publish.MIresult: Present logistic regression and Cox regression obtained with...

In Publish: Format Output of Various Routines in a Suitable Way for Reports and Publication

Present logistic regression and Cox regression obtained with mitools::MIcombine based on smcfcs::smcfcs multiple imputation analysis

Description

Regression tables after multiple imputations

Usage

## S3 method for class 'MIresult'
publish(
  object,
  confint.method,
  pvalue.method,
  digits = c(2, 4),
  print = TRUE,
  factor.reference = "extraline",
  intercept,
  units = NULL,
  fit,
  data,
  ...
)

Arguments

object	Object obtained with mitools::MIcombine based on smcfcs::smcfcs multiple imputation analysis
confint.method	No options here. Only Wald type confidence intervals.
pvalue.method	No options here. Only Wald type tests.
digits	Rounding digits for all numbers but the p-values.
print	If FALSE suppress printing of the results
factor.reference	Style for showing results for categorical. See regressionTable.
intercept	See regressionTable.
units	See regressionTable.
fit	One fitted model using the same formula as object. This can be the fit to the complete case data or the fit to one of the completed data. It is used to get xlevels, formula and terms. For usage see examples. is used to fit
data	Original data set which includes the missing values
...	passed to summary.regressionTable, labelUnits and publish.default.

Details

Show results of smcfcs based multiple imputations of missing covariates in publishable format

Author(s)

Thomas A. Gerds <[e-mail]>

Examples

## Not run: 
if (requireNamespace("riskRegression",quietly=TRUE)
  & requireNamespace("mitools",quietly=TRUE)
  & requireNamespace("smcfcs",quietly=TRUE)){
library(riskRegression)
library(mitools)
library(smcfcs)
## continuous outcome: linear regression
# lava some data with missing values
set.seed(7)
d=sampleData(78)
## generate missing values
d[X1==1,X6:=NA] 
d[X2==1,X3:=NA]
d=d[,.(X8,X4,X3,X6,X7)]
sapply(d,function(x)sum(is.na(x)))

# multiple imputation (should set m to a large value)

set.seed(17)
f= smcfcs(d,smtype="lm",
           smformula=X8~X4+X3+X6+X7,
           method=c("","","logreg","norm",""),m=3)
ccfit=lm(X8~X4+X3+X6+X7,data=d)
mifit=MIcombine(with(imputationList(f$impDatasets),
                lm(X8~X4+X3+X6+X7)))
publish(mifit,fit=ccfit,data=d)
publish(ccfit)

## binary outcome
# lava some data with missing values
set.seed(7)
db=sampleData(78,outcome="binary")
## generate missing values
db[X1==1,X6:=NA] 
db[X2==1,X3:=NA]
db=db[,.(Y,X4,X3,X6,X7)]
sapply(db,function(x)sum(is.na(x)))

# multiple imputation (should set m to a large value)
set.seed(17)
fb= smcfcs(db,smtype="logistic",
           smformula=Y~X4+X3+X6+X7,
           method=c("","","logreg","norm",""),m=2)
ccfit=glm(Y~X4+X3+X6+X7,family="binomial",data=db)
mifit=MIcombine(with(imputationList(fb$impDatasets),
                glm(Y~X4+X3+X6+X7,family="binomial")))
publish(mifit,fit=ccfit)
publish(ccfit)

## survival: Cox regression
library(survival)
# lava some data with missing values
set.seed(7)
ds=sampleData(78,outcome="survival")
## generate missing values
ds[X5==1,X6:=NA] 
ds[X2==1,X3:=NA]
ds=ds[,.(time,event,X4,X3,X6,X7)]
sapply(ds,function(x)sum(is.na(x)))

set.seed(17)
fs= smcfcs(ds,smtype="coxph",
           smformula="Surv(time,event)~X4+X3+X6+X7",
           method=c("","","","logreg","norm",""),m=2)
ccfit=coxph(Surv(time,event)~X4+X3+X6+X7,data=ds)
mifit=MIcombine(with(imputationList(fs$impDatasets),
                coxph(Surv(time,event)~X4+X3+X6+X7)))
publish(mifit,fit=ccfit,data=ds)
publish(ccfit)

## competing risks: Cause-specific Cox regression 
library(survival)
# lava some data with missing values
set.seed(7)
dcr=sampleData(78,outcome="competing.risks")
## generate missing values
dcr[X5==1,X6:=NA] 
dcr[X2==1,X3:=NA]
dcr=dcr[,.(time,event,X4,X3,X6,X7)]
sapply(dcr,function(x)sum(is.na(x)))

set.seed(17)
fcr= smcfcs(dcr,smtype="compet",
           smformula=c("Surv(time,event==1)~X4+X3+X6+X7",
                       "Surv(time,event==2)~X4+X3+X6+X7"),
           method=c("","","","logreg","norm",""),m=2)
## cause 2 
ccfit2=coxph(Surv(time,event==2)~X4+X3+X6+X7,data=dcr)
mifit2=MIcombine(with(imputationList(fcr$impDatasets),
                coxph(Surv(time,event==2)~X4+X3+X6+X7)))
publish(mifit2,fit=ccfit2,data=dcr)
publish(ccfit2)
}

## End(Not run) 

Publish documentation built on Jan. 18, 2023, 1:08 a.m.