Create a continuous exposure fit plot — ggcontinuousexpdist • ggquickeda

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 = "Response",
  mean_obs_byexptile = TRUE,
  mean_text_size = 5,
  mean_obs_bydose = TRUE,
  mean_obs_bydose_plac = FALSE,
  N_text_size = 5,
  binlimits_text_size = 5,
  binlimits_ypos = -Inf,
  binlimits_color = "gray70",
  dist_position_scaler = 0.2,
  dist_offset = 0,
  dist_scale = 0.9,
  lineranges_ypos = -1,
  lineranges_dodge = 1,
  lineranges_doselabel = FALSE,
  proj_bydose = TRUE,
  yproj = TRUE,
  yproj_xpos = 0,
  yproj_dodge = 0.2,
  yaxis_position = c("left", "right"),
  facet_formula = NULL,
  theme_certara = TRUE,
  return_list = FALSE
)

Arguments

data: Data to use with multiple endpoints stacked into response (values), Endpoint(endpoint name)
response: name of the column holding the response values
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_obs_byexptile: observed probability by exptile TRUE/FALSE
mean_text_size: mean text size default to 5
mean_obs_bydose: observed mean by dose TRUE/FALSE
mean_obs_bydose_plac: observed probability by placebo dose TRUE/FALSE
N_text_size: N 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 default to 0
dist_scale: scaling parameter for ggridges default to 0.9
lineranges_ypos: where to put the lineranges -1
lineranges_dodge: lineranges vertical dodge value 1
lineranges_doselabel: TRUE/FALSE
proj_bydose: project the probabilities on logistic curve TRUE/FALSE
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
return_list: What to return if True a list of the datasets and plot is returned instead of only the plot

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")
#> Joining with `by = join_by(loopvariable, DOSE, quant_10)`
#> Joining with `by = join_by(loopvariable, DOSE, quant_90)`
#> Joining with `by = join_by(loopvariable, DOSE, quant_25)`
#> Joining with `by = join_by(loopvariable, DOSE, quant_75)`
#> Joining with `by = join_by(loopvariable, DOSE, medexp)`

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",
                 return_list = FALSE)            
#> Joining with `by = join_by(loopvariable, DOSE, quant_10)`
#> Joining with `by = join_by(loopvariable, DOSE, quant_90)`
#> Joining with `by = join_by(loopvariable, DOSE, quant_25)`
#> Joining with `by = join_by(loopvariable, DOSE, quant_75)`
#> Joining with `by = join_by(loopvariable, DOSE, medexp)`
(a / b)  +
plot_layout(guides = "collect") &
 theme(legend.position = "top")
#> `geom_smooth()` using formula = 'y ~ x'
#> `geom_smooth()` using formula = 'y ~ x'
#> Picking joint bandwidth of 11.7
#> Warning: Removed 244 rows containing non-finite outside the scale range
#> (`stat_density_ridges()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_pointrange()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_text()`).
#> `geom_smooth()` using formula = 'y ~ x'
#> Warning: Removed 4 rows containing non-finite outside the scale range (`stat_smooth()`).
#> `geom_smooth()` using formula = 'y ~ x'
#> Warning: Removed 4 rows containing non-finite outside the scale range (`stat_smooth()`).
#> Picking joint bandwidth of 11.7
#> Warning: Removed 244 rows containing non-finite outside the scale range
#> (`stat_density_ridges()`).
#> Warning: Removed 4 rows containing missing values or values outside the scale range
#> (`geom_point()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_pointrange()`).
#> Warning: Removed 1 row containing missing values or values outside the scale range
#> (`geom_text()`).


#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")
#> Joining with `by = join_by(loopvariable, SEX, color_fill2, DOSE, quant_10)`
#> Joining with `by = join_by(loopvariable, SEX, color_fill2, DOSE, quant_90)`
#> Joining with `by = join_by(loopvariable, SEX, color_fill2, DOSE, quant_25)`
#> Joining with `by = join_by(loopvariable, SEX, color_fill2, DOSE, quant_75)`
#> Joining with `by = join_by(loopvariable, SEX, color_fill2, DOSE, medexp)`
#> `geom_smooth()` using formula = 'y ~ x'
#> `geom_smooth()` using formula = 'y ~ x'
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_pointrange()`).
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_text()`).

if (FALSE) { # \dontrun{
#Example 5
} # }