mi.boot: Obtaining estimates from multiply imputed data
In CICI: Causal Inference with Continuous (Multiple Time Point) Interventions

mi.boot

R Documentation

Obtaining estimates from multiply imputed data

Description

Combines gformula estimates obtained from multiple imputed data sets according to the MI Boot and MI Boot pooled methods decribed in Schomaker and Heumann (2018, see reference section below)

Usage

mi.boot(x, fun, cond=NULL, pooled=FALSE, ...)

Arguments

`x`	A list of objects of `class` ‘gformula’
`fun`	A function to be applied to the outcome(s) of the counterfactual data set. For expected outcome, use `mean` and possibly pass on option `na.rm=TRUE`.
`cond`	A string containing a condition to be applied to the counterfactual datasets.
`pooled`	Logical. If TRUE, confidence interval estimation is based on the MI Boot pooled from Schomaker and Heumann (2018), otherwise on MI Boot.
`...`	additional arguments to be passed on to `fun`

Value

An object of class gformula. See gformula for details.

Author(s)

Michael Schomaker

References

Schomaker, M., Heumann, C. (2018) Bootstrap inference when using multiple imputation, Statistics in Medicine, 37:2252-2266

Examples


data(EFV)

# suppose the following subsets were actually multiply imputed data (M=2)
EFV_1 <- EFV[1:2500,]
EFV_2 <- EFV[2501:5000,]

# first: conduct analysis on each imputed data set. Set ret=T.
m1 <- gformula(X=EFV_1,
               Lnodes  = c("adherence.1","weight.1",
                           "adherence.2","weight.2",
                           "adherence.3","weight.3",
                           "adherence.4","weight.4"
               ),
               Ynodes  = c("VL.0","VL.1","VL.2","VL.3","VL.4"),
               Anodes  = c("efv.0","efv.1","efv.2","efv.3","efv.4"),
               abar=seq(0,5,1), verbose=FALSE, ret=TRUE
        )

m2 <- gformula(X=EFV_2,
               Lnodes  = c("adherence.1","weight.1",
                           "adherence.2","weight.2",
                           "adherence.3","weight.3",
                           "adherence.4","weight.4"
               ),
               Ynodes  = c("VL.0","VL.1","VL.2","VL.3","VL.4"),
               Anodes  = c("efv.0","efv.1","efv.2","efv.3","efv.4"),
               abar=seq(0,5,1), verbose=FALSE, ret=TRUE
)

# second combine results
m_imp <- mi.boot(list(m1,m2), mean) # uses MI rules & returns 'gformula' object
plot(m_imp)

# custom estimand: evaluate probability of suppression (Y=0), among females
m_imp2 <- mi.boot(list(m1,m2), prop, categ=0, cond="sex==1")
plot(m_imp2)

CICI documentation built on April 3, 2025, 7:08 p.m.