# md_smd: Computing Covariance between Mean Difference and Standardized... In metavcov: Computing Variances and Covariances, Visualization and Missing Data Solution for Multivariate Meta-Analysis

## 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

 ```1 2 3``` ```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.

Min Lu

## References

Ahn, S., Lu, M., Lefevor, G.T., Fedewa, A. & Celimli, S. (2016). Application of meta-analysis in sport and exercise science. In N. Ntoumanis, & N. Myers (Eds.), An Introduction to Intermediate and Advanced Statistical Analyses for Sport and Exercise Scientists (pp.233-253). Hoboken, NJ: John Wiley and Sons, Ltd.

Wei, Y., & Higgins, J. (2013). Estimating within study covariances in multivariate meta-analysis with multiple outcomes. Statistics in Medicine, 32(7), 119-1205.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```## 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 ```

metavcov documentation built on Oct. 25, 2021, 9:08 a.m.