metavcov (version 2.1.5)

md_rd: Computing Covariance between Mean Difference and Risk Difference

Description

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

Usage

md_rd(r, n1c, n2c, n1t, n2t,
      n12c = min(n1c, n2c), n12t = min(n1t, n2t),
      s2c, s2t, f2c, f2t, sd1c, sd1t)

Value

rd

Computed risk difference for outcome 2.

v

Computed covariance.

Author

Min Lu

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 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.

sd1c

Sample standard deviation of outcome 1 for the control group.

sd1t

Defined in a similar way as sd1c for the treatment group.

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_rd(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_rd(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