ggcontinuousexpdist: Create a continuous exposure fit plot

In ggquickeda: Quickly Explore Your Data Using 'ggplot2' and 'table1' Summary Tables

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)

ggquickeda documentation built on June 22, 2024, 11:30 a.m.