predict_with_exposure_plot

Description

Plots model predictions with therapeutic and supra therapeutic Cmax

Usage

predict_with_exposure_plot(
  data,
  fit,
  conc_col,
  treatment_predictors,
  control_predictors = NULL,
  reference_threshold = c(
  10
),
  tpx_cmax = NULL,
  stpx_cmax = NULL,
  conf_int = 0.9,
  xlabel = "Concentration (
  ng/mL
)",
  ylabel = NULL,
  title = ""
)

Arguments

Name	Description
data	a dataframe of QTc dataset
fit	the lme model to make predictions with
conc_col	an unquoted column name of drug concentration measurements
treatment_predictors	list of a values for contrast. CONC will update
control_predictors	list of b values for contrast
reference_threshold	optional vector of numbers to add as horizontal dashed lines
tpx_cmax	Optional - numeric therapeutic dose Cmax
stpx_cmax	Optional - numeric supra therapeutic Cmax
conf_int	confidence interval fraction, default = 0.9
xlabel	xlabel for plot
ylabel	ylabel for plot
title	a string of plot title.

Returns

a plot

Examples

data <- preprocess(data)
 fit <- fit_prespecified_model(
   data,
   deltaQTCF,
   ID,
   CONC,
   deltaQTCFBL,
   TRTG,
   TAFD,
   "REML",
   TRUE
 )
 pk_df <- compute_pk_parameters(
   data %>% dplyr::filter(DOSE != 0), ID, DOSEF, CONC, NTLD)

 predict_with_exposure_plot(
   data,
   fit,
   CONC,
   treatment_predictors = list(
     CONC = 0,
     deltaQTCFBL = 0,
     TRTG = "Verapamil HCL",
     TAFD = "2 HR"
   ),
   control_predictors = list(
     CONC = 0,
     deltaQTCFBL = 0,
     TRTG = "Placebo",
     TAFD = "2 HR"
   ),
   tpx_cmax = pk_df[[1, "Cmax_gm"]], # Dose = 120
 )