Fits an IVW multivariable Mendelian randomization model using first order weights.
Arguments
- r_input
A formatted data frame using the
format_mvmr
function or an object of classMRMVInput
fromMendelianRandomization::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
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