Multiple-source data are often collected to provide better information of some underlying construct that is difficult to measure or likely to be missing. In this article, we describe regression-based methods for analyzing multiple-source data in Stata. We use data from the BROMS Cohort Study, a cohort of Swedish adolescents who collected data on body mass index that was self-reported and that was measured by nurses. We draw together into a single frame of reference both source reports and relate these to smoking onset. This unified method has two advantages over traditional approaches: 1) the relative predictiveness of each source can be assessed and 2) all subjects contribute to the analysis. The methods are applicable to other areas of epidemiology where multiple-source reports are used.

中文翻译：

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不同来源的体重指数评估对吸烟的影响：多源信息模型的应用。

通常收集多源数据以提供一些难以衡量或可能缺失的基础结构的更好信息。在本文中，我们描述了在 Stata 中分析多源数据的基于回归的方法。我们使用来自 BROMS 队列研究的数据，这是一个瑞典青少年队列，他们收集了自我报告并由护士测量的体重指数数据。我们将两个来源报告汇总到一个单一的参考框架中，并将这些报告与吸烟的发生联系起来。与传统方法相比，这种统一方法有两个优点：1) 可以评估每个来源的相对预测性；2) 所有受试者都有助于分析。这些方法适用于使用多源报告的其他流行病学领域。