Hayward Rodney A, Heisler Michele, Adams John, Dudley R Adams, Hofer Timothy P
Department of Veterans Affairs, VA Center for Practice Management & Outcomes Research, VA Ann Arbor Healthcare System, Ann Arbor, MI 48113-0170, USA.
Health Serv Res. 2007 Aug;42(4):1718-38. doi: 10.1111/j.1475-6773.2006.00661.x.
To demonstrate how failure to account for measurement error in an outcome (dependent) variable can lead to significant estimation errors and to illustrate ways to recognize and avoid these errors.
Medical literature and simulation models.
STUDY DESIGN/DATA COLLECTION: Systematic review of the published and unpublished epidemiological literature on the rate of preventable hospital deaths and statistical simulation of potential estimation errors based on data from these studies.
Most estimates of the rate of preventable deaths in U.S. hospitals rely upon classifying cases using one to three physician reviewers (implicit review). Because this method has low to moderate reliability, estimates based on statistical methods that do not account for error in the measurement of a "preventable death" can result in significant overestimation. For example, relying on a majority rule rating with three reviewers per case (reliability approximately 0.45 for the average of three reviewers) can result in a 50-100 percent overestimation compared with an estimate based upon a reliably measured outcome (e.g., by using 50 reviewers per case). However, there are statistical methods that account for measurement error that can produce much more accurate estimates of outcome rates without requiring a large number of measurements per case.
The statistical principles discussed in this case study are critically important whenever one seeks to estimate the proportion of cases belonging to specific categories (such as estimating how many patients have inadequate blood pressure control or identifying high-cost or low-quality physicians). When the true outcome rate is low (<20 percent), using an outcome measure that has low-to-moderate reliability will generally result in substantially overestimating the proportion of the population having the outcome unless statistical methods that adjust for measurement error are used.
证明在结果（因变量）变量中未考虑测量误差如何导致显著的估计误差，并说明识别和避免这些误差的方法。
医学文献和模拟模型。
研究设计/数据收集：对已发表和未发表的关于可预防医院死亡发生率的流行病学文献进行系统综述，并基于这些研究的数据对潜在估计误差进行统计模拟。
美国医院可预防死亡发生率的大多数估计依赖于由一至三名医生评审员对病例进行分类（隐性评审）。由于这种方法的可靠性较低至中等，基于未考虑“可预防死亡”测量误差的统计方法得出的估计可能会导致显著高估。例如，依靠对每个病例由三名评审员进行多数规则评级（三名评审员平均值的可靠性约为0.45），与基于可靠测量结果得出的估计（例如，每个病例使用50名评审员）相比，可能会导致高估50%-100%。然而，有一些考虑测量误差的统计方法可以在不需要每个病例进行大量测量的情况下，得出更准确的结果发生率估计值。
每当试图估计属于特定类别的病例比例时（例如估计有多少患者血压控制不佳，或识别高成本或低质量的医生），本案例研究中讨论的统计原则都至关重要。当真实结果发生率较低（<20%）时，使用可靠性较低至中等的结果测量方法通常会导致对有该结果的人群比例的大幅高估，除非使用调整测量误差的统计方法。
