md_smd {metavcov}

Computing Covariance between Mean Difference and Standardized Mean Difference

Description

The function lgor_rd computes covariance between mean difference and standardized mean difference. See mix.vcov for effect sizes of the same or different types.

Usage

md_smd(smd, r, n1c, n2c, n1t, n2t,
       n12c = min(n1c, n2c), n12t = min(n1t, n2t),
       sd1t, sd2t, sd1c, sd2c)

Arguments

smd

Standardized mean difference for outcome 2.

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.

sd1t

Sample standard deviation of outcome 1 for the treatment group.

sd2t

Sample standard deviation of outcome 2 for the treatment group.

sd1c

Defined in a similar way as sd1t for the control group.

sd2c

Defined in a similar way as sd2t for the control group.

Value

g

Computed Hedge's g from the input argument smd 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

## a simple example
md_smd(smd = 1, r = 0.71, n1c = 34, n2c = 35, n1t = 25, n2t = 32,
       sd1t = 0.6, sd2t = 0.4, sd1c = 8, sd2c = 0.9)
## calculate covariances for variable SBP and DBP in Geeganage2010 data
attach(Geeganage2010)
SBP_DBP <- unlist(lapply(1:nrow(Geeganage2010), function(i){md_smd(smd = SMD_DBP, r = 0.71,
                n1c = nc_SBP[i], n2c = nc_DBP[i], n1t = nt_SBP[i], n2t = nt_DBP[i],
                sd1t = sdt_SBP[i], sd2t = sdt_DBP[i],
                sd1c = sdc_SBP[i], sd2c = sdc_SBP[i])$v}))
SBP_DBP
## the function mix.vcov() should be used for dataset