Handling missing items in the Hospital Anxiety and Depression Scale (HADS): a simulation study.
Handling missing items in the Hospital Anxiety and Depression Scale (HADS): a simulation study.
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DOI：
10.1186/s13104-016-2284-z
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发表时间：
2016-10-22
影响因子：
1.8
通讯作者：
Butow PN
中科院分区：
文献类型：
--
作者：
Bell ML;Fairclough DL;Fiero MH;Butow PN
医院焦虑和抑郁量表（HADS）是一种在健康研究中广泛使用的问卷，但很少有关于如何处理缺失项目的指导。我们的目的是研究处理项目无应答的方法，不同的样本量，缺失项目的受试者比例，每个受试者的缺失项目比例，以及缺失机制。我们根据癌症幸存者和患者的焦虑和抑郁数据进行了模拟研究。根据随机、人口统计学和分量表依赖性缺失机制删除项目水平数据。评估了7种处理缺失项目的方法的偏倚和不精密度。使用了插补、非缺失项目数条件插补和完整病例方法。每个参数组合模拟了1000个数据集。当缺失取决于子量表时，所有方法都是最敏感的（即，抑郁值越高，缺失程度越高）。表现最差的方法是仅分析具有完整数据的个体。最好的插补方法取决于推断是针对个体还是针对总体。当在个体水平（例如筛选）使用HADS评分时，我们建议使用个体子量表平均值的“一半规则”。对于总体推断，我们建议放宽至少回答一半项目的要求，以尽量减少缺失分数。本文的在线版本（doi：10.1186/s13104-016-2284-z）包含补充材料，可供授权用户使用。
The Hospital Anxiety and Depression Scale (HADS) is a widely used questionnaire in health research, but there is little guidance on how to handle missing items. We aimed to investigate approaches to handling item non-response, varying sample size, proportion of subjects with missing items, proportion of missing items per subject, and the missingness mechanism. We performed a simulation study based on anxiety and depression data among cancer survivors and patients. Item level data were deleted according to random, demographic, and subscale dependent missingness mechanisms. Seven methods for handling missing items were assessed for bias and imprecision. Imputation, imputation conditional on the number of non-missing items, and complete case approaches were used. One thousand datasets were simulated for each parameter combination. All methods were most sensitive when missingness was dependent on the subscale (i.e., higher values of depression leads to higher levels of missingness). The worst performing approach was to analyze only individuals with complete data. The best performing imputation methods depended on whether inference was targeted at the individual or at the population. We recommend the ‘half rule’ using individual subscale means when using the HADS scores at the individual level (e.g. screening). For population inference, we recommend relaxing the requirement that at least half the items be answered to minimize missing scores. The online version of this article (doi:10.1186/s13104-016-2284-z) contains supplementary material, which is available to authorized users.