dejaVu0.3.1 package

Multiple Imputation for Recurrent Events

ConstantRateDrop

Create a Dropout Mechanism with constant dropout rate

copy_reference

Create a copy reference ImputeMechanism object

CreateNewDropoutMechanism

A function which creates a DropOut Mechanism object

CreateNewImputeMechanism

A function which creates an Impute Mechanism object

CreateScenario

Create Scenario object from list of Fit Summaries

DejaData.object

Data frame of covariates for simulating recurrent events

DropoutMechanism.object

DropoutMechanism object

expandEventCount

Expand event counts into a list of event times

extract_results

Extract the results of running a scenario

GetImputedDataSet

Output a single imputed data set

ImportSim

Import an existing data frame for use with the package

Impute

Produce imputed data sets

ImputeMechanism.object

ImputeMechanism object

ImputeSim.object

ImputeSim object

ImputeSimFit.object

ImputeSimFit object

LinearRateChangeDrop

Create a Dropout Mechanism with drop out rate which changes by a fixed...

MakeDejaData

Create a DejaData object

numberSubjects

S3 generic to output the number of subjects in a given object

Scenario.object

Scenario object

Simfit

S3 generic for fitting models

SimulateComplete

Simulate a complete data set

SimulateDropout

Simulate subject dropout

SingleSim.object

SingleSim Object

SingleSimFit.object

SingleSimFit object

subjectsPerArm

S3 generic to output the number of subjects in each arm for a given ob...

summary.ImputeSimFit.object

summary.ImputeSimFit object

summary.Scenario.object

summary.Scenario object

summary.SingleSim.object

summary.SingleSim object

summary.SingleSimFit

summary.SingleSimFit

weighted_j2r

Create a weighted_j2r ImputeMechanism object

Performs reference based multiple imputation of recurrent event data based on a negative binomial regression model, as described by Keene et al (2014) <doi:10.1002/pst.1624>.

  • Maintainer: Jonathan Bartlett
  • License: GPL (>= 2)
  • Last published: 2024-07-09