Sidik Kurex, Jonkman Jeffrey N
Biometrics Research, Wyeth Research, Princeton, New Jersey 39762, USA.
J Biopharm Stat. 2005;15(5):823-38. doi: 10.1081/BIP-200067915.
For random effects meta-regression inference, variance estimation for the parameter estimates is discussed. Because estimated weights are used for meta-regression analysis in practice, the assumed or estimated covariance matrix used in meta-regression is not strictly correct, due to possible errors in estimating the weights. Therefore, this note investigates the use of a robust variance estimation approach for obtaining variances of the parameter estimates in random effects meta-regression inference. This method treats the assumed covariance matrix of the effect measure variables as a working covariance matrix. Using an example of meta-analysis data from clinical trials of a vaccine, the robust variance estimation approach is illustrated in comparison with two other methods of variance estimation. A simulation study is presented, comparing the three methods of variance estimation in terms of bias and coverage probability. We find that, despite the seeming suitability of the robust estimator for random effects meta-regression, the improved variance estimator of Knapp and Hartung (2003) yields the best performance among the three estimators, and thus may provide the best protection against errors in the estimated weights.
对于随机效应元回归推断，讨论了参数估计的方差估计。由于在实际中估计权重用于元回归分析，由于估计权重时可能存在误差，元回归中使用的假定或估计协方差矩阵并不严格正确。因此，本说明研究了一种稳健方差估计方法在随机效应元回归推断中用于获得参数估计方差的情况。该方法将效应量变量的假定协方差矩阵视为工作协方差矩阵。以一种疫苗的临床试验的元分析数据为例，将稳健方差估计方法与其他两种方差估计方法进行比较说明。进行了一项模拟研究，比较了三种方差估计方法在偏差和覆盖概率方面的情况。我们发现，尽管稳健估计器看似适用于随机效应元回归，但Knapp和Hartung（2003）提出的改进方差估计器在这三种估计器中表现最佳，因此可能为防范估计权重中的误差提供最佳保护。
