Title: Clinical Stroke Research
Version: 24.10.1
Description: A collection of tools for clinical trial data management and analysis in research and teaching. The package is mainly collected for personal use, but any use beyond that is encouraged. This package has migrated functions from 'agdamsbo/daDoctoR', and new functions has been added. Version follows months and year. See NEWS/Changelog for release notes. This package includes sampled data from the TALOS trial (Kraglund et al (2018) <doi:10.1161/STROKEAHA.117.020067>). The win_prob() function is based on work by Zou et al (2022) <doi:10.1161/STROKEAHA.121.037744>. The age_calc() function is based on work by Becker (2020) <doi:10.18637/jss.v093.i02>.
URL: https://agdamsbo.github.io/stRoke/, https://github.com/agdamsbo/stRoke
BugReports: https://github.com/agdamsbo/stRoke/issues
License: GPL-3
Encoding: UTF-8
RoxygenNote: 7.3.2
LazyData: true
Suggests: knitr, rmarkdown, testthat, here, spelling, usethis, pak, roxygen2, devtools
Config/testthat/edition: 3
Imports: calendar, dplyr, ggplot2, grDevices, gtsummary, lubridate, MASS, rankinPlot, stats, tidyr, utils, tibble, tidyselect
Depends: R (≥ 3.5.0)
VignetteBuilder: knitr
Language: en-US
NeedsCompilation: no
Packaged: 2024-10-25 06:34:55 UTC; au301842
Author: Andreas Gammelgaard Damsbo ORCID iD [aut, cre]
Maintainer: Andreas Gammelgaard Damsbo <agdamsbo@clin.au.dk>
Repository: CRAN
Date/Publication: 2024-10-25 06:50:01 UTC

stRoke: Clinical Stroke Research

Description

logo

A collection of tools for clinical trial data management and analysis in research and teaching. The package is mainly collected for personal use, but any use beyond that is encouraged. This package has migrated functions from 'agdamsbo/daDoctoR', and new functions has been added. Version follows months and year. See NEWS/Changelog for release notes. This package includes sampled data from the TALOS trial (Kraglund et al (2018) doi:10.1161/STROKEAHA.117.020067). The win_prob() function is based on work by Zou et al (2022) doi:10.1161/STROKEAHA.121.037744. The age_calc() function is based on work by Becker (2020) doi:10.18637/jss.v093.i02.

Author(s)

Maintainer: Andreas Gammelgaard Damsbo agdamsbo@clin.au.dk (ORCID)

See Also

Useful links:

MOVED Add padding to string

Description

MOVED Add padding to string

Usage

add_padding(
  d,
  length = NULL,
  after = FALSE,
  pad = "0",
  lead = NULL,
  tail = NULL
)

Arguments

d

vector of strings or numbers

length

final string length

after

if padding should be added after as opposed to default before

pad

padding string of length 1

lead

leading string for all. Number or character vector. Cycled.

tail

tailing string for all. Number or character vector. Cycled.

Value

vector or character strings of same length.

Examples

add_padding(sample(1:200,5),tail="AA",lead=c(2,3,"e"))

Calculate age in years, months, or days

Description

Calculate age in years, months, or days

Usage

age_calc(dob, enddate = Sys.Date(), units = "years", precise = TRUE)

Arguments

dob

Date of birth

enddate

End date for age calculation (default is Sys.Date())

units

Units for age calculation (default is "years"). Can be c("days", "months", "years")

precise

Option to calculate age precisely (default is TRUE)

Value

numeric vector length 1

Source

doi:10.18637/jss.v093.i02

References

Becker, J.P. (2020). eeptools: An R Package for Teaching and Learning Ecology and Evolutionary Biology. Journal of Statistical Software, 93(2), 1-27.

Examples

trunc(age_calc(as.Date("1945-10-23"),as.Date("2018-09-30")))

MOVED Split to chunks of size n

Description

MOVED Split to chunks of size n

Usage

chunks_of_n(d, n, label = NULL, even = FALSE, pattern = NULL)

Arguments

d

data. Can be vector or data frame.

n

number of chunks

label

naming prefix for chunk names

even

boolean to set if size of chunks should be evenly distributed.

pattern

regex pattern to extract names from provided vector. If data frame, will assume first column is name.

Value

List of length n

Examples

tail(chunks_of_n(seq_len(100),7),3)
tail(chunks_of_n(seq_len(100),7,even=TRUE),3)
ds <- data.frame(nm=paste0("Sub",
add_padding(rownames(stRoke::talos))),stRoke::talos)
head(chunks_of_n(ds,7,pattern="Sub[0-9]{3}",label="grp"),2)
## Please notice that no sorting is performed. This is on purpose to preserve
## original sorting. If sorting is intended, try something like this:
ds[order(ds$nm),] |> chunks_of_n(7,pattern="Sub[0-9]{3}",label="grp") |> 
head(2)

Confidence interval plot with point estimate

Description

Horizontal forest plot of point estimate with confidence intervals. Includes dichotomous or olr, depending on number of levels in "x". Title and axis labels can be added to the ggplot afterwards.

Usage

ci_plot(
  ds,
  x = NULL,
  y = NULL,
  vars = NULL,
  dec = 3,
  lbls = NULL,
  title = NULL,
  method = "auto"
)

Arguments

ds

main input, either data set or logistic model

x

text string of main exposure variable

y

text string of outcome variable

vars

variables for multivariate analysis.

dec

Decimals in labels

lbls

Labels for variable names

title

Plot title. Can be specified later.

method

Character vector. The method for the regression. Can be c("auto", "model").

Value

ggplot element

Examples

# Auto plot
data(talos)
talos[,"mrs_1"]<-factor(talos[,"mrs_1"],ordered=TRUE)
ci_plot(ds = talos, x = "rtreat", y = "mrs_1", 
vars = c("hypertension","diabetes"))
## Model plot
# iris$ord<-factor(sample(1:3,size=nrow(iris),replace=TRUE),ordered=TRUE)
# lm <- MASS::polr(ord~., data=iris, Hess=TRUE, method="logistic")
# ci_plot(ds = lm, method="model")

Plot color examples with contrasting text

Description

Plots color examples with contrasting text. Parameters are passed to contrast_text.

Usage

color_plot(
  colors,
  labels = TRUE,
  borders = NULL,
  cex_label = 1,
  ncol = NULL,
  ...
)

Arguments

colors

Vector of colors to plot

labels

Show color names. Default is TRUE

borders

Border parameter for 'rect()' function. Default is NULL

cex_label

Label size. Default is 1.

ncol

Desired number of columns. Default is ceiling of square root to the length of 'colors' vector provided.

...

Parameters for the

Value

base plot

Examples

par(bg=NULL)
colors <- sample(colors(),size = 20)
color_plot(colors, method="relative")

MOVED Contrast Text Color

Description

Calculates the best contrast text color for a given background color.

Usage

contrast_text(
  background,
  light_text = "white",
  dark_text = "black",
  threshold = 0.5,
  method = "perceived_2",
  ...
)

Arguments

background

A hex/named color value that represents the background.

light_text

A hex/named color value that represents the light text color.

dark_text

A hex/named color value that represents the dark text color.

threshold

A numeric value between 0 and 1 that is used to determine the luminance threshold of the background color for text color.

method

A character string that specifies the method for calculating the luminance. Three different methods are available: c("relative","perceived","perceived_2")

...

parameter overflow. Ignored.

Details

This function aids in deciding the font color to print on a given background. The function is based on the example provided by teppo: https://stackoverflow.com/a/66669838/21019325. The different methods provided are based on the methods outlined in the StackOverflow thread: https://stackoverflow.com/questions/596216/formula-to-determine-perceived-brightness-of-rgb-color

Value

A character string that contains the best contrast text color.

Examples

contrast_text(c("#F2F2F2", "blue"))

contrast_text(c("#F2F2F2", "blue"), method="relative")

CPR check

Description

Checking validity of cpr number. Vectorised.

Usage

cpr_check(cpr)

Arguments

cpr

cpr-numbers as ddmmyy"-."xxxx or ddmmyyxxxx. Also mixed formatting. Vector or data frame column.

Value

Logical vector of cpr validity

Examples

fsd<-c("2310450637", "010190-2000", "010115-4000",
"300450-1030","010150-4021")
cpr_check("2310450637")
cpr_check(fsd)
all(cpr_check(fsd))

Extracting date of birth from CPR

Description

For easy calculation. Does not handle cprs with letters (interim cpr)

Usage

cpr_dob(cpr, format = "%d-%m-%Y")

Arguments

cpr

cpr-numbers as ddmmyy"-."xxxx or ddmmyyxxxx. Also mixed formatting. Vector or data frame column.

format

character string of dob date format. Default is "%d-%m-%Y".

Value

character vector

Examples

cpr_dob("231045-0637")
fsd<-c("2310450637", "010190-2000", "010115-4000",
"300450-1030","010150-4021")
cpr_dob(fsd)

Determine female sex from CPR

Description

Just checking if last number of a string is equal or not.

Usage

cpr_female(cpr)

Arguments

cpr

Vector. cpr-numbers as ddmmyy"-."xxxx or ddmmyyxxxx. Also mixed formatting. Vector or data frame column.

Value

Logical vector

Examples

cpr_female(stRoke::cprs[,1])

Data frame of 200 cpr numbers

Description

This is just a repeated sample of 8 synthesized cpr-numbers for testing purposes.

Usage

data(cprs)

Format

A data frame with 200 rows and 1 variable:

cpr

Mixed format cpr-numbers, characters

See Also

https://da.wikipedia.org/wiki/231045-0637

DEPRECATED Moved to REDCapCAST::ds2dd() | Data set to data dictionary function

Description

DEPRECATED Moved to REDCapCAST::ds2dd() | Data set to data dictionary function

Usage

ds2dd(
  ds,
  record.id = "record_id",
  form.name = "basis",
  field.type = "text",
  field.label = NULL,
  include.column.names = FALSE,
  metadata = stRoke::metadata_names
)

Arguments

ds

data set

record.id

name or column number of id variable, moved to first row of data dictionary, character of integer. Default is "record_id".

form.name

vector of form names, character string, length 1 or length equal to number of variables. Default is "basis".

field.type

vector of field types, character string, length 1 or length equal to number of variables. Default is "text.

field.label

vector of form names, character string, length 1 or length equal to number of variables. Default is NULL and is then identical to field names.

include.column.names

Flag to give detailed output including new column names for original data set for upload.

metadata

Metadata dataframe. Default is the included stRoke::metadata_names.

Value

data.frame or list of data.frame and vector

Examples

talos$id <- seq_len(nrow(talos))
ds2dd(talos, record.id="id",include.column.names=FALSE)

MOVED Filter files in a folder

Description

This function filters files in a folder based on the provided filter.

Usage

files_filter(folder.path, filter.by, full.names = TRUE)

Arguments

folder.path

character. Path of the folder to be filtered

filter.by

character. Filter to be applied on the files

full.names

logical. Whether to return full file names or not

Value

character vector. Filtered files

Examples

# Gives path to files/folders with "tests" in the name in the 
# working directory
files_filter(getwd(),"tests")

Generic stroke study outcome

Description

Includes table 1, grotta bars and ordinal logistic regression plot. Please just use this function for illustration purposes. To dos: modify grottaBar and include as own function.

Usage

generic_stroke(df, group, score, strata = NULL, variables = NULL)

Arguments

df

Data set as data frame

group

Variable to group by

score

Outcome measure variable

strata

Optional variable to stratify by

variables

String of variable names to include in adjusted OLR-analysis

Value

Returns list with three elements

Examples

# generic_stroke(df = stRoke::talos, group = "rtreat", score = "mrs_6", 
# variables = c("hypertension","diabetes","civil"))

Plot multidimensional cognitive test scores

Description

Plot index scores from five dimensional cognitive testing. Includes option to facet.

Usage

index_plot(
  ds,
  id = "id",
  sub_plot = "_is",
  scores = c("_is", "_lo", "_up", "_per"),
  dom_names = c("immediate", "visuospatial", "verbal", "attention", "delayed", "total"),
  facet.by = NULL
)

Arguments

ds

complete data frame

id

colname of id column. Base for colouring

sub_plot

main outcome scores variable to plot

scores

variables to subset for plotting. Has to follow standard naming (is to be changed)

dom_names

domain names for axis naming

facet.by

variable to base facet_grid on

Value

ggplot element

Examples

index_plot(stRoke::score[score$event=="A",])

Helper function for labels in gtsummary

Description

Function to select labels from list of label pairs (format: age~"Age"). Alternative is to use attributes, eg from library(Hmisc).

Usage

label_select(lst, vec)

Arguments

lst

List of variables and labels (format: age~"Age")

vec

Vector of variables to be subset from the list

Value

List of labels ordered like vec, formatted like lst

Examples

vars<-c("hypertension", "diabetes", "mrs_1")
labels_all<-list(rtreat~"Trial treatment",
civil~"Cohabitation",
diabetes~"Known diabetes", 
hypertension~"Known hypertension", 
mrs_1~"One month mRS", 
mrs_6~"Six months mRS", 
'[Intercept]'~"Intercept")
label_select(labels_all,vars)

## With gtsummary::tbl_summary()
#stRoke::talos[vars] |> 
#gtsummary::tbl_summary(label = label_select(labels_all,vars))

Vector of REDCap metadata headers

Description

Vector of REDCap metadata headers

Usage

data(metadata_names)

Format

Vector of length 18 with REDCap metadata headers:

metadata_names

characterstrings

See Also

project-redcap(dot)org (currently the certificate is broken)

MFI domain score calculator

Description

MFI domain score calculator

Usage

mfi_domains(
  ds,
  reverse = TRUE,
  reverse.vars = c(2, 5, 9, 10, 13, 14, 16, 17, 18, 19)
)

Arguments

ds

data set of MFI scores, 20 columns

reverse

reverse scoring

reverse.vars

variables/columns to reverse

Value

tibble of domain scores

Examples

mfi_mess <- data.frame(matrix(
sample(c(" 1. ", "2. -A", "3.", " 4  ", "5.", NA),200,replace=TRUE),ncol=20))
mfi_mess |> mfi_domains()

Reverses relevant MFI subscores

Description

Reverses relevant MFI subscores

Usage

multi_rev(d, var)

Arguments

d

data frame or tibble

var

numeric vector of indices of columns to reverse

Value

data.frame or tibble depending on input

Examples

# rep_len(sample(1:5),length.out = 100) |> matrix(ncol=10) |> multi_rev(2:4)

Splits in n chunks

Description

Splits in n chunks

Usage

n_chunks(d, n, ...)

Arguments

d

data

n

number of chunks

...

arguments passed to internal chunks_of_n()

Value

List of chunks

Examples

lengths(n_chunks(d=seq_len(100),n=7,even=TRUE))
lengths(n_chunks(d=seq_len(100),n=7,even=FALSE))

Data frame with sample data of PASE score questionnaire

Description

Contains non-identifiable organic trial data. Sample data labels are in Danish.

Usage

data(pase)

Format

A data frame with 200 rows and 21 variables:

sample_pase01

item 01, factor

sample_pase01b

item 01b, factor

sample_pase02

item 02, factor

sample_pase02a

item 02a, factor

sample_pase03

item 03, factor

sample_pase03b

item 03b, factor

sample_pase04

item 04, factor

sample_pase04b

item 04b, factor

sample_pase05

item 05, factor

sample_pase05b

item 05b, factor

sample_pase06

item 06, factor

sample_pase06b

item 06b, factor

sample_pase07

item 07, factor

sample_pase08

item 08, factor

sample_pase09a

item 09a, factor

sample_pase09b

item 09b, factor

sample_pase09c

item 09c, factor

sample_pase09d

item 09d, factor

sample_pase10

item 10, factor

sample_pase10a

item 10a, numeric

sample_pase10b

item 10b, factor

PASE score calculator

Description

Calculates PASE score from raw questionnaire data.

Usage

pase_calc(ds, adjust_work = FALSE, consider.missing = c("Not available"))

Arguments

ds

data set

adjust_work

flag to set whether to include 10b type 1.

consider.missing

character vector of values considered missing. Default is TRUE.

Details

Labelling should be as defined by the questionnaire. 02-06 should start with 0:3, 02a-06b should start with 1:4.

Regarding work scoring

The score calculation manual available for the PASE questionnaire, all types of work should be included. According to the article by Washburn RA. et al (1999) sitting work is not included in the item 10 score. This differentiation is added with the option to set adjust_work to exclude item 10b category 1 work (set TRUE).

Regarding output

Output includes sub scores as well as sums, but also to columns assessing data quality and completeness. If any field has not been filled, score_incompletes will return TRUE. If all measures are missing score_missings is TRUE. If adjust_work==TRUE, 10b has to be filled, or score_incompletes will be set TRUE.

Value

data.frame

Examples

summary(pase_calc(stRoke::pase)[,13])
str(pase_calc(stRoke::pase))

Prints win_prob results

Description

Prints win_prob results

Usage

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

Arguments

x

win_prob results.

...

ignored for now

Value

Prints win_prob statistics.

MOVED Easy function for splitting numeric variable in quantiles

Description

Using base/stats functions cut() and quantile().

Usage

quantile_cut(
  x,
  groups,
  y = NULL,
  na.rm = TRUE,
  group.names = NULL,
  ordered.f = FALSE,
  inc.outs = FALSE,
  detail.list = FALSE
)

Arguments

x

Variable to cut.

groups

Number of groups.

y

alternative vector to draw quantile cuts from. Limits has to be within x. Default is NULL.

na.rm

Remove NA's. Default is TRUE.

group.names

Names of groups to split to. Default is NULL, giving intervals as names.

ordered.f

Set resulting vector as ordered. Default is FALSE.

inc.outs

Flag to include min(x) and max(x) as borders in case of y!=NULL.

detail.list

flag to include details or not

Value

vector or list with vector and details (length 2)

Examples

aa <- as.numeric(sample(1:1000,2000,replace = TRUE))
x <- 1:450
y <- 6:750
summary(quantile_cut(aa,groups=4,detail.list=FALSE)) ## Cuts quartiles

Data frame with sample data of cognitive testing score

Description

Contains non-identifiable organic trial data from a five-dimensional cognitive test.

Usage

data(score)

Format

A data frame with 20 rows and 26 variables:

id

id

event

event

a_is

domain a index score

b_is

domain b index score

c_is

domain c index score

d_is

domain d index score

e_is

domain e index score

i_is

total index score

a_lo

domain a lower ci

b_lo

domain b lower ci

c_lo

domain c lower ci

d_lo

domain d lower ci

e_lo

domain e lower ci

i_lo

total lower ci

a_up

domain a upper ci

b_up

domain b upper ci

c_up

domain c upper ci

d_up

domain d upper ci

e_up

domain e upper ci

i_up

total upper ci

a_per

domain a percentile

b_per

domain b percentile

c_per

domain c percentile

d_per

domain d percentile

e_per

domain e percentile

i_per

total percentile

Source Lines from a File

Description

Sources specific lines from a file

Usage

source_lines(file, lines, ...)

Arguments

file

A character string giving the path to the file to be sourced.

lines

A numeric vector of line numbers to be sourced.

...

Additional arguments to be passed to source.

Value

The result of source.

See Also

This function is borrowed from a gist by christophergandrud.

Examples

test_file <- tempfile(fileext = ".R")
writeLines(c("# Line 1", "2+2", "# Line 3"), test_file)
source_lines(test_file, 1:2, echo=TRUE)

Extract string based on regex pattern

Description

DEPRECATION: moved to agdamsbo/project.aid

Usage

str_extract(d, pattern)

Arguments

d

vector of character strings

pattern

regex pattern to match

Details

Use base::strsplit to

Value

vector of character strings

Examples

ls <- do.call(c,lapply(sample(4:8,20,TRUE),function(i){
paste(sample(letters,i,TRUE),collapse = "")}))
ds <- do.call(c,lapply(1:20,function(i){
paste(sample(ls,1),i,sample(ls,1),"23",sep = "_")}))
str_extract(ds,"([0-9]+)")

Data frame with sample of TALOS data

Description

Contains of non-identifiable subset of data from the TALOS trial.

Usage

data(talos)

Format

A data frame with 200 rows and 6 variables:

rtreat

Randomisation

mrs_1

Modified Rankin scale score at follow-up

mrs_6

Modified Rankin scale score at end of study

hypertension

Known hypertension

diabetes

Known diabetes

civil

Cohabitation status

Source

doi:10.1161/STROKEAHA.117.020067

Calculates the probability of winning

Description

Calculates the probability of winning (winP). In the referenced article Zou et al (2022) proposes a method for calculating probability of winning with a confidence interval an p-value testing.

Usage

win_prob(
  data,
  response = NULL,
  group = NULL,
  alpha = 0.05,
  beta = 0.2,
  group.ratio = 1,
  sample.size = FALSE,
  print.tables = FALSE,
  dec = 3
)

Arguments

data

A data frame containing the response and group variable.

response

The name of the response variable. Takes first column if empty.

group

The name of the group variable. Takes second column if empty.

alpha

The alpha level for the hypothesis test. Default is 0.05.

beta

The beta level for the sample size calculation. Default is 0.2.

group.ratio

The ratio of group sizes. Default is 1.

sample.size

Flag to include sample size calculation. Default is FALSE.

print.tables

Flag to print cumulative tables. Default is FALSE.

dec

Numeric for decimals to print. Default is 3.

Value

A list containing the win_prob statistics.

Source

doi:10.1161/STROKEAHA.121.037744

Examples

win_prob(data=stRoke::talos,response="mrs_6",group="rtreat")

MOVED Write ical object

Description

This function creates an ical file based on a data frame with mixed events. Export as .ics file using calendar::ic_write().

Usage

write_ical(
  df,
  date = "date",
  date.end = NA,
  title = "title",
  time.start = "start",
  time.end = "end",
  place = NA,
  place.def = NA,
  time.def = "10:00:00",
  time.dur = 60,
  descr = NA,
  link = NA,
  t.zone = "CET"
)

Arguments

df

A data frame with the calendar data

date

The name of the event date column in the data frame

date.end

The name of the end date column in the data frame

title

The name of the title column in the data frame

time.start

The name of the start time column in the data frame

time.end

The name of the end time column in the data frame

place

The name of the place column in the data frame

place.def

Default location to use when place is NA

time.def

Default start time to use when time.start is NA

time.dur

Default duration of the event in minutes, if time.end is NA

descr

Name of description/notes column if any.

link

Name of link column, if any.

t.zone

A character string of time zone for events. The string must be a time zone that is recognized by the user's OS.

Value

ical object

See Also

calendar package icalendar standard webpage

Examples

df <- data.frame(
  date = c("2020-02-10", "2020-02-11"),
  date.end = c("2020-02-13",NA),
  title = c("Conference", "Lunch"),
  start = c("12:00:00", NA),
  time.end = c("13:00:00", NA),
  note = c("Hi there","Remember to come"),
  link = c("https://icalendar.org","https://agdamsbo.github.io/stRoke/")
)

write_ical(
  df,
  date = "date",
  date.end = "date.end",
  title = "title",
  time.start = "start",
  time.end = "time.end",
  place.def = "Conference Room",
  descr = "note",
  link = "link"
)