f_dose_draw: Drug Dispensing Data Simulation

In drugDemand: Drug Demand Forecasting

Drug Dispensing Data Simulation

Description

Simulates drug dispensing data after cutoff for both ongoing and new patients.

Usage

f_dose_draw(
  vf_ongoing,
  vf_new,
  common_time_model,
  k0_fit,
  t0_fit,
  t1_fit,
  ki_fit,
  ti_fit,
  di_fit,
  t0,
  t,
  ncores_max
)

Arguments

vf_ongoing	A data frame for the observed drug dispensing data for ongoing patients with drug dispensing records. It includes the following variables: draw, kit, kit_name, usubjid, day, dose, arrivalTime, treatment, treatment_description, time, and totalTime.
vf_new	A data frame for the randomization date for new patients and ongoing patients with no drug dispensing records. It includes the following variables: draw, kit, kit_name, usubjid, arrivalTime, treatment, treatment_description, time, and totalTime.
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.
t0	The cutoff date relative to the trial start date.
t	A vector of new time points for drug dispensing prediction.
ncores_max	The maximum number of cores to use for parallel computing. The actual number of cores used is the minimum of ncores_max and half of the detected number of cores.

Value

A list with two components:

• dosing_subject_new: A data frame containing observed and imputed subject-level dosing records for ongoing and new patients for the first iteration. It contains the following variables: draw, kit, kit_name, usubjid, day, dose, arrivalTime, treatment, treatment_description, time, and totalTime.

• dosing_summary_new: A data frame providing dosing summaries by drug, future time point, and simulation draw for ongoing and new patients. It contains the following variables: kit, kit_name, t, draw, and total_dose_b.

Author(s)

Kaifeng Lu, [e-mail]

See Also

f_fit_t0, f_fit_ki, f_fit_ti, f_fit_di

Examples

set.seed(431)
library(dplyr)

pred <- eventPred::getPrediction(
  df = df2,
  to_predict = "event only",
  target_d = 250,
  event_model = "log-logistic",
  dropout_model = "none",
  pilevel = 0.95,
  nyears = 3,
  nreps = 200,
  showsummary = FALSE,
  showplot = FALSE,
  by_treatment = TRUE)

observed <- f_dose_observed(df2, visitview2, showplot = FALSE)

fit <- f_dispensing_models(
  observed$vf, dosing_schedule_df,
  model_k0 = "zero-inflated poisson",
  model_t0 = "log-logistic",
  model_t1 = "least squares",
  model_ki = "zero-inflated poisson",
  model_ti = "least squares",
  model_di = "linear mixed-effects model",
  nreps = 200, showplot = FALSE)

trialsdt = df2$trialsdt[1]
cutoffdt = df2$cutoffdt[1]
t0 = as.numeric(cutoffdt - trialsdt + 1)
nyears = 3
t1 = t0 + nyears*365
t = c(seq(t0, t1, 30), t1)

vf_ongoing_new <- f_ongoing_new(
  pred$event_pred$newEvents,
  observed$kit_description_df,
  observed$treatment_by_drug_df,
  observed$vf)

dose_draw <- f_dose_draw(
  vf_ongoing_new$vf_ongoing,
  vf_ongoing_new$vf_new,
  fit$common_time_model,
  fit$k0_fit, fit$t0_fit, fit$t1_fit,
  fit$ki_fit, fit$ti_fit, fit$di_fit,
  t0, t, ncores_max = 2)

head(dose_draw$dosing_subject_new)
head(dose_draw$dosing_summary_new)

drugDemand documentation built on May 29, 2024, 8:43 a.m.