f_dispensing_models {drugDemand}  R Documentation 
Drug Dispensing Model Fitting
Description
Fits drug dispensing models to the observed drug dispensing data.
Usage
f_dispensing_models(
vf,
dosing_schedule_df,
model_k0,
model_t0,
model_t1,
model_ki,
model_ti,
model_di,
nreps,
showplot = TRUE
)
Arguments
vf 
A data frame for subjectlevel drug dispensing data,
including the following variables:

dosing_schedule_df 
A data frame providing dosing schedule
information. It contains the following variables:

model_k0 
The model for the number of skipped visits between randomization and the first drug dispensing visit. Options include "constant", "poisson", "zeroinflated poisson", and "negative binomial". 
model_t0 
The model for the gap time between randomization and the first drug dispensing visit when there is no visit skipping. Options include "constant", "exponential", "weibull", "loglogistic", and "lognormal". 
model_t1 
The model for the gap time between randomization and the first drug dispensing visit when there is visit skipping. Options include "least squares", and "least absolute deviations". 
model_ki 
The model for the number of skipped visits between two consecutive drug dispensing visits. Options include "constant", "poisson", "zeroinflated poisson", and "negative binomial". 
model_ti 
The model for the gap time between two consecutive drug dispensing visits. Options include "least squares" and "least absolute deviations". 
model_di 
The model for the dispensed doses at drug dispensing visits. Options include "constant", "linear model", and "linear mixedeffects model". 
nreps 
The number of simulations for drawing posterior model parameters. 
showplot 
A Boolean variable that controls whether or not to
show the model fit plot. It defaults to 
Value
A list with the following components:

common_time_model
: A Boolean variable that indicates whether a common time model is used for drug dispensing visits. 
k0_fit
: The model fit for the number of skipped visits between randomization and the first drug dispensing visit. 
t0_fit
: The model fit for the gap time between randomization and the first drug dispensing visit when there is no visit skipping. 
t1_fit
: The model fit for the gap time between randomization and the first drug dispensing visit when there is visit skipping. 
ki_fit
: The model fit for the number of skipped visits between two consecutive drug dispensing visits. 
ti_fit
: The model fit for the gap time between two consecutive drug dispensing visits. 
di_fit
: The model fit for the dispensed doses at drug dispensing visits.
Author(s)
Kaifeng Lu, kaifenglu@gmail.com
See Also
f_fit_t0
, f_fit_ki
,
f_fit_ti
, f_fit_di
Examples
library(dplyr)
observed < f_dose_observed(df2, visitview2, showplot = FALSE)
dispensing_models < f_dispensing_models(
observed$vf, dosing_schedule_df,
model_k0 = "zeroinflated poisson",
model_t0 = "loglogistic",
model_t1 = "least squares",
model_ki = "zeroinflated poisson",
model_ti = "least squares",
model_di = "linear mixedeffects model",
nreps = 200, showplot = FALSE)
dispensing_models$ki_fit$fit_plot