Predict With Exposure Plot — predict_with_exposure

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),
  cmaxes = NULL,
  conf_int = 0.9,
  style = list()
)

Arguments

data: A data frame containing C-QT analysis dataset
fit: An nlme::lme model object from model fitting
conc_col: An unquoted column name for 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
cmaxes: Optional - numeric vector of Cmax values to add as reference lines
conf_int: Numeric confidence interval level (default: 0.9)
style: A named list of arguments passed to style_plot()

Value

A plot of model predictions with confidence interval and vertical Cmax reference lines

Examples

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

predict_with_exposure_plot(
  data_proc,
  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"
  ),
  cmaxes = pk_df[[1, "Cmax_gm"]], # Dose = 120
)