ProperDraws_MC: ProperDraws_MC
In lbeesleyBIOSTAT/MultiCure: EM Algorithms for Fitting Multistate Cure Models

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.

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: Y_R, the recurrence event/censoring time delta_R, the recurrence event/censoring indicator Y_D, the death event/censoring time delta_D, the death event/censoring indicator G, the cure status variable. This takes value 1 for known non-cured, 0 for "known" cured and NA for unknown cure status
`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 user-specified. 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 user-specified. 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 (follow-up) setting. This only needs to be specified when we have unequal censoring. Several default options exist, but this could also be a user-specified 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: 'SameHazard': Lambda_14(t) = Lambda_24(t) 'AllSeparate': No restrictions on Lambda_14(t) and Lambda_24(t) 'ProportionalHazard': Lambda_14(t) = Lambda_24(t) exp(Beta0) 'SameBaseHaz': Lambda^0_14(t) = Lambda^0_24(t), No restrictions on beta_14 and beta_24
`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 post-processing 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

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