Fedeli Ugo, Brocco Stefano, Alba Natalia, Rosato Rosalba, Spolaore Paolo
SER-Epidemiological Department, Veneto Region, Via Ospedale 18, 31033 Castelfranco Veneto (TV), Italy.
J Clin Epidemiol. 2007 Aug;60(8):858-62. doi: 10.1016/j.jclinepi.2006.11.017. Epub 2007 Mar 26.
Many statistical approaches have been applied to compare health care providers' performance, but few studies have examined how the outlier status of providers depends on the choice between risk-adjustment techniques.
We analyzed the recourse to breast-conserving surgery (BCS) for breast carcinoma across 31 hospitals of the Veneto Region (Italy). The following methods were compared: the ratio of observed to expected events (O/E), regression models with provider effects introduced as dummy variables obtained by standard or weighted effect coding, and multilevel analysis.
The O/E method classified seven hospitals (one with high and six with low BCS rates) as outliers. The regression model with the weighted parameterization gave similar results, whereas through standard effect coding all odds ratios shifted and different outliers were identified. Multilevel analysis was quite conservative in identifying small hospitals with BCS rates lower than the regional mean.
Whenever feasible, results obtained through different statistical methodologies should be compared. If providers are modeled as dummy variables obtained by effect coding, departures of the model intercept from the regional mean should be checked. The increasing use of multilevel models could entail a lower sensitivity in identifying low-quality outliers if a volume-outcome relationship exists.
许多统计方法已被用于比较医疗服务提供者的绩效，但很少有研究探讨提供者的异常值状态如何取决于风险调整技术之间的选择。
我们分析了意大利威尼托地区31家医院对乳腺癌采用保乳手术（BCS）的情况。比较了以下方法：观察事件与预期事件的比率（O/E）、将提供者效应作为通过标准或加权效应编码获得的虚拟变量引入的回归模型，以及多水平分析。
O/E方法将7家医院（1家BCS率高，6家BCS率低）归类为异常值。采用加权参数化的回归模型给出了类似的结果，而通过标准效应编码，所有比值比都发生了变化，并识别出了不同的异常值。多水平分析在识别BCS率低于区域平均水平的小医院时相当保守。
只要可行，就应比较通过不同统计方法获得的结果。如果将提供者建模为通过效应编码获得的虚拟变量，则应检查模型截距与区域平均值的偏差。如果存在量-效关系，多水平模型的使用增加可能会导致识别低质量异常值的敏感性降低。
