Facilitating meta-analyses by deriving relative effect and precision estimates for alternative comparisons from a set of estimates presented by exposure level or disease category
Facilitating meta-analyses by deriving relative effect and precision estimates for alternative comparisons from a set of estimates presented by exposure level or disease category
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DOI：
10.1002/sim.3013
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发表时间：
2008-03-30
影响因子：
2
通讯作者：
Ambuehl, Mathias
中科院分区：
文献类型：
--
作者：
Hamling, Jan;Lee, Peter;Ambuehl, Mathias
与特定疾病的特定接触有关的流行病学研究可能以各种方式呈现其结果。通常，结果以估计的比值比（或相对风险）和置信区间（CI）的形式呈现，例如，通过暴露的持续时间或水平，与单个参考类别（通常是未暴露的）进行比较。对于系统性文献综述，特别是荟萃分析，可能需要对类别进行替代比较的估计，例如任何暴露与无暴露。获得这些替代比较并不简单，因为初始估计值集是相关的。本文介绍了一种方法，估计这些替代比较的基础上最初提出的格陵兰和Longnecker的想法，并提供了该方法的实现，使用Microsoft Excel和SAS开发。基于吸烟和癌症的研究的方法的例子。该方法还处理按疾病类别（如癌症的组织学类型）给出的结果。该方法允许在总结已发表的证据时使用更一致的比较，从而可能提高荟萃分析的可靠性。版权所有（C）2007约翰威利父子有限公司
Epidemiological studies relating a particular exposure to a specified disease may present their results in a variety of ways. Often, results are presented as estimated odds ratios (or relative risks) and confidence intervals (CIs) for a number of categories of exposure, for example, by duration or level of exposure, compared with a single reference category, often the unexposed. For systematic literature review, and particularly meta-analysis, estimates for an alternative comparison of the categories, such as any exposure versus none, may be required. Obtaining these alternative comparisons is not straightforward, as the initial set of estimates is correlated. This paper describes a method for estimating these alternative comparisons based on the ideas originally put forward by Greenland and Longnecker, and provides implementations of the method, developed using Microsoft Excel and SAS. Examples of the method based on studies of smoking and cancer are given. The method also deals with results given by categories of disease (such as histological types of a cancer). The method allows the use of a more consistent comparison when summarizing published evidence, thus potentially improving the reliability of a meta-analysis. Copyright (C) 2007 John Wiley & Sons, Ltd.