ivw_mvmr

Fits an IVW multivariable Mendelian randomization model using first order weights.

Usage

ivw_mvmr(r_input, gencov = 0)

Arguments

r_input

A formatted data frame using the format_mvmr function or an object of class MRMVInput from MendelianRandomization::mr_mvinput

gencov

Calculating heterogeneity statistics requires the covariance between the effect of the genetic variants on each exposure to be known. This can either be estimated from individual level data, be assumed to be zero, or fixed at zero using non-overlapping samples of each exposure GWAS. A value of 0 is used by default.

Value

An dataframe containing MVMR results, including estimated coefficients, their standard errors, t-statistics, and corresponding (two-sided) p-values.

References

Sanderson, E., et al., An examination of multivariable Mendelian randomization in the single-sample and two-sample summary data settings. International Journal of Epidemiology, 2019, 48, 3, 713-727. doi:10.1093/ije/dyy262

Author

Wes Spiller; Eleanor Sanderson; Jack Bowden.

Examples

r_input <- format_mvmr(
    BXGs = rawdat_mvmr[,c("LDL_beta","HDL_beta")],
    BYG = rawdat_mvmr$SBP_beta,
    seBXGs = rawdat_mvmr[,c("LDL_se","HDL_se")],
    seBYG = rawdat_mvmr$SBP_se,
    RSID = rawdat_mvmr$SNP)
ivw_mvmr(r_input)
#> Warning: Covariance between effect of genetic variants on each exposure not specified. Fixing covariance at 0.
#> 
#> Multivariable MR
#> 
#>               Estimate Std. Error    t value  Pr(>|t|)
#> exposure1 -0.031003996 0.01302925 -2.3795686 0.0186526
#> exposure2  0.006039167 0.01029181  0.5867933 0.5582678
#> 
#> Residual standard error: 2.209 on 143 degrees of freedom
#> 
#> 
#>               Estimate Std. Error    t value  Pr(>|t|)
#> exposure1 -0.031003996 0.01302925 -2.3795686 0.0186526
#> exposure2  0.006039167 0.01029181  0.5867933 0.5582678