Wærsted Morten, Børnick Taran Svenssen, Twisk Jos W R, Veiersted Kaj Bo
Department of Work Psychology and Physiology, National Institute of Occupational Health, PO box 8149 Dep, 0033, Oslo, Norway.
Department of Epidemiology and Biostatistics, VU Medical Center, De Boelelaan 1089a, 1081 HV, Amsterdam, The Netherlands.
BMC Res Notes. 2018 Feb 13;11(1):123. doi: 10.1186/s13104-018-3228-6.
Missing data in longitudinal studies may constitute a source of bias. We suggest three simple missing data indicators for the initial phase of getting an overview of the missingness pattern in a dataset with a high number of follow-ups. Possible use of the indicators is exemplified in two datasets allowing wave nonresponse; a Norwegian dataset of 420 subjects examined at 21 occasions during 6.5 years and a Dutch dataset of 350 subjects with ten repeated measurements over a period of 35 years.
The indicators Last response (the timing of last response), Retention (the number of responded follow-ups), and Dispersion (the evenness of the distribution of responses) are introduced. The proposed indicators reveal different aspects of the missing data pattern, and may give the researcher a better insight into the pattern of missingness in a study with several follow-ups, as a starting point for analyzing possible bias. Although the indicators are positively correlated to each other, potential predictors of missingness can have a different relationship with different indicators leading to a better understanding of the missing data mechanism in longitudinal studies. These indictors may be useful descriptive tools when starting to look into a longitudinal dataset with many follow-ups.
纵向研究中的缺失数据可能构成偏差来源。我们针对在具有大量随访的数据集里初步了解缺失模式阶段，提出了三个简单的缺失数据指标。在两个允许出现波次无应答的数据集里举例说明了这些指标的可能用途；一个是挪威的数据集，有420名受试者在6.5年期间接受了21次检查，另一个是荷兰的数据集，有350名受试者在35年期间进行了十次重复测量。
引入了“末次应答”（末次应答的时间）、“留存率”（有应答的随访次数）和“离散度”（应答分布的均匀程度）这几个指标。所提出的指标揭示了缺失数据模式的不同方面，并且可以让研究人员更好地洞察多次随访研究中的缺失模式，作为分析可能偏差的起点。尽管这些指标相互之间呈正相关，但缺失的潜在预测因素与不同指标可能有不同的关系，从而有助于更好地理解纵向研究中的缺失数据机制。在开始研究具有多次随访的纵向数据集时，这些指标可能是有用的描述工具。
