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阐明年龄-时期-队列分析中的假设并验证结果。

Clarifying assumptions in age-period-cohort analyses and validating results.

机构信息

University of Colorado Boulder, Boulder, CO, United States of America.

University of Texas at Austin, Austin, Texas, United States of America.

出版信息

PLoS One. 2020 Oct 6;15(10):e0238871. doi: 10.1371/journal.pone.0238871. eCollection 2020.

DOI:10.1371/journal.pone.0238871
PMID:33021978
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7537862/
Abstract

BACKGROUND

Age-period-cohort (APC) models are often used to decompose health trends into period- and cohort-based sources, but their use in epidemiology and population sciences remains contentious. Central to the contention are researchers' failures to 1) clearly state their analytic assumptions and/or 2) thoroughly evaluate model results. These failures often produce varying conclusions across APC studies and generate confusion about APC methods. Consequently, scholarly exchanges about APC methods usually result in strong disagreements that rarely offer practical advice to users or readers of APC methods.

METHODS

We use research guidelines to help practitioners of APC methods articulate their analytic assumptions and validate their results. To demonstrate the usefulness of the guidelines, we apply them to a 2015 American Journal of Epidemiology study about trends in black-white differences in U.S. heart disease mortality.

RESULTS

The application of the guidelines highlights two important findings. On the one hand, some APC methods produce inconsistent results that are highly sensitive to researcher manipulation. On the other hand, other APC methods estimate results that are robust to researcher manipulation and consistent across APC models.

CONCLUSIONS

The exercise shows the simplicity and effectiveness of the guidelines in resolving disagreements over APC results. The cautious use of APC models can generate results that are consistent across methods and robust to researcher manipulation. If followed, the guidelines can likely reduce the chance of publishing variable and conflicting results across APC studies.

摘要

背景

年龄-时期-队列(APC)模型常用于将健康趋势分解为基于时期和队列的来源,但它们在流行病学和人口科学中的应用仍存在争议。争议的核心是研究人员未能 1)清楚地陈述他们的分析假设,和/或 2)彻底评估模型结果。这些失败常常导致 APC 研究之间产生不同的结论,并对 APC 方法产生混淆。因此,关于 APC 方法的学术交流通常会导致强烈的分歧,很少能为 APC 方法的使用者或读者提供实际建议。

方法

我们使用研究指南来帮助 APC 方法的实践者阐明他们的分析假设并验证他们的结果。为了展示指南的有用性,我们将其应用于 2015 年《美国流行病学杂志》上关于美国心脏病死亡率中黑人和白人差异趋势的研究。

结果

指南的应用突出了两个重要发现。一方面,一些 APC 方法产生不一致的结果,对研究人员的操作非常敏感。另一方面,其他 APC 方法估计的结果在研究人员操作下是稳健的,并且在 APC 模型之间是一致的。

结论

该研究表明,指南在解决 APC 结果的分歧方面具有简单性和有效性。谨慎使用 APC 模型可以生成方法一致且对研究人员操作稳健的结果。如果遵循这些准则,可能会减少 APC 研究之间出现变量和冲突结果的机会。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8750/7537862/5a3b0b02b09b/pone.0238871.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8750/7537862/ab30c291cb4e/pone.0238871.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8750/7537862/30e874279548/pone.0238871.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8750/7537862/0ccb461387ef/pone.0238871.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8750/7537862/9f4e963e55b7/pone.0238871.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8750/7537862/5a3b0b02b09b/pone.0238871.g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8750/7537862/ab30c291cb4e/pone.0238871.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8750/7537862/30e874279548/pone.0238871.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8750/7537862/0ccb461387ef/pone.0238871.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8750/7537862/9f4e963e55b7/pone.0238871.g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8750/7537862/5a3b0b02b09b/pone.0238871.g005.jpg

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