Managing missing items in the Fagerstrom Test for Nicotine Dependence: a simulation study

Background: The Fagerstrom 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. Methods: 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. Results: 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%. Conclusions: 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 similar to 10% of data are missing, we recommend performing a sensitivity analysis with a different method of managing missing data.

Automated summary

This study investigated different methods for managing missing items in the Fagerstrom Test for Nicotine Dependence (FTND) and found that proration performed the best in terms of accuracy and precision. However, a sensitivity analysis with a different method is recommended when more than 10% of data are missing.