bipd (version 0.3)

ipdma.impute: Impute missing data in individual participant data with two treatments (i.e. placebo and a treatment).

Description

Impute missing data in individual participant data with two treatments. Data is clustered by different studies. In the presence of systematically missing variables, the function defaults to 2l.2stage.norm, 2l.2stage.bin, and 2l.2stage.pois methods in micemd package. If there are no systematically missing variables, the function defaults to use 2l.pmm in miceadds package which generalizes predictive mean matching using linear mixed model. If there is only one study available, the function defaults to use pmm in mice package.

Usage

ipdma.impute(
  dataset = NULL,
  covariates = NULL,
  typeofvar = NULL,
  sys_impute_method = "2l.2stage",
  interaction = NULL,
  meth = NULL,
  pred = NULL,
  studyname = NULL,
  treatmentname = NULL,
  outcomename = NULL,
  m = 5
)

Arguments

dataset

data which contains variables of interests

covariates

vector of variable names to find missing data pattern

typeofvar

type of covariate variables; should be a vector of these values: "continuous", "binary", or "count". Order should follow that of covariates parameter specified. Covariates that are specified "binary" are automatically factored.

sys_impute_method

method used for systematically missing studies. Options are "2l.glm", "2l.2stage", or "2l.jomo". Default is set to "2l.2stage". There is also an option to ignore all the clustering level and impute using predictive mean matching by setting this parameter to "pmm".

interaction

indicator denoting whether treatment-covariate interactions should be included. Default is set to true.

meth

imputation method to be used in the mice package. If left unspecified, function picks a reasonable one.

pred

correct prediction matrix to be used in the mice package. If left unspecified, function picks a reasonable one.

studyname

study name in the data specified.

treatmentname

treatment name in the data specified.

outcomename

outcome name in the data specified.

m

number of imputed datasets. Default is set to 5.

Value

missingPattern

missing pattern object returned by running findMissingPattern function

meth

imputation method used with the mice function

pred

prediction matrix used with the mice function

imp

imputed datasets that is returned from the mice function

imp.list

imputed datasets in a list format

Examples

# NOT RUN {
simulated_dataset <- generate_sysmiss_ipdma_example(Nstudies = 10, Ncov = 5, sys_missing_prob = 0.3, 
magnitude = 0.2, heterogeneity = 0.1)
# }
# NOT RUN {
# load in mice packages
library(mice) #for datasets with only one study level
library(miceadds) #for multilevel datasets without systematically missing predictors
library(micemd) #for multilevel datasets with systematically missing predictors.
imputation <- ipdma.impute(simulated_dataset, covariates = c("x1", "x2", "x3", "x4", "x5"), 
typeofvar = c("continuous", "binary", "binary", "continuous", "continuous"), interaction = TRUE, 
studyname = "study", treatmentname = "treat", outcomename = "y", m = 5)
# }