# Drug Dispensing Data Simulation for One Iteration Simulates drug dispensing data for one iteration. ```r f_dose_draw_1( i, common_time_model, k0_fit, t0_fit, t1_fit, ki_fit, ti_fit, di_fit, vf_ongoing, vf_ongoing1, vf_new, vf_new1, vf_kit, l, t ) ``` ## Arguments - `i`: The iteration number. - `common_time_model`: A Boolean variable that indicates whether a common time model is used for drug dispensing visits. - `k0_fit`: The model fit for the number of skipped visits between randomization and the first drug dispensing visit. - `t0_fit`: The model fit for the gap time between randomization and the first drug dispensing visit when there is no visit skipping. - `t1_fit`: The model fit for the gap time between randomization and the first drug dispensing visit when there is visit skipping. - `ki_fit`: The model fit for the number of skipped visits between two consecutive drug dispensing visits. - `ti_fit`: The model fit for the gap time between two consecutive drug dispensing visits. - `di_fit`: The model fit for the dispensed doses at drug dispensing visits. - `vf_ongoing`: A data frame for the observed drug dispensing data for ongoing patients with drug dispensing records. It includes the following variables: `draw`, `kit`, `kit_name`, `usubjid`, `day`, `dose`, `arrivalTime`, `treatment`, `treatment_description`, `time`, and `totalTime`. - `vf_ongoing1`: A data frame for the last observed drug dispensing date for ongoing patients with drug dispensing records. For the common time model, it includes the following variables: `draw`, `usubjid`, `arrivalTime`, `treatment`, `treatment_description`, `time`, `totalTime`, `V`, `C`, and `D`. For separate time models, it includes the following variables: `draw`, `kit`, `kit_name`, `usubjid`, `arrivalTime`, `treatment`, `treatment_description`, `time`, `totalTime`, `V`, `C`, and `D`. - `vf_new`: A data frame for the randomization date for new patients and ongoing patients with no drug dispensing records. It includes the following variables: `draw`, `kit`, `kit_name`, `usubjid`, `arrivalTime`, `treatment`, `treatment_description`, `time`, and `totalTime`. - `vf_new1`: A data frame for the randomization date for new patients and ongoing patients with no drug dispensing records. For the common time model, it includes the following variables: `draw`, `usubjid`, `arrivalTime`, `treatment`, `treatment_description`, `time`, `totalTime`, `V`, `C`, and `D`. For separate time models, it includes the following variables: `draw`, `kit`, `kit_name`, `usubjid`, `arrivalTime`, `treatment`, `treatment_description`, `time`, `totalTime`, `V`, `C`, and `D`. - `vf_kit`: A data frame indicating the kit names for each subject by draw. It includes the following variables: `draw`, `usubjid`, `kit`, and `kit_name`. - `l`: Number of kit types. - `t`: A vector of new time points for drug dispensing prediction. ## Returns A list of two components: * `dosing_subject_newi`: A data frame for the drug dispensing data at the subject level by date for ongoing and new subjects for the given iteration. It contains the following variables: `draw`, `kit`, `kit_name`, `usubjid`, `day`, `dose`, `arrivalTime`, `treatment`, `treatment_description`, `time`, and `totalTime`. * `dosing_summary_newi`: A data frame for the drug dispensing summary data by drug, time, and simulation draw for ongoing and new subjects for the given iteration. It includes the following variables: `kit`, `kit_name`, `t`, `draw`, and `total_dose_b`. ## Examples ```r set.seed(431) library(dplyr) pred <- eventPred::getPrediction( df = df2, to_predict = "event only", target_d = 250, event_model = "log-logistic", dropout_model = "none", pilevel = 0.95, nyears = 3, nreps = 200, showsummary = FALSE, showplot = FALSE, by_treatment = TRUE) observed <- f_dose_observed(df2, visitview2, showplot = FALSE) fit <- f_dispensing_models( observed$vf, dosing_schedule_df, model_k0 = "zero-inflated poisson", model_t0 = "log-logistic", model_t1 = "least squares", model_ki = "zero-inflated poisson", model_ti = "least squares", model_di = "linear mixed-effects model", nreps = 200, showplot = FALSE) trialsdt = df2$trialsdt[1] cutoffdt = df2$cutoffdt[1] t0 = as.numeric(cutoffdt - trialsdt + 1) nyears = 3 t1 = t0 + nyears*365 t = c(seq(t0, t1, 30), t1) l = nrow(observed$kit_description_df) vf_ongoing_new <- f_ongoing_new( pred$event_pred$newEvents, observed$kit_description_df, observed$treatment_by_drug_df, observed$vf) vf_ongoing <- vf_ongoing_new$vf_ongoing vf_new <- vf_ongoing_new$vf_new vf_kit <- vf_ongoing %>% select(-c("day", "dose")) %>% bind_rows(vf_new) %>% group_by(draw, usubjid, kit, kit_name) %>% slice(1) %>% select(c("draw", "usubjid", "kit", "kit_name")) vf_ongoing1 <- vf_ongoing %>% group_by(draw, usubjid) %>% slice(n()) %>% mutate(V = day - 1, C = as.numeric(t0 - arrivalTime), D = pmin(time - 1, t1 - arrivalTime)) %>% select(-c("kit", "kit_name", "day", "dose")) ### new patients and ongoing patients with no dosing records ### vf_new1 <- vf_new %>% group_by(draw, usubjid) %>% slice(n()) %>% mutate(V = 0, C = as.numeric(t0 - arrivalTime), D = pmin(time - 1, t1 - arrivalTime)) %>% select(-c("kit", "kit_name")) # first iteration to extract subject and summary data list1 <- f_dose_draw_1( 1, fit$common_time_model, fit$k0_fit, fit$t0_fit, fit$t1_fit, fit$ki_fit, fit$ti_fit, fit$di_fit, vf_ongoing, vf_ongoing1, vf_new, vf_new1, vf_kit, l, t) head(list1$dosing_subject_newi) head(list1$dosing_summary_newi) ``` ## See Also `f_fit_t0`, `f_fit_ki`, `f_fit_ti`, `f_fit_di` ## Author(s) Kaifeng Lu, kaifenglu@gmail.com

f_dose_draw_1 function