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Systematic handling of missing data in complex study designs - experiences from the Health 2000 and 2011 Surveys

机译：系统地处理复杂研究设计中的缺失数据-2000年和2011年卫生调查的经验

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摘要

We present a systematic approach to the practical and comprehensive handling of missing data motivated by our experiences of analyzing longitudinal survey data. We consider the Health 2000 and 2011 Surveys (BRIF8901) where increased non-response and non-participation from 2000 to 2011 was a major issue. The model assumptions involved in the complex sampling design, repeated measurements design, non-participation mechanisms and associations are presented graphically using methodology previously defined as a causal model with design, i.e. a functional causal model extended with the study design. This tool forces the statistician to make the study design and the missing-data mechanism explicit. Using the systematic approach, the sampling probabilities and the participation probabilities can be considered separately. This is beneficial when the performance of missing-data methods are to be compared. Using data from Health 2000 and 2011 Surveys and from national registries, it was found that multiple imputation removed almost all differences between full sample and estimated prevalences. The inverse probability weighting removed more than half and the doubly robust method 60% of the differences. These findings are encouraging since decreasing participation rates are a major problem in population surveys worldwide.
机译：我们通过分析纵向调查数据的经验，提出了一种系统的方法来实际和全面地处理丢失的数据。我们认为2000年和2011年卫生调查（BRIF8901）的主要问题是，从2000年到2011年，无应答和无参与的情况有所增加。使用先前定义为设计的因果模型的方法（即随研究设计扩展的功能因果模型），以图形方式显示涉及复杂抽样设计，重复测量设计，非参与机制和关联的模型假设。该工具迫使统计学家明确研究设计和缺失数据机制。使用系统方法，可以分别考虑抽样概率和参与概率。当比较丢失数据方法的性能时，这是有益的。使用《 2000年健康调查》和《 2011年调查》以及国家注册机构的数据，发现多次估算消除了全部样本和估计患病率之间的几乎所有差异。逆概率加权消除了一半以上，而双稳健方法消除了60％的差异。这些发现令人鼓舞，因为降低参与率是全球人口调查中的主要问题。

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