A RANDOM-EFFECTS REGRESSION-MODEL FOR METAANALYSIS
A RANDOM-EFFECTS REGRESSION-MODEL FOR METAANALYSIS
复制标题
DOI：
10.1002/sim.4780140406
复制
发表时间：
1995-02-28
影响因子：
2
通讯作者：
COLDITZ, GA
中科院分区：
文献类型：
--
作者：
BERKEY, CS;HOAGLIN, DC;COLDITZ, GA
许多荟萃分析使用随机效应模型来解释研究结果之间的异质性，超出了与固定效应相关的变化。用于合成2 x 2表格的随机效应回归方法允许纳入可能解释异质性的协变量。一项模拟研究发现，随机效应回归方法在对预防结核病的疫苗效力进行荟萃分析时表现良好，其中某些因素被认为会改变疫苗效力。研究内方差的平滑估计在估计的回归系数中产生较少的偏倚。在10项研究的荟萃分析中，该方法为检测非零截距项（代表总体治疗疗效）提供了非常好的把握度，但检测弱协变量的把握度较低。我们通过探索疫苗效力和被认为改变效力的一个因素之间的关系来说明该模型。当存在协变量时，该模型也适用于连续结果的荟萃分析。
Many meta-analyses use a random-effects model to account for heterogeneity among study results, beyond the variation associated with fixed effects. A random-effects regression approach for the synthesis of 2 x 2 tables allows the inclusion of covariates that may explain heterogeneity. A simulation study found that the random-effects regression method performs well in the context of a meta-analysis of the efficacy of a vaccine for the prevention of tuberculosis, where certain factors are thought to modify vaccine efficacy. A smoothed estimator of the within-study variances produced less bias in the estimated regression coefficients. The method provided very good power for detecting a non-zero intercept term (representing overall treatment efficacy) but low power for detecting a weak covariate in a meta-analysis of 10 studies. We illustrate the model by exploring the relationship between vaccine efficacy and one factor thought to modify efficacy. The model also applies to the meta-analysis of continuous outcomes when covariates are present.