使用链接的行政数据来帮助处理不响应和恢复队列研究中的样本代表性:1958年全国儿童发展研究和医院病例统计数据
Nasir Rajah1, Lisa Calderwood1, Bianca L De Stavola2
1Centre for Longitudinal Studies, UCL Social Research Institute, University College London, 20 Bedford Way, London, WC1H 0AL, UK.
BMC medical research methodology
|November 11, 2023
概括
链接的行政数据,比如医院情节统计 (HES),在国家儿童发展研究 (NCDS) 中对缺失数据的处理进行了最小的改进. 需要进一步的研究来探索队列研究的更广泛应用.
科学领域:
- 流行病学 流行病学
- 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:
- 越来越多的人对利用相关的行政数据来解决队列研究中缺少数据的挑战越来越感兴趣.
- 1958年全国儿童发育研究 (NCDS) 是一项英国队列研究,样本规模很大 (n=18,558).
- 缺少的数据可能会损害队列研究结果的代表性和有效性.
研究的目的:
- 评估链接的医院病例统计 (HES) 数据的潜力,以改善在NCDS中缺少数据的分析.
- 为了确定预测NCDS55岁扫描中非响应的HES变量.
- 评估这些HES变量在多重归算 (MI) 中作为辅助变量,恢复样本代表性的程度.
主要方法:
- 使用了链接的NCDS和HES数据.
- LASSO变量选择确定了预测NCDS非响应的HES变量.
- 多重归算 (MI) 分析将预测性HES变量作为辅助预测因素纳入.
- 对早期生活变量和外部基准的样本代表性进行了评估.
主要成果:
- 发现十个HES变量预测了55岁时NCDS无响应.
- 例如,以前的精神疾病治疗增加了不反应的几率 (OR 1.73).
- 在MI中包含HES变量在样本代表性方面提供了有限的改善,与仅调查预测因素相比没有额外的收益.
结论:
- 链接的HES数据在NCDS中处理缺失数据方面提供了有限的帮助.
- 结果可能无法将其推广到其他队列或行政数据集.
- 这项研究作为使用链接行政数据来管理缺少的队列数据的模板.
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