Simulation evaluation of statistical properties of methods for indirect and mixed treatment comparisons.
Simulation evaluation of statistical properties of methods for indirect and mixed treatment comparisons.
复制标题
仿真评估间接和混合治疗比较方法的统计特性。
DOI：
10.1186/1471-2288-12-138
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发表时间：
2012-09-12
影响因子：
4
通讯作者：
Maas J
中科院分区：
文献类型：
--
作者：
Song F;Clark A;Bachmann MO;Maas J
间接处理比较（ITC）和混合处理比较（MTC）在网络meta分析中的应用越来越广泛。本仿真研究全面研究了常用的ITC和MTC方法的统计特性和性能，包括简单ITC （Bucher方法）、frequentist和Bayesian MTC方法。模拟了三组具有闭环的双臂试验的简单网络。不同的模拟情景基于不同的试验数量、假设的治疗效果、异质性程度、偏倚和不一致。通过I型误差、统计功率、观测偏差和均方误差（MSE）来衡量ITC和MTC方法的性能。当原始研究中没有偏倚时，所有被调查的ITC和MTC方法平均都是无偏倚的。根据不同研究中偏倚的程度和方向，ITC和MTC方法可能比直接治疗比较（DTC）或多或少偏倚。在研究的方法中，简单的ITC方法具有最大的均方误差（MSE）。DTC在统计功率和MSE方面优于ITC。在不存在系统偏差和不一致性的模拟环境下，MTC方法的性能普遍优于相应的DTC方法的性能。对于网络元分析中的不一致检测，评估的方法平均无偏。用于检测不一致的常用方法的统计能力非常低。可用于间接和混合治疗比较的方法有不同的优点和局限性，这取决于所分析的数据是否满足基本假设。为了选择最有效的统计方法进行研究综合，需要对证据网络中包含的初步研究进行适当的评估。
Indirect treatment comparison (ITC) and mixed treatment comparisons (MTC) have been increasingly used in network meta-analyses. This simulation study comprehensively investigated statistical properties and performances of commonly used ITC and MTC methods, including simple ITC (the Bucher method), frequentist and Bayesian MTC methods. A simple network of three sets of two-arm trials with a closed loop was simulated. Different simulation scenarios were based on different number of trials, assumed treatment effects, extent of heterogeneity, bias and inconsistency. The performance of the ITC and MTC methods was measured by the type I error, statistical power, observed bias and mean squared error (MSE). When there are no biases in primary studies, all ITC and MTC methods investigated are on average unbiased. Depending on the extent and direction of biases in different sets of studies, ITC and MTC methods may be more or less biased than direct treatment comparisons (DTC). Of the methods investigated, the simple ITC method has the largest mean squared error (MSE). The DTC is superior to the ITC in terms of statistical power and MSE. Under the simulated circumstances in which there are no systematic biases and inconsistencies, the performances of MTC methods are generally better than the performance of the corresponding DTC methods. For inconsistency detection in network meta-analysis, the methods evaluated are on average unbiased. The statistical power of commonly used methods for detecting inconsistency is very low. The available methods for indirect and mixed treatment comparisons have different advantages and limitations, depending on whether data analysed satisfies underlying assumptions. To choose the most valid statistical methods for research synthesis, an appropriate assessment of primary studies included in evidence network is required.
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影响因子：
3.7
作者：
Donegan S;Williamson P;Gamble C;Tudur-Smith C
通讯作者：
Tudur-Smith C
影响因子：
2
作者：
Cooper, Nicola J.;Sutton, Alex J.;Welton, Nicky J.
通讯作者：
Welton, Nicky J.
DOI：
10.1111/j.1742-1241.2009.02072.x
发表时间：
2009-06-01
影响因子：
2.6
作者：
Edwards, S. J.;Clarke, M. J.;Borrill, J.
通讯作者：
Borrill, J.
影响因子：
105.7
作者：
Hrobjartsson, Asbjorn;Thomsen, Ann Sofia Skou;Brorson, Stig
通讯作者：
Brorson, Stig
影响因子：
4.5
作者：
Jansen, Jeroen P.;Fleurence, Rachael;Cappelleri, Joseph C.
通讯作者：
Cappelleri, Joseph C.