解释metafor的元回归输出

时间:2017-07-19 17:03:45

标签: r statistics metafor

我一直在使用 metafor 包进行一些荟萃分析,并希望使用元回归来调整单个连续协变量(平均年龄)。但是,我需要对产出及其含义进行一些澄清。下面我分享了基本案例分析和元回归的输出(两者中的相同研究,唯一的区别是元回归的协变量的增加)。

基本案例输出

Random-Effects Model (k = 36; tau^2 estimator: DL)

  logLik  deviance       AIC       BIC      AICc  
-18.8613   60.5927   41.7226   44.8896   42.0862  

tau^2 (estimated amount of total heterogeneity): 0.0633 (SE = 0.0327)
tau (square root of estimated tau^2 value):      0.2515
I^2 (total heterogeneity / total variability):   51.46%
H^2 (total variability / sampling variability):  2.06

Test for Heterogeneity: 
Q(df = 35) = 72.1031, p-val = 0.0002

Model Results:

estimate       se     zval     pval    ci.lb    ci.ub          
  0.1266   0.0633   2.0014   0.0453   0.0026   0.2506        * 

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

元回归(输出)

Mixed-Effects Model (k = 36; tau^2 estimator: DL)

  logLik  deviance       AIC       BIC      AICc  
-18.7696   60.4092   43.5391   48.2897   44.2891  

tau^2 (estimated amount of residual heterogeneity):     0.0677 (SE = 0.0346)
tau (square root of estimated tau^2 value):             0.2601
I^2 (residual heterogeneity / unaccounted variability): 52.84%
H^2 (unaccounted variability / sampling variability):   2.12
R^2 (amount of heterogeneity accounted for):            0.00%

Test for Residual Heterogeneity: 
QE(df = 34) = 72.1024, p-val = 0.0001

Test of Moderators (coefficient(s) 2): 
QM(df = 1) = 0.2456, p-val = 0.6202

Model Results:

         estimate      se     zval    pval    ci.lb   ci.ub   
intrcpt   -0.3741  1.0140  -0.3690  0.7122  -2.3616  1.6133   
mods       0.0085  0.0172   0.4955  0.6202  -0.0252  0.0423   

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

我的问题是:

  1. 为什么我们在元回归中观察到0%的R平方(是因为协变量不重要还是你怀疑某些东西不正确)?

  2. 我们如何解释元回归的输出?对于logHR的反向转换,我们怀疑如下所示,但我想确保正确地解释'intrcpt'和'mods'值。

    • 我假设mods代表汇总人力资源,考虑到年龄的调整。

    • 我假设intrcpt表示协变量效应(beta) - 即logHR在一个单位年龄增加时变化的量。另外,我已经对这个输出进行了反向转换,我不确定是否合适,或者我是否应该按原样出现。

0 个答案:

没有答案