lgor_rd {metavcov}

Computing Covariance between Log Odds Ratio and Risk Difference

Description

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

Usage

lgor_rd(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

Number defined in a similar way as n12c for 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 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 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 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 treatment group.

Value

lgor

Log odds ratio for outcome 1.

rd

Risk difference for outcome 2.

v

Computed covariance.

Author(s)

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

## simple example
lgor_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){lgor_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