# lgor_lgrr: Computing Covariance between Log Odds Ratio and Log Risk... In metavcov: Computing Variances and Covariances, Visualization and Missing Data Solution for Multivariate Meta-Analysis

 lgor_lgrr R Documentation

## Computing Covariance between Log Odds Ratio and Log Risk Ratio

### Description

The function `lgor_lgrr` computes covariance between log odds ratio and log risk ratio, when the two outcomes are binary. See `mix.vcov` for effect sizes of the same or different types.

### Usage

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

### Arguments

 `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 `n1c` and `n2c`. `n12t ` Defined in a similar way as `n12c` for the treatment group. `s2c ` Number of participants with event for outcome 2 (dichotomous) in the control group. `s2t ` Defined in a similar way as `s2c` for the treatment group. `f2c ` Number of participants without event for outcome 2 (dichotomous) in the control group. `f2t ` Defined in a similar way as `f2c` for the treatment group. `s1c ` Number of participants with event for outcome 1 (dichotomous) in the control group. `s1t ` Defined in a similar way as `s1c` for the treatment group. `f1c ` Number of participants without event for outcome 1 (dichotomous) in the control group. `f1t ` Defined in a similar way as `f1c` for the treatment group.

### Value

 `lgor` Log odds ratio for outcome 1. `lgrr` Log risk ratio for outcome 2. `v` Computed covariance.

Min Lu

### References

Lu, M. (2023). Computing within-study covariances, data visualization, and missing data solutions for multivariate meta-analysis with metavcov. Frontiers in Psychology, 14:1185012.

### Examples

``````lgor_lgrr(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){lgor_lgrr(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
``````

metavcov documentation built on July 9, 2023, 7:11 p.m.