ggcontinuousexpdist {ggquickeda}

Create a continuous exposure fit plot

Description

Produces a logistic fit plot with a facettable exposures/quantiles/distributions in ggplot2

Usage

ggcontinuousexpdist(
  data = effICGI,
  response = "response",
  endpoint = "Endpoint",
  DOSE = "DOSE",
  color_fill = "DOSE",
  exposure_metrics = c("AUC", "CMAX"),
  exposure_metric_split = c("median", "tertile", "quartile", "none"),
  exposure_metric_soc_value = -99,
  exposure_metric_plac_value = 0,
  exposure_distribution = c("distributions", "lineranges", "none"),
  dose_plac_value = "Placebo",
  xlab = "Exposure Values",
  ylab = "Probability of Response",
  mean_text_size = 5,
  mean_obs_bydose = TRUE,
  N_text_size = 5,
  binlimits_text_size = 5,
  binlimits_ypos = -Inf,
  binlimits_color = "gray70",
  dist_position_scaler = 0.2,
  dist_offset = 0,
  lineranges_ypos = -1,
  lineranges_dodge = 1,
  yproj = TRUE,
  yproj_xpos = 0,
  yproj_dodge = 0.2,
  yaxis_position = c("left", "right"),
  facet_formula = NULL,
  theme_certara = TRUE
)

Arguments

data

Data to use with multiple endpoints stacked into Endpoint(endpoint name), response 0/1

response

name of the column holding the valuesresponse 0/1

endpoint

name of the column holding the name/key of the endpoint default to Endpoint

DOSE

name of the column holding the DOSE values default to DOSE

color_fill

name of the column to be used for color/fill default to DOSE column

exposure_metrics

name(s) of the column(s) to be stacked into expname exptile and split into exposure_metric_split

exposure_metric_split

one of "median", "tertile", "quartile", "none"

exposure_metric_soc_value

special exposure code for standard of care default -99

exposure_metric_plac_value

special exposure code for placebo default 0

exposure_distribution

one of distributions, lineranges or none

dose_plac_value

string identifying placebo in DOSE column

xlab

text to be used as x axis label

ylab

text to be used as y axis label

mean_text_size

mean text size default to 5

mean_obs_bydose

observed mean by dose TRUE/FALSE

N_text_size

N respondents/Ntotal by exposure bin text size default to 5

binlimits_text_size

5 binlimits text size

binlimits_ypos

binlimits y position default to 0

binlimits_color

binlimits text color default to "gray70"

dist_position_scaler

space occupied by the distribution default to 0.2

dist_offset

offset where the distribution position starts 0

lineranges_ypos

where to put the lineranges -1

lineranges_dodge

lineranges vertical dodge value 1

yproj

project the probabilities on y axis TRUE/FALSE

yproj_xpos

y projection x position 0

yproj_dodge

y projection dodge value 0.2

yaxis_position

where to put y axis "left" or "right"

facet_formula

facet formula to be use otherwise endpoint ~ expname

theme_certara

apply certara colors and format for strips and default colour/fill

Examples

# Example 1
library(ggplot2)
library(patchwork)
effICGI <- logistic_data |>
dplyr::filter(!is.na(ICGI7))|>
dplyr::filter(!is.na(AUC))
effICGI$DOSE <- factor(effICGI$DOSE,
                      levels=c("0", "600", "1200","1800","2400"),
                      labels=c("Placebo", "600 mg", "1200 mg","1800 mg","2400 mg"))
effICGI$STUDY <- factor(effICGI$STUDY)    
effICGI <- tidyr::gather(effICGI,Endpoint,response,ICGI7,BRLS)
a <- ggcontinuousexpdist(data = effICGI |> dplyr::filter(Endpoint =="ICGI7"),
                 response = "response",
                 endpoint = "Endpoint",
                 exposure_metrics = c("AUC"),
                 exposure_metric_split = c("quartile"),
                 exposure_metric_soc_value = -99,
                 exposure_metric_plac_value = 0,
                 dist_position_scaler = 1, dist_offset = -1 ,
                 yproj_xpos =  -20 ,
                 yproj_dodge = 20 ,
                 exposure_distribution ="distributions")

b <- ggcontinuousexpdist(data = effICGI |> dplyr::filter(Endpoint =="BRLS"),
                 response = "response",
                 endpoint = "Endpoint",
                 exposure_metrics = c("AUC"),
                 exposure_metric_split = c("quartile"),
                 exposure_metric_soc_value = -99,
                 exposure_metric_plac_value = 0,
                 dist_position_scaler = 4.2, dist_offset = 5 ,
                 yproj_xpos =  -20 ,
                 yproj_dodge = 20 ,
                 exposure_distribution ="distributions")            
a / b +
plot_layout(guides = "collect") &
 theme(legend.position = "top")

#Example 2
effICGI$SEX <- as.factor(effICGI$SEX)
ggcontinuousexpdist(data = effICGI |>
  dplyr::filter(Endpoint =="ICGI7"),
                 response = "response",
                 endpoint = "Endpoint",
                 color_fill = "SEX",
                 exposure_metrics = c("AUC"),
                 exposure_metric_split = c("quartile"),
                 exposure_metric_soc_value = -99,
                 exposure_metric_plac_value = 0,
                 dist_position_scaler = 1, dist_offset = -1 ,
                 yproj_xpos =  -20 ,
                 yproj_dodge = 20 ,
                 exposure_distribution ="lineranges")
## Not run: 
#Example 5

## End(Not run)