Type: Package
Title: Accrual Plots and Predictions for Clinical Trials
Version: 1.0.7
Maintainer: Lukas Bütikofer <lukas.buetikofer@ctu.unibe.ch>
Description: Tracking accrual in clinical trials is important for trial success. If accrual is too slow, the trial will take too long and be too expensive. If accrual is much faster than expected, time sensitive tasks such as the writing of statistical analysis plans might need to be rushed. 'accrualPlot' provides functions to aid the tracking of accrual and predict when a trial will reach it's intended sample size.
License: MIT + file LICENSE
URL: https://github.com/CTU-Bern/accrualPlot, https://ctu-bern.github.io/accrualPlot/
BugReports: https://github.com/CTU-Bern/accrualPlot/issues
Encoding: UTF-8
Depends: lubridate, R (≥ 2.10)
Imports: dplyr, ggplot2, grid, magrittr, purrr, rlang
RoxygenNote: 7.1.1
Suggests: knitr, markdown, patchwork, rmarkdown, testthat, vdiffr
VignetteBuilder: knitr
LazyData: true
NeedsCompilation: no
Packaged: 2022-08-16 07:13:34 UTC; lbuetikofer
Author: Lukas Bütikofer [cre, aut], Alan G. Haynes [aut]
Repository: CRAN
Date/Publication: 2022-08-16 10:10:05 UTC

accrual_create_df

Description

Creates a data frame or a list of data frames that contains the absolute and cumululative number of participants recruited at each date from a vector with enrollment dates. Used as input for accrual plot functions.

Usage

accrual_create_df(
  enrollment_dates,
  by = NA,
  start_date = "site",
  current_date = "common",
  overall = TRUE,
  name_overall = "Overall",
  pos_overall = c("last", "first"),
  force_start0 = TRUE
)

Arguments

enrollment_dates

date vector with one entry per participants.

by

factor or character vector with sites, has to have the same length as enrollment dates. If not NA, a list with an accrual data frame for each site is generated.

start_date

date when recruitment started. Single date (used for all sites in by), named date vector (with length and names corresponding to the levels of by), "common" (first date overall) or "site" (first date for each site, default).

current_date

date of the data export or database freeze. Single date, named date vector (with length and names corresponding to the levels of by), "common" (last date overall, default) or "site" (first date for each site).

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).

pos_overall

overall as last or first element of the list (if by is not NA and overall==TRUE).

force_start0

logical, adds an extra 0 line to the accrual data frame in cases where a start date is given and corresponds to the earliest enrollment date.

Value

Returns a data frame of class 'accrual_df' or a list of class 'accrual_list' with an 'accrual_df' for each level of by (if by is not NA). The 'accrual_df' contains a row per accrual day and the following three columns:

Date

date of accrual

Freq

absolute number accrued at Date

Cumulative

cumulative number accrued up to Date

See Also

accrual_plot_cum(), accrual_plot_abs() and accrual_plot_predict() to generate cumulative, absolute and prediction plots, and accrual_table() to generate an accrual table.

Examples


data(accrualdemo)
accrual_create_df(accrualdemo$date)
# different start and current date
accrual_create_df(accrualdemo$date, start_date=as.Date("2020-07-08"),
current_date=as.Date("2020-10-15"))

#by site
accrual_create_df(accrualdemo$date,by=accrualdemo$site)

accrual_linear_model

Description

Creates a weighted linear regression model using an accrual data frame produced by accrual_create_df.

Usage

accrual_linear_model(
  accrual_df,
  fill_up = TRUE,
  wfun = function(x) seq(1/nrow(x), 1, by = 1/nrow(x))
)

Arguments

accrual_df

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

fill_up

whether to fill up days where no recruitment was observed,

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)).

Value

Returns an object of class 'lm' with a weighted linear regression of cumulative accrual on dates.

Examples


data(accrualdemo)
accrual_df<-accrual_create_df(accrualdemo$date)
accrual_linear_model(accrual_df)

#unweighted
accrual_linear_model(accrual_df, wfun=function(x) rep(1,nrow(x)))

#different start and current date
accrual_df<-accrual_create_df(accrualdemo$date,start_date=as.Date("2020-07-08"),
    current_date=as.Date("2020-07-15"))
accrual_linear_model(accrual_df)

#accrual_df with by option
accrual_df<-accrual_create_df(accrualdemo$date,by=accrualdemo$site)
accrual_linear_model(accrual_df)

Absolute accrual plots

Description

Plot of absolute recruitment by time unit using an accrual data frame produced by accrual_create_df.

Usage

accrual_plot_abs(
  accrual_df,
  unit = c("month", "year", "week", "day"),
  target = NULL,
  overall = TRUE,
  name_overall = attr(accrual_df, "name_overall"),
  ylim = NULL,
  xlim = NULL,
  ylab = "Recruited patients",
  xlabformat = NULL,
  xlabsel = NA,
  xlabpos = NULL,
  xlabsrt = 45,
  xlabadj = c(1, 1),
  xlabcex = 1,
  col = NULL,
  legend.list = NULL,
  ...
)

gg_accrual_plot_abs(
  accrual_df,
  unit = c("month", "year", "week", "day"),
  xlabformat = NULL
)

Arguments

accrual_df

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

unit

time unit for which the bars should be plotted, one of "month", "year", "week" or "day".

target

adds horizontal line for target recruitment per time unit.

overall

logical, indicates that accrual_df contains a summary with all sites that should be removed from stacked barplot (only if by is not NA).

name_overall

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

ylim

limits for y-axis.

xlim

limits for x-axis.

ylab

y-axis label.

xlabformat

format of date on x-axis.

xlabsel

selection of x-labels if not all should be shown, by default all are shown up to 15 bars, with more an automated selection is done, either NA (default), NULL (show all), or a numeric vector.

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.

col

colors of bars in barplot, can be a vector if accrual_df is a list, default is grayscale.

legend.list

named list with options passed to legend().

...

further arguments passed to barplot() and axis().

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_abs returns a barplot of absolute accrual by time unit (stacked if accrual_df is a list).

Examples

set.seed(2020)
enrollment_dates <- as.Date("2018-01-01") + sort(sample(1:100, 50, replace=TRUE))
accrual_df<-accrual_create_df(enrollment_dates)
accrual_plot_abs(accrual_df,unit="week")

#time unit
accrual_plot_abs(accrual_df,unit="day")

#include target
accrual_plot_abs(accrual_df,unit="week",target=5)

#further plot options
accrual_plot_abs(accrual_df,unit="week",ylab="No of recruited patients",
   xlabformat="%Y-%m-%d",xlabsrt=30,xlabpos=-0.8,xlabadj=c(1,0.5),
   col="pink",tck=-0.03,mgp=c(3,1.2,0))

#accrual_df with by option
set.seed(2020)
centers<-sample(c("Site 1","Site 2","Site 3"),length(enrollment_dates),replace=TRUE)
centers<-factor(centers,levels=c("Site 1","Site 2","Site 3"))
accrual_df<-accrual_create_df(enrollment_dates,by=centers)
accrual_plot_abs(accrual_df=accrual_df,unit=c("week"))

### ggplot2 approach
data(accrualdemo)
accrual_df<-accrual_create_df(accrualdemo$date)
gg_accrual_plot_abs(accrual_df, unit = "week")
gg_accrual_plot_abs(accrual_df, unit = "week") +
  ggplot2::theme_classic()

#time unit
gg_accrual_plot_abs(accrual_df, unit = "day")

#accrual_df with by option
accrual_df <- accrual_create_df(accrualdemo$date, by = accrualdemo$site)
gg_accrual_plot_abs(accrual_df = accrual_df, unit = "week")
gg_accrual_plot_abs(accrual_df = accrual_df, unit = "week") +
  ggplot2::scale_fill_discrete(type = c("black", "red", "blue", "green"))

Cumulative accrual plots

Description

Plot of cumulative recruitment using an accrual data frame produced by accrual_create_df.

Usage

accrual_plot_cum(
  accrual_df,
  ylim = NA,
  xlim = NA,
  ylab = "Recruited patients",
  xlabn = 5,
  xlabminn = xlabn%/%2,
  xlabformat = "%d%b%Y",
  xlabpos = NA,
  xlabsrt = 45,
  xlabadj = c(1, 1),
  xlabcex = 1,
  col = rep(1:8, 5),
  lty = rep(1:5, each = 8),
  legend.list = NULL,
  ...
)

gg_accrual_plot_cum(accrual_df, xlabformat = "%d%b%Y")

Arguments

accrual_df

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

ylim

limits for y-axis.

xlim

limits for x-axis.

ylab

y-axis label.

xlabn

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

xlabminn

negative integer giving the minimal number of intervals.

xlabformat

format of date on x-axis.

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.

col

color for line(s) in plot

lty

line type(s) in plot

legend.list

named list with options passed to legend().

...

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

Details

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

Value

accrual_plot_cum returns a plot of the cumulative accrual (per site if accrual_df is a list).

ggplot2 object

Examples

set.seed(2020)
enrollment_dates <- as.Date("2018-01-01") + sort(sample(1:30, 50, replace=TRUE))
accrual_df<-accrual_create_df(enrollment_dates)
accrual_plot_cum(accrual_df)
accrual_plot_cum(accrual_df,cex.lab=1.2,cex.axis=1.1,xlabcex=1.1)

#several sites
set.seed(1)
centers<-sample(c("Site 1","Site 2","Site 3"),length(enrollment_dates),replace=TRUE)
accrual_df<-accrual_create_df(enrollment_dates,by=centers)
accrual_plot_cum(accrual_df)

#assuming a common start and current date
accrual_df<-accrual_create_df(enrollment_dates,by=centers,start_date="common",current_date="common")
accrual_plot_cum(accrual_df)

#plot and legend options
accrual_plot_cum(accrual_df,col=c("red",rep(1,3)),lty=c(1,1:3),cex.lab=1.2,cex.axis=1.1,xlabcex=1.1)
accrual_plot_cum(accrual_df,legend.list=list(ncol=2,bty=TRUE,cex=0.8))

#without overall
accrual_df<-accrual_create_df(enrollment_dates,by=centers,overall=FALSE)
accrual_plot_cum(accrual_df)

### ggplot2 approach
data(accrualdemo)
accrual_df<-accrual_create_df(accrualdemo$date)
gg_accrual_plot_cum(accrual_df)
gg_accrual_plot_cum(accrual_df) +
  ggplot2::theme_classic()

#several sites
accrual_df <- accrual_create_df(accrualdemo$date, by = accrualdemo$site)
gg_accrual_plot_cum(accrual_df)

#assuming a common start and current date
accrual_df <-
  accrual_create_df(
    accrualdemo$date,
    by = accrualdemo$site,
    start_date = "common",
    current_date = "common"
  )
gg_accrual_plot_cum(accrual_df)

#without overall
accrual_df <-
  accrual_create_df(accrualdemo$date, by = accrualdemo$site, overall = FALSE)
gg_accrual_plot_cum(accrual_df)

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")

accrual_predict

Description

accrual_predict

Usage

accrual_predict(accrual_df, accrual_fit, target)

Arguments

accrual_df

accrual data frame produced by accrual_create_df (optionally with by option as a list)

accrual_fit

linear model produced by accrual_linear_model, can be a list with the same length as accrual_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 (to add site-specific targets).

Details

Prediction of end date based on an accrual data frame produced by accrual_create_df, a fitted regression model produced by accrual_linear_model and a target sample size.

Value

Returns the predicted end date(s) or the predicted sample size(s).

Examples


data(accrualdemo)
accrual_df<-accrual_create_df(accrualdemo$date)
accrual_model<-accrual_linear_model(accrual_df)
#predict date for a specific n
accrual_predict(accrual_df,accrual_model,target=300)
#predict n at a specific date
accrual_predict(accrual_df,accrual_model,target=as.Date("2020-11-01"))

#different start and current date
accrual_df<-accrual_create_df(accrualdemo$date,start_date=as.Date("2020-07-09"),
    current_date=as.Date("2020-10-15"))
accrual_model<-accrual_linear_model(accrual_df)
accrual_predict(accrual_df,accrual_model,target=300)

 #accrual_df with by option
 accrual_df<-accrual_create_df(accrualdemo$date,by=accrualdemo$site)
accrual_model<-accrual_linear_model(accrual_df)
accrual_predict(accrual_df,accrual_model,
  target=c("Site 1"=160,"Site 2"=100,"Site 3"=40,"Overall"=300))
accrual_predict(accrual_df,accrual_model,target=as.Date("2020-11-01"))

accrual_table

Description

Table of recruitment overview by site, rate of recruitment

Usage

accrual_table(
  accrual_df,
  overall = TRUE,
  name_overall = "Overall",
  pos_overall = c("last", "first"),
  unit = c("month", "year", "week", "day"),
  format_table_date = "%d%b%Y",
  format_time = "%1.0f",
  format_rrate = "%1.2f",
  header = TRUE
)

Arguments

accrual_df

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

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).

pos_overall

overall in last or first row (if by is not NA and overall==TRUE).

unit

time unit for time recruiting and the rate, one of "month", "year", "week" or "day".

format_table_date

format of start date in table.

format_time

format of time recruiting in table.

format_rrate

format of recruitment rate in table.

header

include header, logical or character vector of length 4 or 5 (if accrual_df is a list).

Value

Returns data frame with a header, a row per site and overall and the following columns:

name

name of the site (if accrual_df is a list)

start_date

accrual start date

time

time accruing

n

number of patients accrued

rate

accrual rate per time unit

Examples

data(accrualdemo)
accrual_df<-accrual_create_df(accrualdemo$date,by=accrualdemo$site)
accrual_table(accrual_df)

#format
accrual_table(accrual_df,format_time="%1.1f",format_rrate="%1.1f")

#unit
accrual_table(accrual_df,unit="day")

#common start and current dates
accrual_df<-accrual_create_df(accrualdemo$date,by=accrualdemo$site,start_date="common",
current_date="common")
accrual_table(accrual_df)
accrual_df<-accrual_create_df(accrualdemo$date,by=accrualdemo$site,start_date=as.Date("2020-07-09"),
    current_date=as.Date("2020-10-15"))
accrual_table(accrual_df)

accrual_time_unit

Description

Generates summary of recruitment per time unit

Usage

accrual_time_unit(accrual_df, unit = c("month", "year", "week", "day"))

Arguments

accrual_df

accrual data frame produced by accrual_create_df with by=NA.

unit

time unit for which the bars should be plotted, one of "month", "year", "week" or "day".

Value

Returns a data frame with the number of patients accrued for each time unit.

Examples


data(accrualdemo)
accrual_df<-accrual_create_df(accrualdemo$date)
accrual_time_unit(accrual_df,"week")
accrual_time_unit(accrual_df,"day")

Demonstration data set

Description

Simulated recruitment data from three sites. Each row represents one participant. Sites one and two started on 2020-07-01, site three on 2020-09-01.

Usage

accrualdemo

Format

A data frame with two variables: date, and site.

as.data.frame method for accural_list objects

Description

as.data.frame method for accural_list objects

Usage

## S3 method for class 'accrual_list'
as.data.frame(x, ...)

Arguments

x

accrual_list

...

for consistency with other as.data.frame methods (not used)

Note

methods from within the package will not work on the output from this function.

Examples

data(accrualdemo)
x <- accrual_create_df(accrualdemo$date, accrualdemo$site)
as.data.frame(x)

Plot method for accrual data frames produced by accrual_create_df

Description

Plot method for accrual data frames produced by accrual_create_df

Usage

## S3 method for class 'accrual_df'
plot(x, which = "cum", engine = c("base", "ggplot2"), ...)

Arguments

x

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

which

one of "cumulative", "absolute" or "predict". Abbreviations are allowed.

engine

string to indicate the plotting engine (base/graphics or ggplot2)

...

options passed to other functions

Value

A plot with cumulative or absolute accrual, or accrual prediction.

See Also

accrual_plot_abs(), accrual_plot_cum() and accrual_plot_predict()

Examples

data(accrualdemo)
accrual_df <- accrual_create_df(accrualdemo$date)
plot(accrual_df)
plot(accrual_df, "abs", unit="week")
plot(accrual_df, "pred", target = 300)
plot(accrual_df, "pred", target = 300, engine = "ggplot")

Print methods for accrual objects

Description

Print methods for accrual objects

Usage

## S3 method for class 'accrual_df'
print(x, head = TRUE, ...)

## S3 method for class 'accrual_list'
print(x, ...)

Arguments

x

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

head

show header of the accrual data?

...

arguments passed to head

Value

No return value

Examples

data(accrualdemo)
accrual_df<-accrual_create_df(accrualdemo$date)
print(accrual_df)
# only show text
print(accrual_df, head = FALSE)
# show first 15 days
print(accrual_df, n = 15)

Summary method for accrual_dfs (as created by accrual_create_df)

Description

Summary method for accrual_dfs (as created by accrual_create_df)

Usage

## S3 method for class 'accrual_df'
summary(object, ...)

Arguments

object

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

...

options passed to other functions

Value

Returns data frame with a header, a row per site and overall and the following columns:

name

name of the site (if accrual_df is a list)

start_date

accrual start date

time

time accruing

n

number of patients accrued

rate

accrual rate per time unit

Examples

data(accrualdemo)
accrual_df<-accrual_create_df(accrualdemo$date, accrualdemo$site)
summary(accrual_df)