纠正吸烟趋势中不可否认的缺失
Correcting for non-ignorable missingness in smoking trends
摘要：
数据非随机缺失（MNAR）是调查抽样中的一个主要挑战。我们提出了一种基于登记数据的方法来处理健康检查调查中的不可识别的缺失。这一方法依赖于调查后几年行政登记册提供的后续数据。为了说明，我们使用芬兰国家FINRISK研究在1972年至1997年进行的吸烟流行率的数据。这些数据由测量的调查信息组成，包括缺失指标、基于登记的背景信息和基于登记的疾病生存时间数据。虽然原始调查数据是MNAR，但利用这些数据可以估计缺失机制的参数。底层数据生成过程由贝叶斯模型建模。结果表明，芬兰的估计吸烟流行率可能会受到缺失数据的显著影响。
英文摘要：
Data missing not at random (MNAR) is a major challenge in survey sampling. We propose an approach based on registry data to deal with non-ignorable missingness in health examination surveys. The approach relies on follow-up data available from administrative registers several years after the survey. For illustration we use data on smoking prevalence in Finnish National FINRISK study conducted in 1972-1997. The data consist of measured survey information including missingness indicators, register-based background information and register-based time-to-disease survival data. The parameters of missingness mechanism are estimable with these data although the original survey data are MNAR. The underlying data generation process is modelled by a Bayesian model. The results indicate that the estimated smoking prevalence rates in Finland may be significantly affected by missing data.
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