Performing meta-analysis with incomplete statistical information in clinical trials.
Performing meta-analysis with incomplete statistical information in clinical trials.
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在临床试验中使用不完整的统计信息进行荟萃分析。
DOI：
10.1186/1471-2288-8-56
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发表时间：
2008-08-18
影响因子：
4
通讯作者：
Zhang, Weiya
中科院分区：
文献类型：
--
作者：
Ma, Jianbing;Liu, Weiru;Hunter, Anthony;Zhang, Weiya
临床试验的结果通常以抽样分布的形式进行总结。当给出了这些分布的全部信息（平均值，SEM）时，进行元分析就很简单了。然而，当一些抽样分布只有平均值时，一个具有挑战性的问题是决定如何在元分析中使用这些分布。目前，最常见的方法是忽略这样的试验，或者对于每一个缺失SEM的试验，找到一个类似的试验，并将其SEM值作为缺失的SEM。这两种方法都有缺点。作为一种替代方案，本文开发并测试了两种新的方法，第一种是预测方法，第二种是区间方法，从一组具有完整信息的抽样分布中估计任何缺失的sem。提出了一种合并方法来处理部分信息的临床试验，模拟meta分析。我们的两种方法都使用了一个假设，即抽样分布将被合并的样本是从同一总体中随机选择的。在预测方法中，我们从给定的SEMs中预测缺失的SEMs。在区间方法中，我们定义我们认为将包含缺失的sem的区间，然后在合并过程中使用这些区间。两组临床试验被用来验证我们的方法。一组试验是比较降低2型糖尿病患者低密度脂蛋白胆固醇（LDL）的不同药物，另一组试验是关于降低眼压（IOP）药物的有效性。两种方法都被证明是有用的近似传统的荟萃分析，包括不完全信息的试验。例如，提供的拉坦前列素与替莫洛尔6个月IOP降低的meta分析结果为5.05±1.15 (Mean±SEM)，具有完整的信息。如果假设本研究的最后一个试验是部分信息，那么忽略该试验的处理不完全信息的传统分析方法给出的结果为6.49±1.36，而我们的预测方法给出的结果为5.02±1.15，我们的区间方法给出了两个区间，Mean∈[4.25,5.63],SEM∈[1.01,1.24]。预后法和区间法都是处理元分析中缺失数据的有用替代方法。我们建议临床医生使用预后方法来预测缺失的SEMs，以便进行荟萃分析，并使用区间法来获得更谨慎的结果。
Results from clinical trials are usually summarized in the form of sampling distributions. When full information (mean, SEM) about these distributions is given, performing meta-analysis is straightforward. However, when some of the sampling distributions only have mean values, a challenging issue is to decide how to use such distributions in meta-analysis. Currently, the most common approaches are either ignoring such trials or for each trial with a missing SEM, finding a similar trial and taking its SEM value as the missing SEM. Both approaches have drawbacks. As an alternative, this paper develops and tests two new methods, the first being the prognostic method and the second being the interval method, to estimate any missing SEMs from a set of sampling distributions with full information. A merging method is also proposed to handle clinical trials with partial information to simulate meta-analysis. Both of our methods use the assumption that the samples for which the sampling distributions will be merged are randomly selected from the same population. In the prognostic method, we predict the missing SEMs from the given SEMs. In the interval method, we define intervals that we believe will contain the missing SEMs and then we use these intervals in the merging process. Two sets of clinical trials are used to verify our methods. One family of trials is on comparing different drugs for reduction of low density lipprotein cholesterol (LDL) for Type-2 diabetes, and the other is about the effectiveness of drugs for lowering intraocular pressure (IOP). Both methods are shown to be useful for approximating the conventional meta-analysis including trials with incomplete information. For example, the meta-analysis result of Latanoprost versus Timolol on IOP reduction for six months provided in was 5.05 ± 1.15 (Mean ± SEM) with full information. If the last trial in this study is assumed to be with partial information, the traditional analysis method for dealing with incomplete information that ignores this trial would give 6.49 ± 1.36 while our prognostic method gives 5.02 ± 1.15, and our interval method provides two intervals as Mean ∈ [4.25, 5.63] and SEM ∈ [1.01, 1.24]. Both the prognostic and the interval methods are useful alternatives for dealing with missing data in meta-analysis. We recommend clinicians to use the prognostic method to predict the missing SEMs in order to perform meta-analysis and the interval method for obtaining a more cautious result.
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DOI：
10.1111/j.1467-9868.2005.00512.x
发表时间：
2005-09-01
影响因子：
5.8
作者：
Copas, J;Eguchi, S
通讯作者：
Eguchi, S
影响因子：
3.2
作者：
Goldstein, BJ;Pans, M;Rubin, CJ
通讯作者：
Rubin, CJ
影响因子：
13.7
作者：
Mastropasqua, L;Carpineto, P;Gallenga, PE
通讯作者：
Gallenga, PE
影响因子：
8
作者：
Matthews, DR;Charbonnel, BH;Schernthaner, G
通讯作者：
Schernthaner, G
影响因子：
4.1
作者：
Cantor, L. B.;Hoop, J.;Catoira, Y.
通讯作者：
Catoira, Y.