通过从中断时间序列图表中数字提取的数据,可以准确计算效应估计值
Simon Lee Turner1, Elizabeth Korevaar1, Miranda S Cumpston1
1School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
Research synthesis methods
|June 9, 2023
概括
从中断时间序列 (ITS) 图表中提取数字数据是一种可行的元分析方法. 尽管有轻微的提取错误,但效果估计仍然准确,支持将ITS研究纳入审查.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
背景情况:
- 间断时间序列 (ITS) 研究对于评估人口级干预措施至关重要.
- 系统审查和元分析通常需要对ITS数据进行重新分析,但原始数据很少可用.
- 来自ITS出版物的图表提供了数据提取的潜在来源.
研究的目的:
- 评估从ITS图表中数字提取的时间序列数据中得出的影响估计的准确性.
- 确定数据提取错误是否会影响ITS效应估计的可靠性.
主要方法:
- 选择了43个中断时间序列研究,使用可用的数据集和图表.
- 由四名研究人员使用数字提取软件从图表中提取时间序列数据.
- 将细分线性回归模型应用于提取和原始数据集,以比较效应估计.
主要成果:
- 识别了微小的数据提取错误,主要与图形复杂性有关.
- 发现这些错误并没有显著改变中断效应或相关统计数据的估计.
- 从提取的和原始数据集的效果估计之间显示出强烈的一致性.
结论:
- 从ITS图表中提取数字数据是系统审查和元分析的可行和推方法.
- 包含数字提取数据的研究,尽管有轻微的不准确性,是优于排除它们.
- 这种方法增强了公共卫生和政策决策的证据基础.
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