Department of Medicine and Epidemiology and Community Medicine, Ottawa Hospital Research Institute and Institute for Clinical Evaluative Sciences, University of Ottawa, Ottawa, ON, Canada.
Med Care. 2018 Jul;56(7):e39-e45. doi: 10.1097/MLR.0000000000000787.
Diagnostic codes used in administrative database research can create bias due to misclassification. Quantitative bias analysis (QBA) can correct for this bias, requires only code sensitivity and specificity, but may return invalid results. Bootstrap imputation (BI) can also address misclassification bias but traditionally requires multivariate models to accurately estimate disease probability. This study compared misclassification bias correction using QBA and BI.
Serum creatinine measures were used to determine severe renal failure status in 100,000 hospitalized patients. Prevalence of severe renal failure in 86 patient strata and its association with 43 covariates was determined and compared with results in which renal failure status was determined using diagnostic codes (sensitivity 71.3%, specificity 96.2%). Differences in results (misclassification bias) were then corrected with QBA or BI (using progressively more complex methods to estimate disease probability).
In total, 7.4% of patients had severe renal failure. Imputing disease status with diagnostic codes exaggerated prevalence estimates [median relative change (range), 16.6% (0.8%-74.5%)] and its association with covariates [median (range) exponentiated absolute parameter estimate difference, 1.16 (1.01-2.04)]. QBA produced invalid results 9.3% of the time and increased bias in estimates of both disease prevalence and covariate associations. BI decreased misclassification bias with increasingly accurate disease probability estimates.
QBA can produce invalid results and increase misclassification bias. BI avoids invalid results and can importantly decrease misclassification bias when accurate disease probability estimates are used.
在使用行政数据库进行研究时，诊断代码的使用可能会导致分类错误，从而产生偏倚。定量偏倚分析（QBA）可以纠正这种偏倚，仅需要代码的敏感性和特异性，但可能会产生无效的结果。Bootstrap 插补（BI）也可以解决分类错误偏倚问题，但传统上需要使用多变量模型才能准确估计疾病的概率。本研究比较了使用 QBA 和 BI 进行偏倚校正的效果。
使用血清肌酐测量来确定 100000 名住院患者的严重肾衰竭状态。确定了 86 个患者分层中的严重肾衰竭的患病率及其与 43 个协变量的关联，并将其与使用诊断代码确定的肾衰竭状态的结果进行了比较（敏感性为 71.3%，特异性为 96.2%）。然后，使用 QBA 或 BI（使用越来越复杂的方法来估计疾病的概率）来校正结果中的差异（分类错误偏倚）。
总共 7.4%的患者患有严重肾衰竭。使用诊断代码推断疾病状态会夸大患病率的估计值[中位数相对变化（范围），16.6%（0.8%-74.5%）]及其与协变量的关联[中位数（范围）指数化绝对参数估计差异，1.16（1.01-2.04）]。QBA 有 9.3%的时间会产生无效结果，并增加了对疾病患病率和协变量关联的估计偏倚。BI 通过使用更准确的疾病概率估计来减少分类错误偏倚。
QBA 可能会产生无效结果并增加分类错误偏倚。BI 避免了无效结果，并在使用准确的疾病概率估计时可以重要地减少分类错误偏倚。
