Allowing for uncertainty due to missing continuous outcome data in pairwise and network meta-analysis.
Allowing for uncertainty due to missing continuous outcome data in pairwise and network meta-analysis.
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DOI：
10.1002/sim.6365
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发表时间：
2015-02-28
影响因子：
2
通讯作者：
Salanti G
中科院分区：
文献类型：
--
作者：
Mavridis D;White IR;Higgins JP;Cipriani A;Salanti G
在随机对照试验中经常遇到缺失的结局数据，因此可能需要在多项试验的Meta分析中解决。处理缺失数据的一种常见而简单的方法是将分析限制在获得结果的个体（完整病例分析）。然而，如果忽略信息丰富的缺失数据，则完整病例分析的估计治疗效果可能存在偏差。我们开发了在试验中某些个体存在缺失数据的情况下估计连续结局的Meta分析汇总治疗效果的方法。我们建立在以前开发的二元结果的方法，通过信息缺失的比值比量化偏离随机缺失假设的程度。我们的新模型量化了随机缺失的偏离程度，使用信息缺失的平均值差异或信息缺失的平均值比，这两者都与缺失的结果数据的平均值的观察数据。我们建议估计的治疗效果，调整信息缺失，和他们的标准误差的泰勒级数近似和蒙特卡罗方法。我们将该方法应用于多组试验的成对和网络Meta分析的示例。版权所有© 2014作者.出版社：John Wiley & Sons Ltd
Missing outcome data are commonly encountered in randomized controlled trials and hence may need to be addressed in a meta‐analysis of multiple trials. A common and simple approach to deal with missing data is to restrict analysis to individuals for whom the outcome was obtained (complete case analysis). However, estimated treatment effects from complete case analyses are potentially biased if informative missing data are ignored. We develop methods for estimating meta‐analytic summary treatment effects for continuous outcomes in the presence of missing data for some of the individuals within the trials. We build on a method previously developed for binary outcomes, which quantifies the degree of departure from a missing at random assumption via the informative missingness odds ratio. Our new model quantifies the degree of departure from missing at random using either an informative missingness difference of means or an informative missingness ratio of means, both of which relate the mean value of the missing outcome data to that of the observed data. We propose estimating the treatment effects, adjusted for informative missingness, and their standard errors by a Taylor series approximation and by a Monte Carlo method. We apply the methodology to examples of both pairwise and network meta‐analysis with multi‐arm trials. © 2014 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.
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影响因子：
7
作者：
Schafer, JL;Graham, JW
通讯作者：
Graham, JW
影响因子：
3.4
作者：
Wahlbeck, K;Tuunainen, A;Leucht, S
通讯作者：
Leucht, S
DOI：
10.1016/s0197-2456(03)00021-7
发表时间：
2003-08-01
期刊：
CONTROLLED CLINICAL TRIALS
影响因子：
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作者：
Magder, LS
通讯作者：
Magder, LS
影响因子：
2.7
作者：
Higgins, Julian P. T.;White, Ian R.;Wood, Angela M.
通讯作者：
Wood, Angela M.
影响因子：
7.2
作者：
Salanti, Georgia;Ades, A. E.;Ioannidis, John P. A.
通讯作者：
Ioannidis, John P. A.