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队列研究中重复评估暴露测量数据缺失的报告和处理方法综述。

A review of the reporting and handling of missing data in cohort studies with repeated assessment of exposure measures.

机构信息

Cancer Epidemiology Centre, Cancer Council Victoria, Carlton, VIC, Australia.

出版信息

BMC Med Res Methodol. 2012 Jul 11;12:96. doi: 10.1186/1471-2288-12-96.

Abstract

BACKGROUND

Retaining participants in cohort studies with multiple follow-up waves is difficult. Commonly, researchers are faced with the problem of missing data, which may introduce biased results as well as a loss of statistical power and precision. The STROBE guidelines von Elm et al. (Lancet, 370:1453-1457, 2007); Vandenbroucke et al. (PLoS Med, 4:e297, 2007) and the guidelines proposed by Sterne et al. (BMJ, 338:b2393, 2009) recommend that cohort studies report on the amount of missing data, the reasons for non-participation and non-response, and the method used to handle missing data in the analyses. We have conducted a review of publications from cohort studies in order to document the reporting of missing data for exposure measures and to describe the statistical methods used to account for the missing data.

METHODS

A systematic search of English language papers published from January 2000 to December 2009 was carried out in PubMed. Prospective cohort studies with a sample size greater than 1,000 that analysed data using repeated measures of exposure were included.

RESULTS

Among the 82 papers meeting the inclusion criteria, only 35 (43%) reported the amount of missing data according to the suggested guidelines. Sixty-eight papers (83%) described how they dealt with missing data in the analysis. Most of the papers excluded participants with missing data and performed a complete-case analysis (n=54, 66%). Other papers used more sophisticated methods including multiple imputation (n=5) or fully Bayesian modeling (n=1). Methods known to produce biased results were also used, for example, Last Observation Carried Forward (n=7), the missing indicator method (n=1), and mean value substitution (n=3). For the remaining 14 papers, the method used to handle missing data in the analysis was not stated.

CONCLUSIONS

This review highlights the inconsistent reporting of missing data in cohort studies and the continuing use of inappropriate methods to handle missing data in the analysis. Epidemiological journals should invoke the STROBE guidelines as a framework for authors so that the amount of missing data and how this was accounted for in the analysis is transparent in the reporting of cohort studies.

摘要

背景

在具有多个随访波的队列研究中留住参与者是困难的。通常,研究人员面临数据缺失的问题,这可能会导致结果产生偏差,以及统计效力和精度的损失。von Elm 等人的 STROBE 指南(柳叶刀,370:1453-1457,2007 年);Vandenbroucke 等人(PLoS Med,4:e297,2007 年)和 Sterne 等人提出的指南(英国医学杂志,338:b2393,2009 年)建议队列研究报告缺失数据的数量、不参与和无响应的原因,以及在分析中处理缺失数据所使用的方法。我们对队列研究的出版物进行了综述,以记录暴露措施缺失数据的报告,并描述用于处理缺失数据的统计方法。

方法

在 PubMed 中进行了一项从 2000 年 1 月至 2009 年 12 月发表的英文论文的系统搜索。纳入了样本量大于 1000 且使用暴露重复测量进行数据分析的前瞻性队列研究。

结果

在符合纳入标准的 82 篇论文中,只有 35 篇(43%)根据建议的指南报告了缺失数据的数量。68 篇论文(83%)描述了他们如何在分析中处理缺失数据。大多数论文排除了缺失数据的参与者并进行了完整病例分析(n=54,66%)。其他论文使用了更复杂的方法,包括多重插补(n=5)或完全贝叶斯建模(n=1)。还使用了产生偏差结果的方法,例如,末次观察结转(n=7)、缺失指示符方法(n=1)和均值替代(n=3)。对于其余 14 篇论文,分析中处理缺失数据的方法未说明。

结论

本综述突出了队列研究中缺失数据报告不一致的问题,以及在分析中继续使用不适当的方法处理缺失数据的问题。流行病学杂志应援引 STROBE 指南作为作者的框架,以便在报告队列研究时,缺失数据的数量以及如何在分析中对此进行说明具有透明度。

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