Create a continuous fit vs exposure(s) plot — ggcontinuousexpdist • ggquickeda

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

Usage

ggcontinuousexpdist(
  data = effICGI,
  response = "response",
  endpoint = "Endpoint",
  model_type = c("linear", "loess", "none"),
  DOSE = "DOSE",
  color_fill = "DOSE",
  fit_by_color_fill = FALSE,
  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"),
  exposure_distribution_percent = TRUE,
  exposure_distribution_percent_text_size = 5,
  dose_plac_value = "Placebo",
  xlab = "Exposure Values",
  ylab = "Response",
  points_alpha = 0.2,
  points_show = TRUE,
  mean_obs_byexptile = TRUE,
  mean_obs_byexptile_plac = TRUE,
  mean_obs_byexptile_group = "none",
  mean_text_size = 5,
  mean_obs_bydose = TRUE,
  mean_obs_bydose_plac = FALSE,
  N_text_show = TRUE,
  N_text_size = 5,
  N_text_ypos = c("with means", "top"),
  N_text_sep = "\n",
  binlimits_show = TRUE,
  binlimits_text_size = 5,
  binlimits_ypos = -Inf,
  binlimits_color = "#B3B3B380",
  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
model_type: type of the trend fit one of "linear", "loess", "none"
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
fit_by_color_fill: fit split by color? default FALSE
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
exposure_distribution_percent: show percent of distribution between binlimits TRUE/FALSE
exposure_distribution_percent_text_size: distribution percentages text size default to 5
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
points_alpha: alpha transparency for points
points_show: show the observations TRUE/FALSE
mean_obs_byexptile: observed mean by exptile TRUE/FALSE
mean_obs_byexptile_plac: observed mean by exptile placebo TRUE/FALSE
mean_obs_byexptile_group: additional grouping for exptile probabilities default none
mean_text_size: mean text size default to 5
mean_obs_bydose: observed mean by dose TRUE/FALSE
mean_obs_bydose_plac: observed mean by placebo dose TRUE/FALSE
N_text_show: show the N by exptile TRUE/FALSE
N_text_size: N by exposure text size default to 5
N_text_ypos: y position for N two text elements the first for by exptile and the second for by dose/color options include with means top bottom
N_text_sep: character string to separat N from mean default \n
binlimits_show: show the binlimits vertical lines TRUE/FALSE
binlimits_text_size: binlimits text size default to 5
binlimits_ypos: binlimits y position default to -Inf
binlimits_color: binlimits text color default to alpha("gray70",0.5)
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",
                 mean_obs_bydose_plac = TRUE,
                 mean_obs_byexptile_plac=FALSE,
                 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)`

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",
                 mean_obs_bydose_plac = TRUE,
                 mean_obs_byexptile_plac=FALSE,
                 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()`).
#> `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()`).


#Example 2 loess fit
effICGI$SEX <- as.factor(effICGI$SEX)
ggcontinuousexpdist(data = effICGI |>
  dplyr::filter(Endpoint =="ICGI7"),
                 model_type = "loess",
                 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",
                 lineranges_ypos = -0.2,
                 lineranges_dodge = 0.2,
                 lineranges_doselabel = TRUE)
#> Joining with `by = join_by(loopvariable, DOSE, SEX, color_fill2, quant_10)`
#> Joining with `by = join_by(loopvariable, DOSE, SEX, color_fill2, quant_90)`
#> Joining with `by = join_by(loopvariable, DOSE, SEX, color_fill2, quant_25)`
#> Joining with `by = join_by(loopvariable, DOSE, SEX, color_fill2, quant_75)`
#> Joining with `by = join_by(loopvariable, DOSE, SEX, color_fill2, medexp)`
#> `geom_smooth()` using formula = 'y ~ x'
#> `geom_smooth()` using formula = 'y ~ x'

if (FALSE) { # \dontrun{
#Example 3
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,PRLS,BRLS)
effICGI$Endpoint2 <- effICGI$Endpoint
ggcontinuousexpdist(data = effICGI ,
                    model_type = "loess",
                    response = "response",
                    endpoint = "Endpoint",
                    color_fill = "Endpoint2",
                    fit_by_color_fill = TRUE,
                    exposure_metrics = c("AUC"),
                    exposure_metric_split = c("quartile"),
                    exposure_metric_soc_value = -99,
                    exposure_metric_plac_value = 0,
                    yproj = FALSE, points_show = FALSE,
                    exposure_distribution ="none",
                    N_text_sep = " | N = ",
                    N_text_ypos = c("bottom","with means"),
                    binlimits_color = "red",
                    binlimits_ypos = 10,
                    facet_formula = ~expname)
} # }