这些预期在很大程度上是错误的。
作者使用三种对疾病严重程度调整方式不同的预测模型研究了两种结局（医院死亡率和术后并发症发生率）。
在未对严重程度进行调整时，基于这两种结局各自得出的两个医院排名具有良好的相关性。然而，随着临床数据被纳入模型，相关性往往会消失。作者基于关于死亡率、并发症发生率和严重程度协变量之间偏相关相对大小的假设对此进行了解释。
在能够宣称具有结构效度之前，研究者必须表明，在对严重程度进行充分校正之后，旨在衡量护理质量的结局之间的相关性仍然存在。最重要的是，应该认识到，未充分控制的混杂变量可能导致一个被认可的结局指标与一个新的结局指标之间出现虚假的高相关性，以及一种虚假的充分结构效度感。
