Description Usage Arguments Value Author(s) References Examples

The function `md_lgrr`

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

for effect sizes of the same or different types.

1 2 3 |

`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 |

`lgrr` |
Log risk ratio for outcome 2. |

`v` |
Computed covariance. |

Min Lu

Ahn, S., Lu, M., Lefevor, G.T., Fedewa, A. & Celimli, S. (2016). Application of meta-analysis in sport and exercise science. In N. Ntoumanis, & N. Myers (Eds.), *An Introduction to Intermediate and Advanced Statistical Analyses for Sport and Exercise Scientists* (pp.233-253). Hoboken, NJ: John Wiley and Sons, Ltd.

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

1 2 3 4 5 6 7 8 9 10 11 | ```
## a simple example
md_lgrr(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){md_lgrr(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])$v}))
SBP_DD
## the function mix.vcov() should be used for dataset
``` |

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