Accounting for Misclassified Outcomes in Binary Regression Models Using Multiple Imputation With Internal Validation Data
Accounting for Misclassified Outcomes in Binary Regression Models Using Multiple Imputation With Internal Validation Data
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DOI：
10.1093/aje/kws340
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发表时间：
2013-05-01
影响因子：
5
通讯作者：
Richardson, David B.
中科院分区：
文献类型：
--
作者：
Edwards, Jessie K.;Cole, Stephen R.;Richardson, David B.
结果错误分类在流行病学中很普遍，但很少使用解释方法。我们描述了使用多重插补，以减少偏倚时，验证数据可用于一个亚组的研究参与者。使用1992年至1998年多中心疱疹性眼病研究中308名参与者的数据（48%女性; 85%白色;中位年龄49岁）说明了这种方法。阿昔洛韦组与安慰剂组在金标准结局（医生诊断的单纯疱疹病毒复发）方面的比值比为0.62（95%置信区间（CI）：0.35，1.09）。除了用于比较方法的30%验证亚组外，我们对医生诊断进行了掩蔽。多重插补（比值比（OR）= 0.60; 95% CI：0.24，1.51）与使用自我报告结局的朴素分析进行比较（OR = 0.90; 95% CI：0.47，1.73），分析仅限于验证亚组（OR = 0.57; 95%CI：0.20，1.59）和直接最大似然（OR = 0.62; 95%CI：0.26，1.53）。在模拟中，多重插补和直接最大似然比仅限于验证亚组的分析具有更大的统计功效，但所有3种分析均提供了比值比的无偏估计。多重插补方法扩展到使用对数二项回归估计风险比。对于熟悉缺失数据方法的流行病学家来说，多重插补在灵活性和易于实施方面具有优势。
Outcome misclassification is widespread in epidemiology, but methods to account for it are rarely used. We describe the use of multiple imputation to reduce bias when validation data are available for a subgroup of study participants. This approach is illustrated using data from 308 participants in the multicenter Herpetic Eye Disease Study between 1992 and 1998 (48% female; 85% white; median age, 49 years). The odds ratio comparing the acyclovir group with the placebo group on the gold-standard outcome (physician-diagnosed herpes simplex virus recurrence) was 0.62 (95% confidence interval (CI): 0.35, 1.09). We masked ourselves to physician diagnosis except for a 30% validation subgroup used to compare methods. Multiple imputation (odds ratio (OR) = 0.60; 95% CI: 0.24, 1.51) was compared with naive analysis using self-reported outcomes (OR = 0.90; 95% CI: 0.47, 1.73), analysis restricted to the validation subgroup (OR = 0.57; 95% CI: 0.20, 1.59), and direct maximum likelihood (OR = 0.62; 95% CI: 0.26, 1.53). In simulations, multiple imputation and direct maximum likelihood had greater statistical power than did analysis restricted to the validation subgroup, yet all 3 provided unbiased estimates of the odds ratio. The multiple-imputation approach was extended to estimate risk ratios using log-binomial regression. Multiple imputation has advantages regarding flexibility and ease of implementation for epidemiologists familiar with missing data methods.