f_fit_di: Model Fitting for Dispensed Doses

In drugDemand: Drug Demand Forecasting

Model Fitting for Dispensed Doses

Description

Fits a linear mixed-effects model to the dispensed doses at drug dispensing visits.

Usage

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

Arguments

df	The subject-level dosing data, including usubjid, day, kit, and dose.
model	The model used to analyze the dispensed doses, with options including "constant", "linear model", and "linear mixed-effects model".
nreps	The number of simulations for drawing posterior model parameters.
showplot	A Boolean variable that controls whether or not to show the fitted dose 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.

  • mud: The estimated mean dose.

  • vmud: The estimated variance of mud.

  • sigmab: The estimated between-subject standard deviation.

  • sigmae: The estimated within-subject residual standard deviation.

  • aic: The Akaike Information Criterion value.

  • bic: The Bayesian Information Criterion value.

• fit_plot: A fitted dose bar chart.

• theta: Posterior draws of model parameters.

  • fixed: Posterior draws of fixed model parameters: mud, sigmab, and sigmae.

  • random: Posterior draws of subject random effects.

  • usubjid: The unique subject ID associated with the subject random effects.

Author(s)

Kaifeng Lu, [e-mail]

Examples

library(dplyr)

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

vf1 <- vf %>% filter(kit == 3)
di_fit <- f_fit_di(vf1, model = "linear mixed-effects model", nreps = 200)

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