md_lgor | R Documentation |
The function lgor_rd
computes covariance between mean difference and log odds ratio. See mix.vcov
for effect sizes of the same or different types.
md_lgor(r, n1c, n2c, n1t, n2t,
n12c = min(n1c, n2c), n12t = min(n1t, n2t),
s2c, s2t, f2c, f2t, sd1c, sd1t)
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 |
lgor |
Log odds ratio for outcome 2. |
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.
## a simple example
md_lgor(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_lgor(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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