Help for package integrity

Type:

Package

Title:

Assessing the Integrity and Trustworthiness of Clinical Trials Data

Version:

1.0.1

Date:

2026-05-19

VignetteBuilder:

knitr

Encoding:

UTF-8

Depends:

R (≥ 4.1.0)

Imports:

ggplot2, dplyr, janitor, gtsummary, ggpubr, lubridate, car, rlang

Suggests:

knitr, pkgload, readxl, rmarkdown, testthat (≥ 3.0.0), yaml

Description:

The integrity package implements the IPD Integrity Tool, a structured and transparent framework for evaluating the integrity of individual participant data (IPD) from randomised trials (see Hunter et al. (2024) <doi:10.1002/jrsm.1738> and <doi:10.32614/RJ-2017-008>). It supports users to identify potential issues, such as unusual data patterns, implausible values, lack of expected correlations, date violations, and inconsistencies. The package provides reproducible workflows for screening, documenting and summarising integrity concerns, and may be applied by evidence synthesists, editors, and others to determine whether a randomised trial may be considered sufficiently trustworthy to contribute to the evidence base that informs policy and practice.

License:

GPL-3

URL:

https://github.sydney.edu.au/Charles-Perkins-Centre-Data-Science-Hub/CPCDASH0010

Config/roxygen2/version:

8.0.0

Config/testthat/edition:

NeedsCompilation:

Packaged:

2026-05-25 23:23:47 UTC; jkan0113

Author:

Sol Libesman [aut, cre], Kylie Hunter [aut], David Nguyen [aut], Dario Strbenac [aut], Anne Lene Seidler [aut], Jie Kang [aut]

Maintainer:

Sol Libesman <sol.libesman@sydney.edu.au>

Repository:

CRAN

Date/Publication:

2026-05-26 10:10:02 UTC

Check Variability Between Intervention and Control Groups

Description

Internal function documentation for developers. Levene's test for differential variability.

Usage

.differential_variability(dataset_subset, intervention, alpha)

Arguments

dataset_subset

A data.frame of clinical trial data subset to only numeric columns.

intervention

Column name of intervention indicator.

alpha

p-value signficance threshold.

Value

One-row data.frame with a Pass or Fail indicator.

Examples

library(readxl)
examplePath <- system.file("extdata", "dataset.xlsx", package = "integrity")
dataset <- read_excel(examplePath)
library(yaml)
infoPath <- system.file("extdata", "variables.yaml", package = "integrity")
info <- read_yaml(infoPath)
dataset <- integrity:::.prepare_data(dataset, info)
numeric_columns <- info$baseline$numeric
dataset_subset <- dataset[, c(numeric_columns, info$intervention)]
integrity:::.differential_variability(dataset_subset, info$intervention, 0.05)

Check Day of Week of Randomisation for Non-uniformity

Description

Internal function documentation for developers. Dates are converted into days of the week and tested for association to intervention status using chisq.test.

Usage

.imbalance_day_intervention(dataset, intervention, intervention_date, unexpected, alpha)

Arguments

dataset

A data.frame of clinical trial data.

intervention

Column name of column storing intervention status indicator.

intervention_date

Column name of column storing intervention date.

unexpected

List of elements specifying implausible values. Names of list are column names. One must be "days".

alpha

p-value signficance threshold.

Value

A list of length two. check_table: One-row data.frame with a Pass or Fail indicator. images: Bar chart of days of week. Bars are coloured by intervention status.

Examples

library(readxl)
examplePath <- system.file("extdata", "dataset.xlsx", package = "integrity")
dataset <- read_excel(examplePath)
library(yaml)
infoPath <- system.file("extdata", "variables.yaml", package = "integrity")
info <- read_yaml(infoPath)
integrity:::.imbalance_day_intervention(dataset, info$intervention, info$enrollment$randomisation,
                                        info$unexpected, 0.05)

Check Variables for Implausible Values

Description

Internal function documentation for developers. Each column is checked for violations.

Usage

.implausible_values(dataset, participantID, unexpected, enrollment)

Arguments

dataset

A data.frame of clinical trial data.

participantID

Column name of column storing participant IDs.

unexpected

List of elements specifying implausible values. Names of list are column names

enrollment

Column name of column storing enrollment dates.

Value

A data.frame with one row for each violation or one row with Pass if no rows violated the check.

Examples

library(readxl)
examplePath <- system.file("extdata", "dataset.xlsx", package = "integrity")
dataset <- read_excel(examplePath)
library(yaml)
infoPath <- system.file("extdata", "variables.yaml", package = "integrity")
info <- read_yaml(infoPath)
integrity:::.implausible_values(dataset, info$participantID, info$unexpected, info$enrollment)

Check clinical Data Matches its Data Specification

Description

Internal function documentation for developers. Firstly, the function checks all expected variables are present as column names. Then, it converts any columns defined as categorical to factors. Finally, it removes any columns that have all missing values.

Usage

.prepare_data(dataset, info)

Arguments

dataset

A data.frame of clinical trial data.

info

A named list of column names corresponding to different aspects of the clinical trial. See the vignette for detailed requirements.

Value

If no missing colums, a data.frame that has been filtered for columns containing all missing values.

Examples

library(readxl)
examplePath <- system.file("extdata", "dataset.xlsx", package = "integrity")
dataset <- read_excel(examplePath)
library(yaml)
infoPath <- system.file("extdata", "variables.yaml", package = "integrity")
info <- read_yaml(infoPath)
integrity:::.prepare_data(dataset, info)

Check Baseline Variables for Repetition

Description

Internal function documentation for developers. Essentially a wrapper around get_dupes of janitor.

Usage

.repeating_baseline(dataset_subset, type = c("across", "within", "across_rare"))

Arguments

dataset_subset

A data.frame of clinical trial data subset to only the baseline variables.

type

If "across", across all baseline variables. If "within", within each baseline variable. If "across_rare", across the baseline variables but only for participants who had a rare outcome.

Value

A data.frame with one row for each repetition or just one row reporting Pass status for the check.

Examples

library(readxl)
examplePath <- system.file("extdata", "dataset.xlsx", package = "integrity")
dataset <- read_excel(examplePath)
library(yaml)
infoPath <- system.file("extdata", "variables.yaml", package = "integrity")
info <- read_yaml(infoPath)
dataset_subset <- dataset[, unlist(info$baseline)]
integrity:::.repeating_baseline(dataset_subset)

Check Terminal Digits of Numerical Variables for Non-uniformity

Description

Internal function documentation for developers. Creates a distribution plot of terminal digits

Usage

.terminal_digits(dataset_subset)

Arguments

dataset_subset

A data.frame of clinical trial data subset to only numeric columns.

Value

A ggplot2 plot.

Examples

library(readxl)
examplePath <- system.file("extdata", "dataset.xlsx", package = "integrity")
dataset <- read_excel(examplePath)
library(yaml)
infoPath <- system.file("extdata", "variables.yaml", package = "integrity")
info <- read_yaml(infoPath)
numeric_columns <- info$baseline$numeric
dataset_subset <- dataset[, unlist(info$baseline)]
integrity:::.terminal_digits(dataset_subset)

Check Pairs of Variables Expected to be Correlated

Description

Internal function documentation for developers. Essentially, cor.test.

Usage

.unexpectedly_uncorrelated(dataset_subset, pairs, alpha)

Arguments

dataset_subset

A data.frame of clinical trial data subset to numeric columns.

pairs

List of elements, each of length two. The elements are column names.

alpha

p-value signficance threshold.

Value

A list of length two. check_table: One-row data.frame with a Pass or Fail indicator for each variable pair. images: Scatter plots.

Examples

library(readxl)
examplePath <- system.file("extdata", "dataset.xlsx", package = "integrity")
dataset <- read_excel(examplePath)
library(yaml)
infoPath <- system.file("extdata", "variables.yaml", package = "integrity")
info <- read_yaml(infoPath)
integrity:::.unexpectedly_uncorrelated(dataset, info$correlated, 0.05)

Read Dataset Metadata from an Excel Template

Description

Reads a metadata workbook template and converts it into the named list structure required by run_checks().

Usage

read_metadata_excel(path, sheet = 1)

Arguments

path

Path to an Excel workbook containing metadata rows.

sheet

Sheet name or position to read. Default: 1.

Value

A named list suitable for the info argument of run_checks().

Examples

if(interactive())
{
  example_path <- system.file("extdata", "variables_template.xlsx", package = "integrity")
  dataset_info <- read_metadata_excel(example_path)
  names(dataset_info)
}

Read Dataset Metadata from an R Script

Description

Sources an R script template and returns the metadata list required by run_checks().

Usage

read_metadata_r(path, object_name = "dataset_info")

Arguments

path

Path to an R script containing a metadata object.

object_name

Name of the object to return from the R script. Default: "dataset_info".

Value

A named list suitable for the info argument of run_checks().

Examples

if(interactive())
{
  example_path <- system.file("extdata", "variables_template.R", package = "integrity")
  dataset_info <- read_metadata_r(example_path)
  names(dataset_info)
}

Run a Suite of Integrity Checks Based on Dataset Annotation

Description

Depending on the characteristics of the variables, some test may be skipped if the data type required for the test is not present.

Usage

run_checks(dataset, info, alpha = 0.05)

Arguments

dataset

A data.frame of clinical trial data.

info

A named list of column names corresponding to different aspects of the clinical trial. See the vignette for detailed requirements.

alpha

Default: 0.05. For checks which use a statistical test, the p-value threshold at which to report a failure.

Value

A list with the element named "check_table" having the table of passes and fails, the element named "detail_tables" storing additional per-variable results for selected checks, the element named "images" storing ggplot2 plots and the element named "summary_table" having an overview table of the baseline and outcome variables split by intervention.

Examples

if(interactive())
{
  library(readxl)
  examplePath <- system.file("extdata", "dataset.xlsx", package = "integrity")
  dataset <- read_excel(examplePath)
  library(yaml)
  example_path <- system.file("extdata", "variables.yaml", package = "integrity")
  dataset_info <- read_yaml(example_path)
  result <- run_checks(dataset, dataset_info)
  names(result)
}

Package {integrity}

Check Variability Between Intervention and Control Groups

Description

Usage

Arguments

Value

Examples

Check Day of Week of Randomisation for Non-uniformity

Description

Usage

Arguments

Value

Examples

Check Variables for Implausible Values

Description

Usage

Arguments

Value

Examples

Check clinical Data Matches its Data Specification

Description

Usage

Arguments

Value

Examples

Check Baseline Variables for Repetition

Description

Usage

Arguments

Value

Examples

Check Terminal Digits of Numerical Variables for Non-uniformity

Description

Usage

Arguments

Value

Examples

Check Pairs of Variables Expected to be Correlated

Description

Usage

Arguments

Value

Examples

Read Dataset Metadata from an Excel Template

Description

Usage

Arguments

Value

Examples

Read Dataset Metadata from an R Script

Description

Usage

Arguments

Value

Examples

Run a Suite of Integrity Checks Based on Dataset Annotation

Description

Usage

Arguments

Value

Examples