f_dose_pp {drugDemand}

Drug Demand Per Protocol

Description

Obtains drug demand prediction based on protocol-assumed visit and dosing schedules.

Usage

f_dose_pp(
  dosing_summary_t0,
  vf_ongoing,
  vf_new,
  dosing_schedule_df,
  t0,
  t,
  pilevel
)

Arguments

dosing_summary_t0

A data frame for the cumulative doses dispensed before the cutoff date. It contains the following variables: kit, kit_name, and cum_dose_t0.

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.

dosing_schedule_df

A data frame providing dosing schedule information. It contains the following variables: kit, target_days, target_dose, and max_cycles.

t0

The cutoff date relative to the trial start date.

t

A vector of new time points for drug dispensing prediction.

pilevel

The prediction interval level.

Value

A data frame for dosing summary by drug and time point per protocol. It contains the following variables: kit, kit_name, t, n, pilevel, lower, upper, mean, and var.

Author(s)

Kaifeng Lu, [e-mail]

Examples

# Design stage drug demand forecasting per protocol.

set.seed(312)
library(dplyr)

pred <- eventPred::getPrediction(
  df = NULL,
  to_predict = "enrollment and event",
  target_n = 250,
  target_d = 250,
  enroll_prior = list(
    model = "piecewise poisson",
    theta = c(-0.74, -1.18),
    vtheta = matrix(c(0.0087, 0, 0, 0.0082), 2, 2),
    accrualTime = c(0, 240)),
  event_prior = list(
    list(model = "log-logistic",
         theta = c(5.9, -0.2),
         vtheta = matrix(c(0.022, 0.004, 0.004, 0.012), 2, 2)),
    list(model = "log-logistic",
         theta = c(5.6, 0.02),
         vtheta = matrix(c(0.032, 0.003, 0.003, 0.012), 2, 2)),
    list(model = "log-logistic",
         theta = c(5.7, -0.3),
         vtheta = matrix(c(0.071, 0.013, 0.013, 0.054), 2, 2))),
  dropout_prior = NULL,
  pilevel = 0.95,
  nyears = 3,
  nreps = 200,
  showsummary = FALSE,
  showplot = FALSE,
  by_treatment = TRUE,
  ngroups = 3,
  alloc = c(2, 2, 1),
  treatment_label = c("Drug A + Drug B",
                      "Drug C + Placebo",
                      "Drug A + Placebo"))

dosing_summary_t0 = kit_description_df %>%
  mutate(cum_dose_t0 = 0) %>%
  select(-c("drug", "drug_name"))

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

t0 = 1
nyears = 3
t1 = t0 + nyears*365
t = c(seq(t0, t1, 30), t1)
pilevel = 0.95

dosing_pred_pp <- f_dose_pp(
  dosing_summary_t0, vf_ongoing_new$vf_ongoing,
  vf_ongoing_new$vf_new, dosing_schedule_df, t0, t, pilevel)

head(dosing_pred_pp)