Description Usage Arguments Details Value Examples
The function ProperDraws_MC is used to obtain "proper" imputations of the missing data after the MCEM algorithm is used to fit the multistate cure model. These proper imputations are then used in the functions MultiCure_VAREST_Imputation or MultiCure_VAREST_ImputationBOOT to estimate the parameter standard errors.
1 2 3 4  ProperDraws_MC(datWIDE, Cov, CovImp, GImp, YRImp, deltaRImp,
COVIMPUTEFUNCTION = NULL, COVIMPUTEINITIALIZE = NULL,
UNEQUALCENSIMPUTE = NULL, ASSUME = "SameHazard", TransCov, BASELINE,
PENALTY = "None", POSTITER = 5)

datWIDE 
A data frame with the following columns:

Cov 
matrix of covariates used in MultiCure (may have missingness) 
CovImp 
A list with IMPNUM elements containing the imputations of Cov output from MultiCure 
GImp 
A matrix with IMPNUM elements containing the imputations of G output from MultiCure 
YRImp 
A matrix with IMPNUM elements containing the imputations of Y_R output from MultiCure 
deltaRImp 
A matrix with IMPNUM elements containing the imputations of delta_R output from MultiCure 
COVIMPUTEFUNCTION 
This is a function for creating a single imputed version of the covariate set when covariate imputation is needed. This is userspecified. See XXXXXX for an example of the input and output structure. 
COVIMPUTEINITIALIZE 
This is a function for initializing the missing values of the covariates. This is userspecified. See XXXXXX for an example of the input and output structure. 
UNEQUALCENSIMPUTE 
This is a function for imputing the outcome data in the unequal censoring (followup) setting. This only needs to be specified when we have unequal censoring. Several default options exist, but this could also be a userspecified function. Inputs and outputs must match default versions. 
ASSUME 
This variables indicates what equality assumptions we are making regarding the 24 and 14 transitions. The possible options are:

TransCov 
a list with elements: Trans13, Trans24, Trans14, Trans34, PNonCure. Each list element is a vector containing the names of the variables in Cov to be used in the model for the corresponding transition. 13 is NonCured > Recurrence, 24 is Cured > Death, 14 is NonCured > Death, 34 is Recurrence > Death. PNonCure contains the names of the covariates for the logistic regression for P(NonCure). 
BASELINE 
This variable indicates the assumptions about the baseline hazard form. This can take values 'weib' and 'cox' 
PENALTY 
This variable indicates whether we are using any variable selection in the model fitting. Right now, the options are 'None' (no variable selection), 'Ridge' (ridge regression for all covariates in all models) and 'Lasso' (lasso for all covariates in all models, only implemented for Cox baseline hazards) 
POSTITER 
This variable indicates the number of postprocessing steps should be done. The default is 5. 
In order to output the imputed data from MultiCure, one must use the trace = TRUE option in MultiCure.
OUT a matrix containing the following:
CovImp A list with IMPNUM elements containing "proper" imputations of Cov
GImp A list with IMPNUM elements containing "proper" imputations of G
YRImp A list with IMPNUM elements containing "proper" imputations of Y_R
deltaRImp A list with IMPNUM elements containing "proper" imputations of delta_R
1 2 3 4 5 6 7  attach(SimulateMultiCure(type = "UnequalCensoring"))
Cov = data.frame(X1,X2)
VARS = names(Cov)
TransCov = list(Trans13 = VARS, Trans24 = VARS, Trans14 = VARS, Trans34 = VARS, PNonCure = VARS)
datWIDE = data.frame( Y_R, Y_D, delta_R , delta_D, G)
fit = MultiCure(iternum = 100, datWIDE, Cov, ASSUME = "SameHazard", TransCov = TransCov, BASELINE = "weib", IMPNUM = 10) ### Note: This will take a moment
Proper = ProperDraws_MC(datWIDE,Cov, CovImp = fit[[9]], GImp = fit[[10]], YRImp = fit[[11]], deltaRImp = fit[[12]], ASSUME = "SameHazard", TransCov = TransCov, BASELINE = "weib") ### Note: This will take a moment

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