The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

qgcompint: quantile g-computation with effect measure modification

Quick start

devtools::install_github("alexpkeil1/qgcompint")
library(qgcomp)
library(qgcompint)
 set.seed(40)
dat <- data.frame(y=runif(50),
                  x1=runif(50),
                  x2=runif(50),
                  z=rbinom(50,1,0.5),
                  r=rbinom(50,1,0.5))
 
 # quantile g-computation without effect measure modification
 qfit <- qgcomp.noboot(f=y ~ z + x1 + x2, 
           expnms = c('x1', 'x2'), 
           data=dat, q=2, 
           family=gaussian())
 # no output given here          
 
 # with effect measure modification by Z
 (qfitemm <- qgcomp.emm.noboot(f=y ~ z + x1 + x2,
           emmvar="z", 
           expnms = c('x1', 'x2'), 
           data=dat, q=2, 
           family=gaussian()))



> ## Qgcomp weights/partial effects at z = 0
> Scaled effect size (positive direction, sum of positive coefficients = 0)
> None

> Scaled effect size (negative direction, sum of negative coefficients = -0.278)
>    x2    x1 
> 0.662 0.338 

> ## Qgcomp weights/partial effects at z = 1
> Scaled effect size (positive direction, sum of positive effects = 0.0028)
> x1 
>  1 

> Scaled effect size (negative direction, sum of negative effects = -0.0128)
> x2 
>  1 

> Mixture slope parameters (Delta method CI):

>             Estimate Std. Error Lower CI Upper CI t value  Pr(>|t|)
> (Intercept)  0.58062    0.11142  0.36224  0.79900  5.2112 4.787e-06
> psi1        -0.27807    0.20757 -0.68490  0.12876 -1.3397    0.1872
> z           -0.10410    0.15683 -0.41148  0.20329 -0.6637    0.5103
> z:mixture    0.26811    0.26854 -0.25822  0.79444  0.9984    0.3235

> Estimate (CI), z=1: 
> -0.0099575 (-0.34389, 0.32398)

Current package capabilities/limitations

• Single modifiers only (e.g. interaction terms between a modifier and the mixture can only be estimated for a single modifier at a time). This also implies that no interaction terms between the modifier and other covariates can be considered.
• linear only specifications (i.e. linear effects of a mixture, but not covariates) for Cox model

Interpretation

• coefficients
  • psi1 coefficient: effect of the mixture in the referent category of the effect measure modifier (z in this case)
  • z coefficient: main effect of the effect measure modifier (will change based on name of effect measure modifier)
  • z:mixture coefficient: interaction term between the effect measure modifier and the entire mixture
• weights/partial effects (only given if effect measure modifier is binary)
  • first set of weights/sum of negative/positive coefficients: - weights: proportion of positive/negative partial effect in the reference stratum effect measure modifier (here, z=0)
    • partial effect: sum of main term coefficients for exposure in a given direction (these will sum to psi1, the effect estimate in the reference stratum effect measure modifier (here, z=0))
  • second set:
    • weights: proportion of positive/negative partial effect in the index stratum effect measure modifier (here, z=1)
    • partial effect: sum of main term coefficients + interaction term coefficients for exposure in a given direction (these will sum to the mixture effect estimate in the index stratum effect measure modifier (here, z=1))
• Additional estimates (only given if effect measure modifier is binary): The effect of the mixture in the index category of the effect measure modifier (here, z=1)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.