Rawlings Andreea Monica, Sang Yingying, Sharrett Albert Richey, Coresh Josef, Griswold Michael, Kucharska-Newton Anna Maria, Palta Priya, Wruck Lisa Miller, Gross Alden Lawrence, Deal Jennifer Anne, Power Melinda Carolyn, Bandeen-Roche Karen Jean
Department of Epidemiology, Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins Bloomberg School of Public Health, 2024 E. Monument Street, Suite 2-600, Baltimore, MD, 21287, USA.
Center of Biostatistics and Bioinformatics, University of Mississippi Medical Center, Jackson, MS, USA.
Eur J Epidemiol. 2017 Jan;32(1):55-66. doi: 10.1007/s10654-016-0197-8. Epub 2016 Sep 12.
Longitudinal studies of cognitive performance are sensitive to dropout, as participants experiencing cognitive deficits are less likely to attend study visits, which may bias estimated associations between exposures of interest and cognitive decline. Multiple imputation is a powerful tool for handling missing data, however its use for missing cognitive outcome measures in longitudinal analyses remains limited. We use multiple imputation by chained equations (MICE) to impute cognitive performance scores of participants who did not attend the 2011-2013 exam of the Atherosclerosis Risk in Communities Study. We examined the validity of imputed scores using observed and simulated data under varying assumptions. We examined differences in the estimated association between diabetes at baseline and 20-year cognitive decline with and without imputed values. Lastly, we discuss how different analytic methods (mixed models and models fit using generalized estimate equations) and choice of for whom to impute result in different estimands. Validation using observed data showed MICE produced unbiased imputations. Simulations showed a substantial reduction in the bias of the 20-year association between diabetes and cognitive decline comparing MICE (3-4 % bias) to analyses of available data only (16-23 % bias) in a construct where missingness was strongly informative but realistic. Associations between diabetes and 20-year cognitive decline were substantially stronger with MICE than in available-case analyses. Our study suggests when informative data are available for non-examined participants, MICE can be an effective tool for imputing cognitive performance and improving assessment of cognitive decline, though careful thought should be given to target imputation population and analytic model chosen, as they may yield different estimands.
认知表现的纵向研究对失访很敏感，因为出现认知缺陷的参与者参加研究访视的可能性较小，这可能会使感兴趣的暴露因素与认知衰退之间的估计关联产生偏差。多重填补是处理缺失数据的有力工具，然而其在纵向分析中用于缺失认知结局测量的情况仍然有限。我们使用链式方程多重填补法（MICE）对未参加社区动脉粥样硬化风险研究2011 - 2013年检查的参与者的认知表现分数进行填补。我们在不同假设下使用观察到的数据和模拟数据检验了填补分数的有效性。我们检验了在有和没有填补值的情况下，基线糖尿病与20年认知衰退之间估计关联的差异。最后，我们讨论了不同的分析方法（混合模型和使用广义估计方程拟合的模型）以及选择对谁进行填补如何导致不同的估计量。使用观察到的数据进行的验证表明，MICE产生了无偏填补。模拟显示，在缺失情况具有强信息性但现实的结构中，将MICE（偏差为3 - 4%）与仅分析可用数据（偏差为16 - 23%）相比，糖尿病与认知衰退之间20年关联的偏差大幅降低。与仅分析可用病例相比，MICE得出的糖尿病与20年认知衰退之间的关联要强得多。我们的研究表明，当有未接受检查参与者的信息性数据可用时，MICE可以成为填补认知表现和改善认知衰退评估的有效工具，不过应仔细考虑目标填补人群和所选的分析模型，因为它们可能会产生不同的估计量。
