Package index • cqtkit

All functions
`add_error_bars_to_plot()`	adds errorbars to a plot
`add_horizontal_references()`	adds horizontal reference lines to plot
`add_secondary_data()`	adds secondary data to a plot
`compute_conc_for_upper_pred()`	Computes the concentration needed for a upper conf_int prediction of the threshold value.
`compute_contrast_observations()`	Compute contrast observations for prediction plots
`compute_dataset_simulation()`	Simulates a dataset used to fit the model.
`compute_delta_hrblm()`	computes delta HR BL Mean
`compute_delta_qtcbblm()`	Computes Baseline Mean QTCB
`compute_delta_qtcfblm()`	Computes Baseline Mean QTCF
`compute_deltas()`	Computes delta variables RR, QTCF, HR, etc
`compute_ecg_param_summary()`	Generates a tibble of summary of QTc, dQTc and ddQTc over time stratified by dose
`compute_enGRI()`	Exposure Normalized GRI computation
`compute_exposure_predictions()`	Predicts dQTC over range of concentration values with contrast. To help keep the predictions quick, the concentration values to predict at are done at on order of magnitude of concentration values, if max(conc) = 7340, then by = 100
`compute_fit_results()`	computes all fitted results and residuals for GOF plots
`compute_grouped_mean_sd()`	computes a the mean and standard deviation of the dependent variable data in DV_col grouped by time and dose.
`compute_high_qtc_sub()`	Computes the number of subjects with QTc > 450, 500 as well as deltaQTc > 30, 60
`compute_hysteresis_labeller()`	Gets labeller for hysteresis_loop_plot. Should most likely not be used by user
`compute_lm_fit_df()`	Fits a linear model of input dataframe
`compute_lme_slope_df()`	Takes the slope from a lme model and its confidence interval
`compute_loess_linear_r_squared()`	Computes R-squared and Adjusted R-squared of loess regression compared to linear regression. Used for determining linearity of C-QT data
`compute_model_fit_parameters()`	Converts tTable of summary(model_fit) to tibble and adds CIs.
`compute_pk_parameters()`	Generates pk parameters Cmax and Tmax for exporsure predictions.
`compute_potential_hysteresis()`	Detects the pressence of hysteresis
`compute_qtcb_qtcf()`	Computes QTCF and QTCB from qt_col and rr_col and QTCFBL and QTCBBL from qtbl_col and rrbl_col
`compute_quantiles_obs_df()`	returns a dataframe of quantiles of concentrations and deltaQTcs
`compute_study_summary()`	Creates a dataframe summarizing number of subjects in each trtreatment group
`compute_summary_statistics_of_simulations()`	Wrapper for calling compute_dataset_simulation nruns time and computing summary statsitics of the simulations
`cqtkit_data_bl_dofetilide`	Baseline data for dofetilide C-QT study
`cqtkit_data_bl_quinidine`	Baseline data for quinidine C-QT study
`cqtkit_data_bl_ranolazine`	Baseline data for ranolazine C-QT study
`cqtkit_data_bl_verapamil`	Baseline data for verapamil C-QT study
`cqtkit_data_dofetilide`	C-QT analysis dataset for dofetilide with significant QTc effect (~38 ms prolongation)
`cqtkit_data_quinidine`	C-QT analysis dataset for quinidine with significant QTc effect (~50 ms prolongation)
`cqtkit_data_ranolazine`	C-QT analysis dataset for ranolazine with moderate QTc effect (~14 ms prolongation)
`cqtkit_data_verapamil`	C-QT analysis dataset for verapamil with minimal QTc effect (~8 ms prolongation)
`eda_hysteresis_loop_plot()`	Hysteresis loop plot to visually inspect hysteresis
`eda_mean_dv_over_time()`	Plots mean dependent variable over time
`eda_qt_rr_plot()`	Plot QT against RR
`eda_qtc_comparison_plot()`	plots different corrections of QT against RR to compare which to use.
`eda_quantiles_plot()`	plots the observed decile-decile scatter plot of x-data vs y-data with linear regression.
`eda_scatter_with_regressions()`	plots scatter plot with with linear and loess regressions. Can be used to check for linearity.
`fit_prespecified_model()`	generates nlme::lme model either prespecified or without TRT and TIME.
`fit_qtc_linear_model()`	Fits QT(c) data to linear mixed effects model with fixed effects of intercept and RR slope, with random effects on intercept and slope.
`gof_concordance_plots()`	Concordance plots between population and individual predictions
`gof_plots()`	Makes goodness of fit plots
`gof_qq_plots()`	Plots QQ plot of WRES and IWRES
`gof_residuals_plots()`	Plots residuals vs predicted dQTCF and concentration
`gof_residuals_time_boxplots()`	plots boxplots of residuals over Nominal Times
`gof_residuals_trt_boxplots()`	generates boxplots for treatment group
`gof_vpc_plot()`	Performs a visual predictive check and plots the results
`predict_with_exposure_plot()`	Plots model predictions with therapeutic and supra therapeutic Cmax
`predict_with_observations_plot()`	Plots predictions of the model with observed values
`predict_with_quantiles_plot()`	Plots predictions and 90% CI
`preprocess()`	Pre-processes data Computes QTcB, QTcF, deltaQTcF, deltaQTcB, deltaHR, deltaQTcB Baseline Mean, deltaQTcF Baseline Mean, deltaHR Baseline Mean
`set_style()`	Creates a style list for eda graphing functions
`style_plot()`	Styles a plot with provided colors and labels
`tabulate_ecg_param_summary()`	Generates a gt table of summary of QTc, dQTc and ddQTc over time stratified by dose
`tabulate_exposure_predictions()`	Tablulates exposure predictions at therapeutic and supratherapuetic Cmax.
`tabulate_high_qtc_sub()`	Tabulates number of high QTc/deltaQTc observations.
`tabulate_model_fit_parameters()`	Generates table of model parameter esitmates and statistics
`tabulate_pk_parameters()`	Converts pk_parameters df into gt table for printing
`tabulate_study_summary()`	Creates a gt table of study summary for number of subjects in each grouping

Reference

All functions