纵向研究中缺失数据的多种插补方法比较。
A comparison of multiple imputation methods for missing data in longitudinal studies.
机构信息
Clinical Epidemiology and Biostatistics Unit, Murdoch Children's Research Institute, Parkville, VIC, 3052, Australia.
Department of Paediatrics, The University of Melbourne, Parkville, VIC, 3052, Australia.
出版信息
BMC Med Res Methodol. 2018 Dec 12;18(1):168. doi: 10.1186/s12874-018-0615-6.
BACKGROUND
Multiple imputation (MI) is now widely used to handle missing data in longitudinal studies. Several MI techniques have been proposed to impute incomplete longitudinal covariates, including standard fully conditional specification (FCS-Standard) and joint multivariate normal imputation (JM-MVN), which treat repeated measurements as distinct variables, and various extensions based on generalized linear mixed models. Although these MI approaches have been implemented in various software packages, there has not been a comprehensive evaluation of the relative performance of these methods in the context of longitudinal data.
METHOD
Using both empirical data and a simulation study based on data from the six waves of the Longitudinal Study of Australian Children (N = 4661), we investigated the performance of a wide range of MI methods available in standard software packages for investigating the association between child body mass index (BMI) and quality of life using both a linear regression and a linear mixed-effects model.
RESULTS
In this paper, we have identified and compared 12 different MI methods for imputing missing data in longitudinal studies. Analysis of simulated data under missing at random (MAR) mechanisms showed that the generally available MI methods provided less biased estimates with better coverage for the linear regression model and around half of these methods performed well for the estimation of regression parameters for a linear mixed model with random intercept. With the observed data, we observed an inverse association between child BMI and quality of life, with available data as well as multiple imputation.
CONCLUSION
Both FCS-Standard and JM-MVN performed well for the estimation of regression parameters in both analysis models. More complex methods that explicitly reflect the longitudinal structure for these analysis models may only be needed in specific circumstances such as irregularly spaced data.
背景
目前，多变量插补（MI）已广泛应用于处理纵向研究中的缺失数据。已经提出了几种 MI 技术来插补不完全的纵向协变量，包括标准完全条件规范（FCS-Standard）和联合多变量正态插补（JM-MVN），它们将重复测量视为不同的变量，以及基于广义线性混合模型的各种扩展。虽然这些 MI 方法已在各种软件包中实现，但尚未对这些方法在纵向数据背景下的相对性能进行全面评估。
方法
使用实证数据和基于澳大利亚儿童纵向研究六波数据的模拟研究，我们研究了广泛的 MI 方法在调查儿童体重指数（BMI）和生活质量之间关联的性能，使用线性回归和线性混合效应模型。
结果
在本文中，我们已经确定并比较了 12 种不同的 MI 方法，用于插补纵向研究中的缺失数据。在随机缺失（MAR）机制下分析模拟数据表明，一般可用的 MI 方法提供了更具偏差的估计值，并且具有更好的线性回归模型覆盖率，其中约一半的方法在估计具有随机截距的线性混合模型的回归参数方面表现良好。对于观察数据，我们观察到儿童 BMI 和生活质量之间存在反比关系，无论是否存在缺失数据，都可以进行 MI。
结论
FCS-Standard 和 JM-MVN 在这两种分析模型中对回归参数的估计都表现良好。更复杂的方法可能仅在特定情况下（例如不规则间隔的数据）需要明确反映这些分析模型的纵向结构。
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