School of Public Health, University of Queensland, Public Health Building, Herston Road, Herston, Brisbane, Qld 4006, Australia.
School of Public Health, University of Queensland, Public Health Building, Herston Road, Herston, Brisbane, Qld 4006, Australia.
J Clin Epidemiol. 2015 Oct;68(10):1165-75. doi: 10.1016/j.jclinepi.2015.03.011. Epub 2015 Mar 31.
To compare methods for analysis of longitudinal studies with missing data due to participant dropout and follow-up truncated by death.
We analyzed physical functioning in an Australian longitudinal study of elderly women where the missing data mechanism could either be missing at random (MAR) or missing not at random (MNAR). We assumed either an immortal cohort where deceased participants are implicitly included after death or a mortal cohort where the target of inference is surviving participants at each survey wave. To illustrate the methods a covariate was included. Simulation was used to assess the effect of the assumptions.
Ignoring attrition or restricting analysis to participants with complete follow up led to biased estimates. Linear mixed model was appropriate for an immortal cohort under MAR but not MNAR. Linear increment model and joint modeling of longitudinal outcome and time to death were the most robust to MNAR. For a mortal cohort, inverse probability weighting and multiple imputation could be used, but care is needed in specifying dropout and imputation models, respectively.
Appropriate analysis methodology to deal with attrition in longitudinal studies depends on the target of inference and the missing data mechanism.
比较因参与者失访和随访因死亡而截断而导致数据缺失的纵向研究的分析方法。
我们分析了澳大利亚一项针对老年女性的纵向研究中的身体功能，其中缺失数据机制可能是随机缺失（MAR）或非随机缺失（MNAR）。我们假设要么是一个不朽的队列，在死亡后，死亡参与者被隐含地包括在内，要么是一个有死亡风险的队列，在每个调查波次中，推断的目标是幸存的参与者。为了说明这些方法，我们纳入了一个协变量。模拟用于评估假设的影响。
忽略失访或将分析仅限于具有完整随访的参与者会导致有偏差的估计。线性混合模型适用于 MAR 下的不朽队列，但不适用于 MNAR。线性增量模型和纵向结果与死亡时间的联合建模对 MNAR 最稳健。对于有死亡风险的队列，可以使用逆概率加权和多重插补，但分别需要注意失访和插补模型的指定。
处理纵向研究中失访的适当分析方法取决于推断的目标和缺失数据机制。
