[R-meta] Which method to compute 95% confidence intervals around individual effect sizes (standardized mean differences) ?
d@k|@-y@ob@@ouedr@ogo @end|ng |rom mnhn@|r
Tue May 18 16:59:53 CEST 2021
I am gathering studies about effects of various chemicals on corals, and for every concentration of a chemical I computed the standardized mean differences for several outcomes. My final goal is to know for which maximal concentration of a chemical no significant effect is observed, and for which minimal concentration of a chemical a significant effect is observed.
To get this I computed the 95% confidence intervals around the standardized mean differences to identify the effect sizes that are significantly/non significantly different from zero.
I am quite confused about how to properly compute these condidence intervals. I could see 3 different types of CI :
1/ 95% CI assuming a normal distribution
data$cilow <- data$yi - sqrt(data$vi)*qnorm(0.05/2, lower.tail = FALSE)
data$ciup <- data$yi + sqrt(data$vi)*qnorm(0.05/2, lower.tail = FALSE)
2/ 95% Wald-type confidence intervals
These are computed using the summary.escalc() function in metafor
3/ The confidence intervals computed from a multi-level model rma.mv where a variance-covariance matrix V is specified to take into account that I have several concentrations compared to the same control (Correction of Gleser & Olkin, I followed the tutorial in
to compute V)
mod <- rma.mv(yi=yi, V=V, mods= ~1, random= ~1 | ID_case, data=data, method="REML")
With forest(mod) I can see the individual study 95%CI on the plot and I can get them with
mod$yi - sqrt(mod$vi)*qnorm(0.05/2, lower.tail = FALSE)
mod$yi + sqrt(mod$vi)*qnorm(0.05/2, lower.tail = FALSE)
Because the method chosen to compute the 95% CI around the individual standardized mean differences will greatly influence the conclusions about the problematic chemical concentrations, I will greatly appreciate any help, comment or advise on my issue.
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