Description Usage Arguments Value Author(s) References Examples
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.
1 2 3 4 5 |
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
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 12 13 | ## 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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