accrual_plot_predict {accrualPlot}

Accrual prediction plots

Description

Generates an accrual prediction plot using an accrual data frame produced by accrual_create_df and a target sample size. Prediction is based on a weighted linear regression. If the accrual data frame is a list (i.e. using the by option in accrual_create_df), or if center start dates are given, the number of enrolled and targeted sites is included.

Usage

accrual_plot_predict(
  accrual_df,
  target,
  overall = TRUE,
  name_overall = attr(accrual_df, "name_overall"),
  fill_up = TRUE,
  wfun = function(x) seq(1/nrow(x), 1, by = 1/nrow(x)),
  col.obs = NULL,
  lty.obs = 1,
  col.pred = "red",
  lty.pred = 2,
  pch.pred = 8,
  pos_prediction = c("out", "in", "none"),
  label_prediction = NULL,
  cex_prediction = 1,
  format_prediction = "%B %d, %Y",
  show_center = TRUE,
  design = 1,
  center_label = "Centers",
  center_legend = c("number", "strip"),
  targetc = NA,
  center_colors = NULL,
  center_legend_text_size = 0.7,
  ylim = NA,
  xlim = NA,
  ylab = "Recruited patients",
  xlabformat = "%d%b%Y",
  xlabn = 5,
  xlabminn = xlabn%/%2,
  xlabpos = NA,
  xlabsrt = 45,
  xlabadj = c(1, 1),
  xlabcex = 1,
  mar = NA,
  legend.list = NULL,
  ...,
  center_start_dates = NULL
)

gg_accrual_plot_predict(
  accrual_df,
  target,
  overall = TRUE,
  name_overall = attr(accrual_df, "name_overall"),
  col.pred = "red",
  lty.pred = 2,
  pch.pred = 8,
  fill_up = TRUE,
  wfun = function(x) seq(1/nrow(x), 1, by = 1/nrow(x)),
  pos_prediction = c("out", "in", "none"),
  label_prediction = NULL,
  format_prediction = "%B %d, %Y",
  xlabformat = "%d%b%Y"
)

Arguments

accrual_df

object of class 'accrual_df' or 'accrual_list' produced by accrual_create_df.

target

target sample size or date to predict end date or expected sample size, respectively. A single number or date, or a named vector with the same length as accrual_df. For the latter, center-specific predictions are shown.

overall

logical, indicates that accrual_df contains a summary with all sites (only if by is not NA).

name_overall

name of the summary with all sites (if by is not NA and overall==TRUE).

fill_up

whether to fill up days where no recruitment was observed, otherwise these points do not contribute to the regression.

wfun

function to calculate the weights with accrual data frame as argument, default is wfun<-function(x) seq(1 / nrow(x), 1, by = 1/nrow(x)).

col.obs

line color of cumulative recruitment, can be a vector with the same length as accrual_df.

lty.obs

line type of cumulative recruitment, can be a vector with the same length as accrual_df.

col.pred

line color of prediction, can be a vector with the same length as accrual_df.

lty.pred

line color of prediction, can be a vector with the same length as accrual_df.

pch.pred

point symbol for end of prediction, can be a vector with the same length as accrual_df.

pos_prediction

position of text with predicted end date or sample size, either "out", "in" or "none".

label_prediction

label for predicted end date or sample size.

cex_prediction

text size for predicted end date or sample size.

format_prediction

date format for predicted end date (only if target is a sample size)

show_center

logical, whether the center info should be shown (if accrual_df is a list or if center_start_dates are given).

design

design options for the center info 1 (default): below plot, 2: within plot, top, 3: within plot, bottom.

center_label

label for the center info.

center_legend

either "number" to plot numbers in the center strip or "strip" to add a legend strip, requires specification of center_colors.

targetc

target number of centers, to scale the legend if it is "strip".

center_colors

colors to be used for the strip with the centers, a vector of length targetc.

center_legend_text_size

size of the text of the center or legend strip, only has a function

ylim

limits for y-axis.

xlim

limits for x-axis.

ylab

y-axis label.

xlabformat

format of date on x-axis.

xlabn

integer giving the desired number of intervals for the xlabel, default=5.

xlabminn

integer giving the minimal number of intervals.

xlabpos

position of the x-label.

xlabsrt

rotation of x-axis labels in degrees.

xlabadj

adjustment of x-label, numeric vector with length 1 or 2 for different adjustment in x- and y-direction.

xlabcex

size of x-axis label.

mar

vector of length 4 (bottom, left, top, right margins), overwrite default margins.

legend.list

named list with options passed to legend(), only if accrual data frame is a list.

...

further options passed to plot() and axis().

center_start_dates

alternative way to add center info, vector with dates on which centers are enrolled.

Details

When the accrual_df includes multiple sites, the dataframe passed to ggplot includes a site variable which can be used for facetting

Value

accrual_plot_predict returns a plot with the accrual prediction.

Examples

data(accrualdemo)
accrual_df<-accrual_create_df(accrualdemo$date)
##Predict end date
accrual_plot_predict(accrual_df=accrual_df,target=300)
##Predict sample size
accrual_plot_predict(accrual_df=accrual_df,as.Date("2020-11-01"))

#Include site
accrual_df<-accrual_create_df(accrualdemo$date,by=accrualdemo$site)
accrual_plot_predict(accrual_df=accrual_df,target=300,center_label="Site")
## with strip and target
accrual_plot_predict(accrual_df=accrual_df,target=300,center_label="Site",
 targetc=5,center_colors=heat.colors(5),center_legend="strip")

#Design for site
accrual_plot_predict(accrual_df=accrual_df,target=300,design=2)

#Format prediction end date
accrual_plot_predict(accrual_df=accrual_df,target=300,
     pos_prediction="in",label_prediction="End of accrual: ",cex_prediction=1.2,
     format_prediction="%Y-%m-%d",ylim=c(0,150))

#Format plot
accrual_plot_predict(accrual_df=accrual_df,target=300,
     ylab="No of recruited patients",ylim=c(0,150),
     xlabcex=1.2,xlabsrt=30,xlabn=5,xlabmin=5,
     mgp=c(3,0.5,0),cex.lab=1.2,cex.axis=1.2)

#predictions for all sites
accrual_plot_predict(accrual_df=accrual_df,
target=c("Site 1"=160,"Site 2"=100,"Site 3"=40,"Overall"=300))
## different colors
accrual_plot_predict(accrual_df=accrual_df,
target=c("Site 1"=160,"Site 2"=100,"Site 3"=40,"Overall"=300),
col.obs=topo.colors(length(accrual_df)))
##not showing center info
accrual_plot_predict(accrual_df=accrual_df,
target=c("Site 1"=160,"Site 2"=100,"Site 3"=40,"Overall"=300),
show_center=FALSE)

#predictions of sample size for all sites
target<-rep(as.Date("2020-11-01"),4)
names(target)<-c("Site 1","Site 2","Site 3","Overall")
accrual_plot_predict(accrual_df=accrual_df,target=target,col.obs=topo.colors(length(accrual_df)))
### ggplot2 approach
data(accrualdemo)
accrual_df<-accrual_create_df(accrualdemo$date)
gg_accrual_plot_predict(accrual_df = accrual_df, target = 300)
gg_accrual_plot_predict(accrual_df = accrual_df, target = 300) +
  ggplot2::theme_classic()

#Include site
accrual_df<-accrual_create_df(accrualdemo$date, by=accrualdemo$site)
gg_accrual_plot_predict(accrual_df=accrual_df, target=300)


#Format prediction end date
gg_accrual_plot_predict(accrual_df = accrual_df,
target=300,
pos_prediction="in",
format_prediction="%Y-%m-%d")


#predictions for all sites
gg_accrual_plot_predict(accrual_df = accrual_df,
target=c("Site 1"=160,"Site 2"=100,"Site 3"=40,"Overall"=300))
gg_accrual_plot_predict(accrual_df = accrual_df,
 target=c("Site 1"=160,"Site 2"=100,"Site 3"=40,"Overall"=300)) +
	ggplot2::theme(legend.position = c(0.15,.9)) +
	ggplot2::labs(col = "Site")