f_fit_ki: Model Fitting for Number of Skipped Visits

In drugDemand: Drug Demand Forecasting

Model Fitting for Number of Skipped Visits

Description

Fits a count model to the number of skipped visits between two consecutive drug dispensing visits.

Usage

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

Arguments

df	The subject-level dosing data, including skipped to indicate the number of skipped visits.
model	The count model used to analyze the number of skipped visits, with options including "constant", "poisson", "zero-inflated poisson", and "negative binomial".
nreps	The number of simulations for drawing posterior model parameter values.
showplot	A Boolean variable that controls whether or not to show the fitted count bar chart. It defaults to 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 count bar chart.

• theta: Posterior draws of model parameters.

Author(s)

Kaifeng Lu, [e-mail]

Examples

library(dplyr)

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

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

df_ti <- vf %>%
  mutate(time = lead(day) - day,
         skipped = pmax(floor((time - target_days/2)/target_days), 0),
         k1 = skipped + 1) %>%
  filter(row_id < n())

ki_fit <- f_fit_ki(df_ti, model = "zero-inflated poisson", nreps = 200)

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