g.dr.dicho: The estimator for the g-formula that utilizes data with...

In mcl868/causalinmisdata: Causal inference in missing data

The estimator for the g-formula that utilizes data with missing observations

Usage

g.dr.dicho(mmodels, exposure, covariates, pattern, regList, data, aug = NULL, ...)

Arguments

mmodels	Models corresponding to response.
exposure	The time-varying exposure.
covariates	The ordered sequence of the variables of the interest without the response.
regList	The list consist of the models to estimate the probabilities for the missingness in data. See the function missing.pattern.
augList	The list consist of the models of the Augmentations space. All the models are linear by default (augList=NULL)
data	Data.
...

Value

ExpectEstimate	The expected value of the potential outcome.
coef	The coefficients in marginal strucktural model.
mmodels	The mmodels that have been used for modeling data.
N	The sample size of data.
NCC	The sample size of complete cases of data. In case of no missing values NCC is equal to N.
exposure	The exposure of the analysis.
augList	The list consist of the models of the Augmentations space. All the models are linear by default (augList=NULL)

Author(s)

Thomas Maltesen [e-mail]

References

put references to the literature/web site here

See Also

See also g.dicho.

Examples

## Not run: 
model1 <- Y ~ L0 + A0 + L1 + A1 + L2 + A2 + L2*A2
model2 <- L2 ~ A1 + L1 + A0 + L0
model3 <- L1 ~ A0 + L0 + A0*L0


estimationSG.NA<-lapply(1:loop,function(iiii) g.dicho(mmodels=c(model1,model2,model3),
                                                      exposure=c("A0","A1","A2"),
                                                      data=DataSetMonotone[[iiii]])$coef)
round(listMean(estimationSG.NA),3)
     (Intercept)    A0    A1    A2 A0*A1  A0*A2  A1*A2 A0*A1*A2
Est.      -0.249 6.443 3.555 5.743     0 -2.551 -0.782        0

estimationMis.SG<-lapply(1:loop,function(iiii) g.dr.dicho(mmodels=c(model1,model2,model3),
                                                          exposure=c("A0","A1","A2"),
                                                          data=DataSetMonotone[[iiii]],
                                                          covariates=c("L0","A0","L1","A1","L2","A2"),
                                                          regList=regList)$coef)
round(listMean(estimationMis.SG),3)
     (Intercept)    A0 A1    A2 A0*A1  A0*A2  A1*A2 A0*A1*A2
Est.      -0.016 6.015  4 5.015     0 -2.004 -1.004        0

## End(Not run)

mcl868/causalinmisdata documentation built on March 5, 2024, 8:22 a.m.