mix.vcov | R Documentation |

The function `r.vcov`

computes effect sizes and variance-covariance matrices between effect sizes of the same of different types. Effect sizes include mean difference (MD), standardized mean difference (SMD), log odds ratio (logOR), log risk ratio (logRR), and risk difference (RD). Formulas are in Table I of Wei et al.'s paper (2013).

```
mix.vcov(d, r, nt, nc,
st, sc, n_rt = NA, n_rc = NA,
sdt, sdc, type,
name = NULL, na.impute = NA)
```

`d ` |
A |

`r ` |
A |

`nt ` |
A |

`nc ` |
A matrix defined in a similar way as |

`st ` |
A |

`sc ` |
Defined in a similar way as |

`n_rt ` |
A |

`n_rc ` |
A list defined in a similar way as |

`sdt ` |
A |

`sdc ` |
A matrix defined in a similar way as |

`type ` |
A |

`name ` |
A |

`na.impute ` |
Missing values in |

` ef` |
A |

`list.vcov ` |
A |

`matrix.vcov ` |
A |

Min Lu

Lu, M. (2023). Computing within-study covariances, data visualization, and missing data solutions for multivariate meta-analysis with metavcov. *Frontiers in Psychology*, 14:1185012.

Wei, Y., & Higgins, J. (2013). Estimating within study covariances in multivariate meta-analysis with multiple outcomes. *Statistics in Medicine, 32*(7), 119-1205.

```
#########################################################################
# Example: Geeganage2010 data
# Preparing covariances for a multivariate meta-analysis
# Choose variable SBP, DBP, DD, D with effect sizes "MD","MD","RD","lgOR"
#########################################################################
data(Geeganage2010)
## set the correlation coefficients list r
r12 <- 0.71
r13 <- 0.5
r14 <- 0.25
r23 <- 0.6
r24 <- 0.16
r34 <- 0.16
r <- vecTosm(c(r12, r13, r14, r23, r24, r34))
diag(r) <- 1
mix.r <- lapply(1:nrow(Geeganage2010), function(i){r})
attach(Geeganage2010)
## compute variance co-variances
computvcov <- mix.vcov(type = c("MD", "MD", "RD", "lgOR"),
d = cbind(MD_SBP, MD_DBP, NA, NA),
sdt = cbind(sdt_SBP, sdt_DBP, NA, NA),
sdc = cbind(sdc_SBP, sdc_DBP, NA, NA),
nt = cbind(nt_SBP, nt_DBP, nt_DD, nt_D),
nc = cbind(nc_SBP, nc_DBP, nc_DD, nc_D),
st = cbind(NA, NA, st_DD, st_D),
sc = cbind(NA, NA, sc_DD, sc_D),
r = mix.r,
name = c("MD.SBP", "MD.DBP", "RD.DD", "lgOR.D"))
# save different effect sizes in y
y <- computvcov$ef
head(y)
# save variances and covariances of all the effect sizes in a matrix S
S <- computvcov$matrix.vcov
S[1, ]
## fixed-effect model
MMA_FE <- summary(metafixed(y = y, Slist = computvcov$list.vcov))
#######################################################################
# Running random-effects model using package "mixmeta" or "metaSEM"
#######################################################################
# Restricted maximum likelihood (REML) estimator from the mixmeta package
# library(mixmeta)
# mvmeta_RE <- summary(mixmeta(cbind(MD.SBP, MD.DBP, RD.DD, lgOR.D) ~ 1,
# S = S, data = y, method = "reml"))
# mvmeta_RE
# maximum likelihood estimators from the metaSEM package
# library(metaSEM)
# metaSEM_RE <- summary(meta(y = y, v = S))
# metaSEM_RE
##############################################################
# Plotting the result:
##############################################################
obj <- MMA_FE
# obj <- mvmeta_RE
# obj <- metaSEM_RE
# pdf("CI.pdf", width = 4, height = 7)
plotCI(y = computvcov$ef, v = computvcov$list.vcov,
name.y = NULL, name.study = Geeganage2010$studyID,
y.all = obj$coefficients[,1],
y.all.se = obj$coefficients[,2],
hline = c(0, 0, 0, 1))
# dev.off()
```

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