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drugDemand 0.1.3

• remove the dependence on pscl and icenReg packages
• add stringr to imported packages
• add survival::survreg and stats::optim to imported functions
• use the Brent method for optimization to fit an interval-censored exponential distribution to the time from randomization to the first drug dispensing visit
• check the number of unique combinations of time and k1 in f_fit_ti
• use the VarCorr function from the nlme package to obtain the variance of random effects from the lme function call
• use data from all kit types to inform the common time model
• add the pred_pp_only parameter to f_drug_demand to make protocol-based predictions only
• ensure that df and visitview have all the required columns and none of the required columns have missing values
• combine dosing_pred_df and dosing_pred_pp in the f_drug_demand output
• handle cases where all patients in a treatment arm discontinued before the cutoff
• replace round with formatC to retain the zeros after the decimal point
• rename fit_xx to xx_fit, where xx = k0, t0, t1, ki, ti, di to be consistent with enroll_fit and event_fit naming convention
• add kit and kit_name to handle cases when a drug has different kit types depending on the site
• add the prior probability of different kit types within a drug at the design stage
• streamline the examples through the use of function calls
• update the initial parameter values in f_fit_ki for zero-inflated Poisson model
• update the p.fit calculation in f_fit_ti when using the least absolute deviations model
• convert kit_name to a factor to ensure the correct order when creating a plot
• add kit_name as the sub plot title in f_dispensing_models.R
• add the vf_kit parameter to f_dose_draw_1 to get the kit information for each subject in each simulation draw
• add the f_ongoing_new function to prepare the dosing data sets to impute for ongoing and new subjects
• add kit_description_df to the output of f_dose_observed
• add the rdirichlet function to generate cell probabilities from the Dirichlet distribution
• remove the f_treatment_by_drug_df function
• replace the treatment_by_drug matrix with the treatment_by_drug_df data frame
• add kit_description_df, treatment_by_drug_df, and dosing_schedule_df to the f_drug_demand output

drugDemand 0.1.2

• add least absolute deviations (LAD) regression as an option for modelling the gap time between drug dispensing visits and rename the original linear model as least squares (LS)
• use more descriptive names for drug dispensing models

drugDemand 0.1.1

• add a reference for parametric analysis of interval-censored survival data
• only keep one record per subject and drug dispensing day when using a common time model
• rename f_dosing_draw, f_dosing_draw_1, and f_dosing_draw_t_1 to f_dose_draw, f_dose_draw_1, and f_dose_draw_t_1, respectively
• remove the vf_new parameter of the f_dose_draw_1 function
• remove the nreps parameter from the f_drug_demand function
• add the f_dose_observed function and incorporate it in the f_drug_demand function
• modify the condition “Vi + Ti <= D(i)” to “Vi + Ti < D(i)” in the f_dose_ongoing_cpp and f_dose_new_cpp functions
• change “as.numeric(exp(attr(a\(apVar, "Pars")))" to "exp(as.numeric(attr(a\)apVar,”Pars“)))” in the f_fit_di function to avoid the error for non-numeric argument to mathematical function
• simplify the condition for common_time_model to “length(unique(target_days)) == 1”
• add dosing_summary_t0 to the output of the f_drug_demand function
• replace mutate and slice(n()) with summarise in the f_dose_observed and f_dose_draw functions to improve efficiency
• plot gap time t0 instead of t0 + 1 in the f_fit_t0 function
• plot the rounded value of di based on probability calculations alongside the observed value of di in the f_fit_di function
• replace the residual plot with the fitted gap time bar chart in the f_fit_ti function
• redefine row_id for vf1 if common_time_model is true in the f_dispensing_models function
• use df and visitview to derive treatment_by_drug_df for real-time drug demand forecasting
• update the examples of the f_fit_t0, f_fit_ki, and f_fit_ti functions
• add trialsdt and cutoffdt to the output of the f_dose_observed and f_drug_demand functions
• move the arrange operation of dosing_subject_newi out of the f_dose_draw_1 function into the f_dose_draw function to improve efficiency
• combine the two summarise operation of dosing_summary_newi in the f_dose_draw_1 function to improve efficiency
• replace the zero-inflated negative binomial distribution with the negative binomial distribution in the f_fit_ki function to avoid convergence issues
• print cum_dispense_plot if showplot is TRUE in the f_dose_observed function
• add colors = “Set2” to cum_dispense_plot, bar_t0_plot, bar_ti_plot, and bar_di_plot in the f_dose_observed function
• remove the custom legend of cum_dispense_plot in the f_dose_observed and f_drug_demand functions
• add parameter l to the f_dose_draw_1 function to improve efficiency
• add structure and more details to the function parameters and output descriptions
• combine the dosing_subject_t and dosing_summary_t steps in the f_dose_observed function to improve efficiency
• replace target_days with dosing_schedule_df in the argument for the f_dispensing_models function
• drop the creation of the status variable and use table instead of survfit for observed data summary in the f_fit_t0 function
• add the handling of one observation case in the f_fit_ti function
• remove the creation of the unames1 variable in the f_dose_draw function

drugDemand 0.1.0

• initial release

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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