[R-meta] Two ways to calculate subgroup and overall average effect sizes

[Nathan Pace]

Thanks for the link.

Using struct = ‘ID’ works very nicely.

Best,

Nathan

On 2408//2017, 1:51 AM, "Viechtbauer Wolfgang (SP)" <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote:

    You can do a LRT to compare the models. But you have to fit the 'standard' model also with rma.mv(). See here:
    
    http://www.metafor-project.org/doku.php/tips:rma.uni_vs_rma.mv
    
    So, in your case:
    
    painearly.df$id <- 1:nrow(painearly.df)
    
    painearly1surgery.rma <- rma.mv(yi = yi, V = vi, mods = ~ surgery, random = ~ 1 | id,
                                data = painearly.df, test = 't', digits = 3)
    
    painearly2surgery.rma.mv <- rma.mv(yi = yi, V = vi, mods = ~ surgery,
                             random = ~ surgery | study, struct = 'DIAG',
                             data = painearly.df, test = 't', digits = 3)
    
    anova(painearly1surgery.rma, painearly2surgery.rma.mv)
    
    So, that will give you a LRT of H0: tau^2.1 = tau^2.2 = tau^2.3.
    
    Another way to think about this is that this is a comparison between an 'ID' and a 'DIAG' structure.
    
    res0 <- rma.mv(yi = yi, V = vi, mods = ~ surgery,
                   random = ~ surgery | study, struct = 'ID',
                   data = painearly.df, test = 't', digits = 3)
    
    res1 <- rma.mv(yi = yi, V = vi, mods = ~ surgery,
                   random = ~ surgery | study, struct = 'DIAG',
                   data = painearly.df, test = 't', digits = 3)
    
    anova(res0, res1)
    
    But that will give you the same results (assuming that there are no studies where the same level of 'surgery' occurs more than once).
    
    Best,
    Wolfgang
    
    -----Original Message-----
    From: Nathan Pace [mailto:n.l.pace at utah.edu] 
    Sent: Thursday, August 24, 2017 00:44
    To: Viechtbauer Wolfgang (SP); r-sig-meta-analysis at r-project.org
    Subject: Re: [R-meta] Two ways to calculate subgroup and overall average effect sizes
    
    Hi Wolfgang, 
    
    I have a k = 29 SMD meta analysis.
    
    The moderator is a three level factor.
    
    painearly1surgery.rma <- rma(yi = yi, vi = vi, mods = ~ surgery,
                                data = painearly.df, test = 'knha', digits = 3)
    
    painearly2surgery.rma.mv <- rma.mv(yi = yi, V = vi, mods = ~ surgery,
                             random = ~ surgery | study, struct = 'DIAG',
                             data = painearly.df, test = 't', digits = 3)
    
    There is a nearly 10 fold variation in the individual tau^2s.
    
    Variance Components: 
    
    outer factor: study   (nlvls = 29)
    inner factor: surgery (nlvls = 3)
    
               estim   sqrt  k.lvl  fixed  level
    tau^2.1    0.082  0.287      8     no   open
    tau^2.2    0.759  0.871     10     no    lap
    tau^2.3    0.086  0.294     11     no  other
    
    The average tau^2 is:
    
    tau^2 (estimated amount of residual heterogeneity):     0.312 (SE = 0.110)
    tau (square root of estimated tau^2 value):             0.559
    
    The omnibus test of moderators  is not rejected in either model.
    
    Test of Moderators (coefficient(s) 2:3):  (average tau^2)
    F(df1 = 2, df2 = 26) = 2.082, p-val = 0.145
    
    Test of Moderators (coefficient(s) 2:3):  (individual tau^2)
    F(df1 = 2, df2 = 26) = 2.643, p-val = 0.090
    
    Is there a meaningful statistical comparison of the individual tau^2s?
    
    Are there other ways to compare the model fits (AIC or BIC)?
    
    The anova function won’t mix rma.uni and rma.mv objects.
    
    Nathan