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:
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)?
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。