weighted_j2r: Create a weighted_j2r 'ImputeMechanism' object
In scientific-computing-solutions/dejaVu: Multiple Imputation for Recurrent Events

Description Usage Arguments Details Value See Also Examples

View source: R/specificImputeMechanisms.R

Missing counts for a subject in the active treatment arm will be imputed according to a point (determined by trt.weight) between the means of the placebo and treatment arms, conditioned on the number of events. Missing counts for subjects in the placebo arm will be imputed according to the mean of the placebo arm, conditioned on the subject's observed number of events.

1	weighted_j2r(trt.weight, delta = c(1, 1))

`trt.weight`	See details
`delta`	If `trt.weight=1` then delta is a vector of length 2 (control.delta,treatment.delta) and the mean number of expected events for the imputed missing data is multiplied by the appropriate delta

If trt.weight = 0 then imputation using this mechanism will follow the jump to reference (j2r) model whereby missing counts for subjects in both arms will be imputed according to the mean of the placebo arm conditioned on the subject's observed number of events

If trt.weight = 1 then imputation using this mechanism will follow the MAR model whereby missing counts for subjects in each arm will be imputed according to the event rate of subjects in its treatment group conditioned on the subject's observed number of events

See the User guide vignette for further details

An ImputeMechanism object

ImputeMechanism.object

sim <- SimulateComplete(study.time=365,number.subjects=50,
event.rates=c(0.01,0.005),dispersions=0.25)
sim.with.MCAR.dropout <- SimulateDropout(sim,
                     drop.mechanism = ConstantRateDrop(rate = 0.0025))
fit <- Simfit(sim.with.MCAR.dropout)
imps <- Impute(fit, weighted_j2r(trt.weight=0), 10)