drugDemand (version 0.1.3)

f_fit_t0: Model Fitting for Dispensing Delay After Randomization

Description

Fits a specified time-to-event model to the gap time between randomization and the first drug dispensing visit when there is no visit skipping.

Usage

f_fit_t0(df, model, nreps, showplot = TRUE)

Value

A list with three components:

• fit: A list of results from the model fit that includes

  • model: The specific model used in the analysis.

  • theta: The estimated model parameters.

  • vtheta: The estimated covariance matrix of theta.

  • aic: The Akaike Information Criterion value.

  • bic: The Bayesian Information Criterion value.

• fit_plot: A fitted time-to-event bar chart.

• theta: Posterior draws of model parameters.

Arguments

df

The subject-level dosing data, including the following variables:

• time: The number of days between randomization and the first drug dispensing visit (first drug dispensing visit date - randomization date + 1).

• left: Equals time - 1, used to indicate the left endpoint of an interval for interval censoring.

• right: Equals time, used to indicate the right endpoint of an interval for interval censoring.

model

The event model used to analyze the gap time between randomization and the first drug dispensing visit when there is no visit skipping, with options including "constant", "exponential", "weibull", "log-logistic", and "log-normal".

nreps

The number of simulations for drawing posterior model parameter values.

showplot

A Boolean variable that controls whether or not to show the fitted time-to-event bar chart. It defaults to TRUE.

Author

Kaifeng Lu,

Examples

library(dplyr)

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

vf <- vf %>% left_join(dosing_schedule_df, by = "kit")

# time from randomization to the first drug dispensing visit
df_k0 <- vf %>%
  filter(row_id == 1) %>%
  mutate(time = day,
         skipped = floor((time - target_days/2)/target_days) + 1)

# no skipping
df_t0 <- df_k0 %>%
  filter(skipped == 0) %>%
  mutate(left = time - 1, right = time)

t0_fit <- f_fit_t0(df_t0, model = "log-logistic", nreps = 200)