f_dose_new_cpp {drugDemand}

Dosing Date Imputation for New Patients

Description

Imputes the dosing dates for new patients and ongoing patients with no dosing records.

Usage

f_dose_new_cpp(
  usubjid,
  V,
  C,
  D,
  model_k0,
  theta_k0,
  model_t0,
  theta_t0,
  model_t1,
  theta_t1,
  model_ki,
  theta_ki,
  model_ti,
  theta_ti
)

Arguments

usubjid

The unique subject ID.

V

Initialized to 0 and corresponds to the randomization visit.

C

The cutoff date relative to randomization.

D

The discontinuation date relative to randomization.

model_k0

The model for the number of skipped visits between randomization and the first drug dispensing visit. Options include "constant", "poisson", "zero-inflated poisson", and "negative binomial".

theta_k0

The model parameters for the number of skipped visits between randomization and the first drug dispensing visit.

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", "log-logistic", and "log-normal".

theta_t0

The model parameters for the gap time between randomization and the first drug dispensing visit when there is no visit skipping.

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".

theta_t1

The model parameters for the gap time between randomization and the first drug dispensing visit when there is visit skipping.

model_ki

The model for the number of skipped visits between two consecutive drug dispensing visits. Options include "constant", "poisson", "zero-inflated poisson", and "negative binomial".

theta_ki

The model parameters for the number of skipped visits between two consecutive drug dispensing visits.

model_ti

The model for the gap time between two consecutive drug dispensing visits. Options include "least squares" and "least absolute deviations".

theta_ti

The model parameters for the gap time between two consecutive drug dispensing visits.

Value

A data frame with two variables:

• usubjid: The unique subject ID.

• day: The dosing visit date relative to randomization.

Author(s)

Kaifeng Lu, [e-mail]

Examples

set.seed(529)

f_dose_new_cpp(
  usubjid = "Z001", V = 0, C = 87, D = 985,
  model_k0 = "zero-inflated poisson", theta_k0 = c(0.6, 1.1),
  model_t0 = "log-logistic", theta_t0 = c(-1.0, 0.7),
  model_t1 = "least squares", theta_t1 = c(21.5, 1.9),
  model_ki = "zero-inflated poisson", theta_ki = c(0.1, 0.4),
  model_ti = "least squares", theta_ti = c(21, 2.3))