统计前考虑因素在认知工具的协调中的应用：ARIC、CARDIA、CHS、FHS、MESA 和 NOMAS 的协调。
Pre-Statistical Considerations for Harmonization of Cognitive Instruments: Harmonization of ARIC, CARDIA, CHS, FHS, MESA, and NOMAS.
机构信息
Department of Physical Medicine and Rehabilitation, University of Michigan Medical School, Ann Arbor, MI, USA.
Cognitive Health Services Research Program, University of Michigan Medical School, Ann Arbor, MI, USA.
出版信息
J Alzheimers Dis. 2021;83(4):1803-1813. doi: 10.3233/JAD-210459.
BACKGROUND
Meta-analyses of individuals' cognitive data are increasing to investigate the biomedical, lifestyle, and sociocultural factors that influence cognitive decline and dementia risk. Pre-statistical harmonization of cognitive instruments is a critical methodological step for accurate cognitive data harmonization, yet specific approaches for this process are unclear.
OBJECTIVE
To describe pre-statistical harmonization of cognitive instruments for an individual-level meta-analysis in the blood pressure and cognition (BP COG) study.
METHODS
We identified cognitive instruments from six cohorts (the Atherosclerosis Risk in Communities Study, Cardiovascular Health Study, Coronary Artery Risk Development in Young Adults study, Framingham Offspring Study, Multi-Ethnic Study of Atherosclerosis, and Northern Manhattan Study) and conducted an extensive review of each item's administration and scoring procedures, and score distributions.
RESULTS
We included 153 cognitive instrument items from 34 instruments across the six cohorts. Of these items, 42%were common across ≥2 cohorts. 86%of common items showed differences across cohorts. We found administration, scoring, and coding differences for seemingly equivalent items. These differences corresponded to variability across cohorts in score distributions and ranges. We performed data augmentation to adjust for differences.
CONCLUSION
Cross-cohort administration, scoring, and procedural differences for cognitive instruments are frequent and need to be assessed to address potential impact on meta-analyses and cognitive data interpretation. Detecting and accounting for these differences is critical for accurate attributions of cognitive health across cohort studies.
背景
越来越多的个体认知数据的荟萃分析被用来研究影响认知能力下降和痴呆风险的生物医学、生活方式和社会文化因素。认知工具的预统计协调是准确认知数据协调的关键方法步骤，但这一过程的具体方法尚不清楚。
目的
描述血压和认知（BP COG）研究中个体水平荟萃分析中认知工具的预统计协调。
方法
我们从六个队列（动脉粥样硬化风险社区研究、心血管健康研究、冠状动脉风险发展在年轻人研究、弗雷明汉后代研究、多民族动脉粥样硬化研究和曼哈顿北部研究）中确定了认知工具，并对每个项目的管理和评分程序以及分数分布进行了广泛的审查。
结果
我们纳入了来自 6 个队列的 34 种认知工具的 153 个认知工具项目。在这些项目中，42%在≥2 个队列中是共同的。86%的常见项目在队列之间存在差异。我们发现了看似相同的项目在管理、评分和编码方面的差异。这些差异对应于分数分布和范围在队列之间的变化。我们进行了数据扩充以调整差异。
结论
认知工具在跨队列的管理、评分和程序方面存在差异且频繁发生，需要进行评估以解决其对荟萃分析和认知数据解释的潜在影响。检测和解释这些差异对于准确归因于队列研究中的认知健康至关重要。
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