Title



     mrfunnel -- Funnel plot for two-sample MR analysis



Syntax



        mrfunnel varname_gd varname_gdse varname_gp varname_gpse [if] [in] [, options]



    options               Description
    ----------------------------------------------------------------------------------------------
      extraplots(string)  extra plots to add to the overall plot
      metric(metric)      scale of y-axis
      noivw               do not plot IVW line
      nomregger           do not plot MR-Egger line
      mrivestsopts(opts)  options passed to mrivests
      scatteropts(opts)   options passed to the scatter command
      xlrange(# #)        the range for the IVW and MR-Egger lines, see twoway_function range()
                            option
      *                   other options passed to twoway



Description



    mrfunnel provides a funnel plot for a two-sample Mendelian randomization analysis.



    There are 3 choices of measures of instrument strength to plot on the y-axis, which are
    specified using the metric option and are described below.
 
    On the plot the MR-Egger estimate is the line with the longer dashes, the IVW estimate is
    shown with the shorter dashes.



    varname_gd is a variable containing the genotype-disease (SNP-outcome) association estimates.



    varname_gdse is a variable containing the genotype-disease (SNP-outcome) association estimate
    standard errors.



    varname_gp is a variable containing the genotype-phenotype (SNP-exposure) association
    estimates.



    varname_gpse is a variable containing the genotype-phenotype (SNP-exposure) association
    estimate standard errors.



Options



    metric(gpbeta|gpbetastd|invse) specifies the metric for the y-axis. Can be one of:
    - gpbeta: the absolute value of the genotype-phenotype (SNP-exposure) estimates,
    - gpbetastd: gpbeta standardised by the genotype-disease (SNP-outcome) standard errors (the
    default),
    - invse: the inverse of the standard errors on the genotype specific IV ratio estimates.



Examples



    Using the data provided by Do et al. (2013) recreate Bowden et al. (2016) Web Figure A2
    (top-right plot, LDL-C with 73 genotypes).



    Setup
        . use https://raw.github.com/remlapmot/mrrobust/master/dodata, clear



    Select observations (p-value with exposure < 10^-8)
        . gen byte sel1 = (ldlcp2 < 1e-8)



    Funnel plot
        . mrfunnel chdbeta chdse ldlcbeta ldlcse if sel1==1



    Without adding the IVW and MR-Egger estimates
        . mrfunnel chdbeta chdse ldlcbeta ldlcse if sel1==1, noivw nomregger



    Using an unstandardised y-axis
        . mrfunnel chdbeta chdse ldlcbeta ldlcse if sel1==1, metric(gpbeta)



    Using inverse IV SEs on the y-axis
        . mrfunnel chdbeta chdse ldlcbeta ldlcse if sel1==1, metric(invse)



    Remove the legend
        . mrfunnel chdbeta chdse ldlcbeta ldlcse if sel1==1, legend(off)



    Extend the IVW and MR-Egger lines to the y-axis limits (as per the original version of this
    command)
        . mrfunnel chdbeta chdse ldlcbeta ldlcse if sel1==1, xlrange(0 10)



References



    Do et al., 2013. Common variants associated with plasma triglycerides and risk for coronary
        artery disease. Nature Genetics. 45, 1345-1352.  DOI



    Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in Mendelian
        randomization with some invalid instruments using a weighted median estimator. Genetic
        Epidemiology, 2016, 40, 4, 304-314.  DOI



Author



INCLUDE help mrrobust-author