A New Criterion for Determining a Cutoff Value Based on the Biases of Incidence Proportions in the Presence of Non-differential Outcome Misclassifications.

IF 4.7 2区 医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Epidemiology Pub Date : 2024-09-01 Epub Date: 2024-07-05 DOI:10.1097/EDE.0000000000001756
Norihiro Suzuki, Masataka Taguri
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Abstract

When conducting database studies, researchers sometimes use an algorithm known as "case definition," "outcome definition," or "computable phenotype" to identify the outcome of interest. Generally, algorithms are created by combining multiple variables and codes, and we need to select the most appropriate one to apply to the database study. Validation studies compare algorithms with the gold standard and calculate indicators such as sensitivity and specificity to assess their validities. As the indicators are calculated for each algorithm, selecting an algorithm is equivalent to choosing a pair of sensitivity and specificity. Therefore, receiver operating characteristic curves can be utilized, and two intuitive criteria are commonly used. However, neither was conceived to reduce the biases of effect measures (e.g., risk difference and risk ratio), which are important in database studies. In this study, we evaluated two existing criteria from perspectives of the biases and found that one of them, called the Youden index always minimizes the bias of the risk difference regardless of the true incidence proportions under nondifferential outcome misclassifications. However, both criteria may lead to inaccurate estimates of absolute risks, and such property is undesirable in decision-making. Therefore, we propose a new criterion based on minimizing the sum of the squared biases of absolute risks to estimate them more accurately. Subsequently, we apply all criteria to the data from the actual validation study on postsurgical infections and present the results of a sensitivity analysis to examine the robustness of the assumption our proposed criterion requires.

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根据非差异结果误分类情况下发病比例的偏差确定临界值的新标准。
在进行数据库研究时,研究人员有时会使用一种称为 "病例定义"、"结果定义 "或 "可计算表型 "的算法来确定感兴趣的结果。一般来说,算法是由多个变量和代码组合而成的,我们需要选择最合适的算法应用于数据库研究。验证研究将算法与金标准进行比较,并计算灵敏度和特异性等指标,以评估其有效性。由于这些指标是针对每种算法计算的,因此选择一种算法就相当于选择一对灵敏度和特异度。因此,可以利用接收者操作特征曲线(ROC),通常使用两种直观标准。然而,这两种标准都不是为了减少效应测量(如风险差异、风险比)的偏差而设计的,而效应测量在数据库研究中非常重要。在本研究中,我们从偏差的角度对现有的两个标准进行了评估,发现其中一个称为尤登指数(Youden index)的标准总能最大限度地减少风险差异的偏差,而不管在非差异结果误分类的情况下真实的发病比例如何。然而,这两种标准都可能导致对绝对风险的估计不准确,而这种特性在决策中是不可取的。因此,我们提出了一种新的标准,即最小化绝对风险的平方偏差之和,以更准确地估计绝对风险。随后,我们将所有标准应用于手术后感染的实际验证研究数据,并展示了敏感性分析的结果,以检验我们提出的标准所要求的假设的稳健性。
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来源期刊
Epidemiology
Epidemiology 医学-公共卫生、环境卫生与职业卫生
CiteScore
6.70
自引率
3.70%
发文量
177
审稿时长
6-12 weeks
期刊介绍: Epidemiology publishes original research from all fields of epidemiology. The journal also welcomes review articles and meta-analyses, novel hypotheses, descriptions and applications of new methods, and discussions of research theory or public health policy. We give special consideration to papers from developing countries.
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