smd_rd | R Documentation |

The function `smd_rd`

computes covariance between standardized mean difference and risk difference. See `mix.vcov`

for effect sizes of the same or different types.

```
smd_rd(d, r, n1c, n2c, n1t, n2t,
n12c = min(n1c, n2c), n12t = min(n1t, n2t),
s2c, s2t, f2c, f2t, sd1c, sd1t)
```

`d ` |
Standardized mean difference for outcome 1. |

`r ` |
Correlation coefficient of the two outcomes. |

`n1c ` |
Number of participants reporting outcome 1 in the control group. |

`n2c ` |
Number of participants reporting outcome 2 in the control group. |

`n1t ` |
Number of participants reporting outcome 1 in the treatment group. |

`n2t ` |
Number of participants reporting outcome 2 in the treatment group. |

`n12c ` |
Number of participants reporting both outcome 1 and outcome 2 in the control group. By default, it is equal to the smaller number between |

`n12t ` |
Number defined in a similar way as |

`s2c ` |
Number of participants with event for outcome 2 (dichotomous) in the control group. |

`s2t ` |
Defined in a similar way as |

`f2c ` |
Number of participants without event for outcome 2 (dichotomous) in the control group. |

`f2t ` |
Defined in a similar way as |

`sd1c ` |
Sample standard deviation of outcome 1 for the control group. |

`sd1t ` |
Defined in a similar way as |

`g` |
Computed Hedge's g from the input argument |

`rd` |
Computed risk difference for outcome 1. |

`v` |
Computed covariance. |

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.

```
## simple example
smd_rd(d = 1, r = 0.71, n1c = 34, n2c = 35, n1t = 25, n2t = 32,
s2c = 5, s2t = 8, f2c = 30, f2t = 24, sd1t = 0.4, sd1c = 8)
## calculate covariances for variable SBP and DD in Geeganage2010 data
attach(Geeganage2010)
SBP_DD <- unlist(lapply(1:nrow(Geeganage2010), function(i){smd_rd(d = SMD_SBP, r = 0.71,
n1c = nc_SBP[i], n2c = nc_DD[i], n1t = nt_SBP[i], n2t = nt_DD[i],
sd1t = sdt_SBP[i], s2t = st_DD[i], sd1c = sdc_SBP[i], s2c = sc_DD[i],
f2c = nc_DD[i] - sc_DD[i], f2t = nt_DD[i] - st_DD[i])}))
SBP_DD
## the function mix.vcov() should be used for dataset
```

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