lgrr_rd | R Documentation |

The function `lgrr_rd`

compute covariance between log risk ratio and risk difference, when the two outcomes are binary. See `mix.vcov`

for effect sizes of the same or different types.

```
lgrr_rd(r, n1c, n2c, n1t, n2t,
n12c = min(n1c, n2c),
n12t = min(n1t, n2t),
s2c, s2t, f2c, f2t,
s1c, s1t, f1c, f1t)
```

`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 ` |
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 |

`s1c ` |
Number of participants with event for outcome 1 (dichotomous) in the control group. |

`s1t ` |
Defined in a similar way as |

`f1c ` |
Number of participants without event for outcome 1 (dichotomous) in the control group. |

`f1t ` |
Defined in a similar way as |

`lgrr` |
Log risk ratio for outcome 1. |

`rd` |
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
lgrr_rd(r = 0.71, n1c = 30, n2c = 35, n1t = 28, n2t = 32,
s2c = 5, s2t = 8, f2c = 30, f2t = 24,
s1c = 5, s1t = 8, f1c = 25, f1t = 20)
## calculate covariances for variable D and DD in Geeganage2010 data
attach(Geeganage2010)
D_DD <- unlist(lapply(1:nrow(Geeganage2010), function(i){lgrr_rd(r = 0.71,
n1c = nc_SBP[i], n2c = nc_DD[i],
n1t = nt_SBP[i], n2t = nt_DD[i], s2t = st_DD[i], s2c = sc_DD[i],
f2c = nc_DD[i] - sc_DD[i], f2t = nt_DD[i] - st_DD[i],
s1t = st_D[i], s1c = sc_D[i], f1c = nc_D[i] - sc_D[i], f1t = nt_D[i] - st_D[i])$v}))
D_DD
## the function mix.vcov() should be used for dataset
```

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