# gammaImpute: Perform gamma-Imputation for a given data set In InformativeCensoring: Multiple Imputation for Informative Censoring

## Description

This function performs the Imputation described in Relaxing the independent censoring assumptions in the Cox proportional hazards model using multiple imputation. (2014) D. Jackson et al. Statist. Med. (33) 4681-4694

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13``` ```gammaImpute( formula, data, m, gamma, gamma.factor, bootstrap.strata = rep(1, nrow(data)), DCO.time, ..., parallel = c("no", "multicore", "snow"), ncpus = 1L, cl = NULL ) ```

## Arguments

 `formula` The model formula to be used when fitting the models to calculate the cumulative hazard. A formula for coxph can include strata terms but not cluster or tt and only right-censored `Surv` objects can be used. Note the function does not allow multiple strata to be written as `strata(W1)+strata(W2)`, use `strata(W1,W2)` instead `data` A time to event data set for which event times are to be imputed `m` The number of imputations to be created `gamma` Either column name containing the value of gamma or a vector of values giving the subject specific size of the step change in the log hazard at censoring. If a subject has NA in this column then no imputation is performed for this subject (i.e. the subject's censored time remains unchanged after imputation). If a subject has already had an event then the value of gamma is ignored. If `gamma.factor` is also used then the subject specific gamma are all multipled by `gamma.factor`. At least one of `gamma` and `gamma.factor` must be included. `gamma.factor` If used, a single numeric value. If no `gamma` then the step change in log hazard for all subjects at censoring is given by `gamma.factor`. If `gamma` is used then for each subject, the step change in log hazard is given by `gamma.factor` multiplied by the subject specific gamma. At least one of `gamma` and `gamma.factor` must be included. `bootstrap.strata` The strata argument for stratified bootstrap sampling, see argument `strata` for the function `boot::boot` for further details. If argument is not used then standard sampling with replacement will be used `DCO.time` Either column name containing the subject's data cutoff time or a vector of DCO.times for the subjects or a single number to be used as the DCO.time for all subjects (if imputed events are > this DCO.time then subjects are censored at DCO.time in imputed data sets) `...` Additional parameters to be passed into the model fit function `parallel` The type of parallel operation to be used (if any). `ncpus` integer: number of processes to be used in parallel operation: typically one would chose this to be the number of available CPUs `cl` An optional parallel or snow cluster for use if `parallel="snow"`. If not supplied, a cluster on the local machine is created for the duration of the call.

## Details

See the Gamma Imputation vignette for further details

## Value

A `GammaImputedSet.object` containing the imputed data sets

`GammaImputedSet.object` `GammaImputedData.object`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25``` ```## Not run: data(nwtco) nwtco <- nwtco[1:500,] #creating 2 imputed data sets (m=2) for speed, would normally create more ans <- gammaImpute(formula=Surv(edrel,rel)~histol + instit, data = nwtco, m=2, gamma.factor=1, DCO.time=6209) #subject specific gamma (multiplied by gamma.factor to give the jump) #NA for subjects that are not to be imputed jumps <- c(rep(NA,10),rep(1,490)) DCO.values <- rep(6209,500) ans.2 <- gammaImpute(formula=Surv(edrel,rel)~histol + instit + strata(stage), data = nwtco, m=2, bootstrap.strata=strata(nwtco\$stage), gamma=jumps, gamma.factor=1, DCO.time=DCO.values) #can also use column names nwtco\$gamma <- jumps nwtco\$DCO.time <- DCO.values ans.3 <- gammaImpute(formula=Surv(edrel,rel)~histol + instit + strata(stage), data = nwtco, m=2, bootstrap.strata=strata(nwtco\$stage), gamma="gamma", DCO.time="DCO.time") ## End(Not run) ```