Department of Society, Human Development, and Health, Harvard School of Public Health, Boston, MA, USA.
Neurology. 2012 Sep 25;79(13):1340-8. doi: 10.1212/WNL.0b013e31826cd62a. Epub 2012 Sep 12.
Longitudinal studies of dementia rely on the assumption that individuals who drop out are comparable to those who remain in the study, adjusting for measured covariates. Existing methods to handle dropouts account for differences based on past health and cognitive measures. We assess whether such adjustments fully account for differences in future dementia risk.
Among Three-City Study participants in Dijon, France, with 1 (n = 1,633) or 2 (n = 1,168) brain MRI scans, we tested whether white matter lesion volume (WMLV), hippocampal volume, or brain CSF volume predicted dropout ("unable to contact" or "refused interview") in repeated-measures logistic regression with up to 4 follow-ups (average 3.5 waves). Using linear regression, we also estimated differences in MRI volumes and MRI changes by subsequent dropout status and estimated plausible ranges for selective attrition bias based on these associations. Models were adjusted for demographic, health, and cognitive score covariates.
Baseline greater WMLV predicted increased odds of dropping out (adjusted odds ratio = 1.71; 95% confidence interval [CI] 1.20-2.43). Among participants with 2 MRI scans, individuals who subsequently dropped out had significantly worse declines in hippocampal volume (-0.30 SD difference; 95% CI -0.43 to -0.17) between the first and second MRI scans.
Higher future dementia risk, indicated by worse past brain MRI findings, predicted future study dropout. Adjustment for selective attrition, based on MRI markers when available, may help reduce bias in estimates of dementia incidence and improve research on dementia risk factors. MRI findings may also help prospectively identify cohort members at elevated risk of attrition.
纵向痴呆研究依赖于这样一个假设,即退出的个体与留在研究中的个体是可比的,通过对测量的协变量进行调整。现有的处理辍学的方法考虑了基于过去健康和认知测量的差异。我们评估这些调整是否充分考虑了未来痴呆风险的差异。
在法国第戎的三城市研究参与者中,我们对 1 次(n=1633)或 2 次(n=1168)脑 MRI 扫描的参与者进行了测试,我们使用重复测量逻辑回归来测试白质病变体积(WMLV)、海马体积或脑 CSF 体积是否预测辍学(“无法联系”或“拒绝访谈”),最多有 4 次随访(平均 3.5 次波)。我们还使用线性回归来估计随后辍学状态下 MRI 体积和 MRI 变化的差异,并根据这些关联估计选择性流失偏倚的合理范围。模型调整了人口统计学、健康和认知评分协变量。
基线时更大的 WMLV 预测辍学的几率增加(调整后的优势比=1.71;95%置信区间[CI] 1.20-2.43)。在有 2 次 MRI 扫描的参与者中,随后辍学的个体在第一次和第二次 MRI 扫描之间的海马体积下降明显更差(-0.30 标准差差异;95%CI-0.43 至-0.17)。
过去脑 MRI 结果较差预示着未来痴呆风险更高,这预示着未来的研究辍学。当可用时,基于 MRI 标志物进行有选择性的流失调整,可能有助于减少痴呆发病率估计的偏差,并改善痴呆风险因素的研究。MRI 结果也可能有助于前瞻性地识别流失风险较高的队列成员。