Stevens John W
Centre for Bayesian Statistics in Health Economics, University of Sheffield, Sheffield, UK.
Pharm Stat. 2011 Jul-Aug;10(4):374-8. doi: 10.1002/pst.491. Epub 2011 Mar 11.
A meta-analysis of a continuous outcome measure may involve missing standard errors. This is not a problem depending on assumptions made about the population standard deviation. Multiple imputation can be used to impute missing values while allowing for uncertainty in the imputation. Markov chain Monte Carlo simulation is a multiple imputation technique for generating posterior predictive distributions for missing data. We present an example of imputing missing variances using WinBUGS. The example highlights the importance of checking model assumptions, whether for missing or observed data.
对连续结果测量的荟萃分析可能涉及缺失标准误差。这并非问题，具体取决于对总体标准差所做的假设。多重填补可用于填补缺失值，同时考虑到填补过程中的不确定性。马尔可夫链蒙特卡罗模拟是一种用于为缺失数据生成后验预测分布的多重填补技术。我们给出一个使用WinBUGS填补缺失方差的示例。该示例突出了检查模型假设的重要性，无论是针对缺失数据还是观测数据。
