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

## Description

The function `smd_lgor` computes covariance between standardized mean difference and log odds ratio. See `mix.vcov` for effect sizes of the same or different types.

## Usage

 ```1 2 3``` ```smd_lgor(d, r, n1c, n2c, n1t, n2t, n12c = min(n1c, n2c), n12t = min(n1t, n2t), s2c, s2t, f2c, f2t, sd1c, sd1t) ```

## Arguments

 `d ` Standardized mean difference for outcome 1. `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.

## Value

 `g` Computed Hedge's g from the input argument `d` for outcome 1. `lgor` Computed log odds ratio 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``` ```## simple example smd_lgor(d = 1, 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){smd_lgor(d = SMD_SBP, 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 ```

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