用固定和可变总和分析组合数据的方法的比较:使用时间使用和饮食数据的示例进行模拟研究
Georgia D Tomova1,2,3, Rosemary Walmsley4, Laurie Berrie5
1The Alan Turing Institute, British Library, 96 Euston Road, London, NW1 2DB, UK. gtomova@turing.ac.uk.
BMC medical research methodology
|April 17, 2025
概括
选择合适的组合数据分析方法至关重要. 模拟表明,错误的参数化严重影响结果,特别是在可变总和更大的重新分配的情况下.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 数据科学数据科学数据科学
背景情况:
- 组合数据代表了一个整体的部分,具有固定或可变的总和.
- 现有的分析方法包括同热量/同时间模型,比率变量和组成数据分析 (CoDA).
- 以前的比较依赖于真实数据,限制了对真正模型性能的理解.
研究的目的:
- 用模拟来评估和比较不同组成数据分析方法的性能.
- 调查参数化匹配在各种条件下如何影响模型准确性.
- 评估固定和可变总数对分析结果的影响.
主要方法:
- 模拟体育活动 (固定总数) 和饮食 (可变总数) 数据.
- 在组件和结果 (禁食血葡萄糖) 之间采用线性,log2和同位数的log比关系.
- 对1个单位和更大的重新配置 (10或100个单位) 评估了通用线性/添加模型和CoDA.
主要成果:
- 模型性能取决于参数化与数据生成过程对齐.
- 错误的参数化导致更严重的错误,具有更大的重新配置和可变总和.
- 比率变量模型,相当于固定总数的线性模型,在变量总数中差异很大.
结论:
- 有固定和可变总和的组合数据表现出不同的行为.
- 所有分析方法都有实用性,但需要根据数据特征进行仔细选择.
- 调查关系形状,并选择最适合它的分析方法.
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