University of Arizona, Statistics Graduate Interdisciplinary Program, Tucson, AZ, USA.
University of Arizona, Statistics Consulting Lab, Health Sciences & Bio5 Institute, Tucson, AZ, USA.
BMC Med Res Methodol. 2022 May 20;22(1):145. doi: 10.1186/s12874-022-01637-2.
The Fagerström Test for Nicotine Dependence (FTND) is frequently used to assess the level of smokers' nicotine dependence; however, it is unclear how to manage missing items. The aim of this study was to investigate different methods for managing missing items in the FTND.
We performed a simulation study using data from the Arizona Smokers' Helpline. We randomly sampled with replacement from the complete data to simulate 1000 datasets for each parameter combination of sample size, proportion of missing data, and type of missing data (missing at random and missing not at random). Then for six methods for managing missing items on the FTND (two involving no imputation and four involving single imputation), we assessed the accuracy (via bias) and precision (via bias of standard error) of the total FTND score itself and of the regression coefficient for the total FTND score regressed on a covariate.
When using the total FTND score as a descriptive statistic or in analysis for both types of missing data and for all levels of missing data, proration performed the best in terms of accuracy and precision. Proration's accuracy decreased with the amount of missing data; for example, at 9% missing data proration's maximum bias for the mean FTND was only - 0.3%, but at 35% missing data its maximum bias for the mean FTND increased to - 6%.
For managing missing items on the FTND, we recommend proration, because it was found to be accurate and precise, and it is easy to implement. However, because proration becomes less accurate with more missing data, if more than ~ 10% of data are missing, we recommend performing a sensitivity analysis with a different method of managing missing data.
尼古丁依赖量表（FTND）常用于评估吸烟者的尼古丁依赖程度，但对于如何处理缺失条目尚不清楚。本研究旨在探讨 FTND 缺失条目处理的不同方法。
我们使用来自亚利桑那州吸烟者热线的数据进行模拟研究。我们采用有放回的随机抽样方法从完整数据中抽样，为每种样本量、缺失数据比例和缺失类型（随机缺失和非随机缺失）的参数组合模拟 1000 个数据集。然后，我们评估了六种 FTND 缺失条目处理方法（两种不涉及插补，四种涉及单一插补）对 FTND 总分本身和 FTND 总分回归协变量的回归系数的准确性（通过偏倚）和精密度（通过标准误的偏倚）。
当将 FTND 总分用作描述性统计量或用于分析两种缺失类型和所有缺失水平的数据时，在准确性和精密度方面，比例法表现最佳。比例法的准确性随缺失数据量的增加而降低；例如，在缺失 9%数据时，FTND 平均值的最大偏倚仅为-0.3%，但在缺失 35%数据时，FTND 平均值的最大偏倚增加到-6%。
对于处理 FTND 缺失条目，我们建议采用比例法，因为它准确且精确，易于实施。但是，由于比例法在缺失数据较多时准确性降低，如果缺失数据超过约 10%，我们建议采用不同的缺失数据处理方法进行敏感性分析。
