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烟草和尼古丁产品暴露的观察性研究:最大化统计精度和准确性的最佳实践。

Observational studies of exposure to tobacco and nicotine products: Best practices for maximizing statistical precision and accuracy.

作者信息

Cohen Gal, Cook Steven

机构信息

Rose Research Center, Raleigh, NC 27617, USA.

Epidemiology Department, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA.

出版信息

iScience. 2025 Feb 8;28(3):111985. doi: 10.1016/j.isci.2025.111985. eCollection 2025 Mar 21.

DOI:10.1016/j.isci.2025.111985
PMID:40104063
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11915159/
Abstract

Non-randomized observational studies can track risk-induction and -reduction associated with real-world use of non-combusted nicotine and tobacco products. The objective of this analysis was to evaluate the precision and accuracy of recent studies and to identify opportunities for further optimizing future study designs. The ROBINS framework for minimizing statistical bias was translated to specific considerations that spanned the selection and quantification of cohorts, exposure, and outcomes. These principles were then considered within the context of a recent comprehensive meta-analysis, representing 107 observational studies, which evaluated the effects of using electronic cigarettes (ECs), combusted cigarettes (CCs) and dual use of both. The meta-analysis had previously reported the relative risk from all-sources, including tobacco use and non-tobacco use. We now report the product use-specific risk associated with displacing CCs with ECs indicated from the primary references, along with observations regarding the precision of characterization of CC and EC exposure in the cited studies.

摘要

非随机观察性研究可以追踪与非燃烧尼古丁和烟草制品的实际使用相关的风险诱导和降低情况。本分析的目的是评估近期研究的精度和准确性,并确定进一步优化未来研究设计的机会。用于最小化统计偏差的ROBINS框架被转化为涵盖队列选择与量化、暴露和结局的具体考量因素。然后,在最近一项综合荟萃分析的背景下考虑这些原则,该荟萃分析涵盖107项观察性研究,评估了使用电子烟(EC)、燃烧香烟(CC)以及两者兼用的效果。该荟萃分析此前已报告了包括烟草使用和非烟草使用在内的所有来源的相对风险。我们现在报告从主要参考文献中得出的用电子烟替代燃烧香烟所带来的特定产品使用风险,以及关于所引用研究中燃烧香烟和电子烟暴露特征描述精度的观察结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f2/11915159/b30453a3a203/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f2/11915159/9d7055d06a53/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f2/11915159/3d74cd763dca/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f2/11915159/abf8378b0531/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f2/11915159/a9c429d6c426/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f2/11915159/5367878977de/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f2/11915159/9e689a9c2228/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f2/11915159/b30453a3a203/gr6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f2/11915159/9d7055d06a53/fx1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f2/11915159/3d74cd763dca/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f2/11915159/abf8378b0531/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f2/11915159/a9c429d6c426/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f2/11915159/5367878977de/gr4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f2/11915159/9e689a9c2228/gr5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f2/11915159/b30453a3a203/gr6.jpg

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