clinCompare: Dataset Comparison with CDISC Validation

In clinCompare: Dataset Comparison with 'CDISC' Validation for Clinical Trial Data

Introduction

clinCompare is an R package for comparing datasets at the dataset, variable, and observation level. For clinical trial data, an optional CDISC validation layer checks SDTM and ADaM conformance automatically. The package is designed for statistical programmers, data managers, and regulatory professionals who need to ensure data quality and compliance with industry standards.

Key Features

• Compare dimensions, variable names, data types, and values in a single call
• Key-based row matching with auto-detected CDISC ID variables
• CDISC validation for 51 SDTM domains and 14 ADaM datasets
• Export results to HTML, plain text, or Excel
• Batch compare entire submissions across two directories
• Numeric tolerance for floating-point comparisons

Getting Started

library(clinCompare)

Basic Dataset Comparison

Comparing Two Data Frames

The compare_datasets() function gives a comprehensive overview: dimension checks, variable comparison, type mismatches, and row-level value differences.

baseline <- data.frame(
  USUBJID = c("SUBJ01", "SUBJ02", "SUBJ03"),
  AGE     = c(45, 52, 38),
  SEX     = c("M", "F", "M"),
  RACE    = c("WHITE", "WHITE", "ASIAN"),
  stringsAsFactors = FALSE
)

updated <- data.frame(
  USUBJID = c("SUBJ01", "SUBJ02", "SUBJ03"),
  AGE     = c(45, 53, 38),
  SEX     = c("M", "F", "F"),
  RACE    = c("WHITE", "WHITE", "ASIAN"),
  stringsAsFactors = FALSE
)

result <- compare_datasets(baseline, updated)
result

The result is a structured list you can drill into programmatically:

# Per-column difference counts
result$observation_comparison$discrepancies

# Row-level details for a specific variable
result$observation_comparison$details$SEX

Comparing Variables

Use compare_variables() to focus on structural differences between two datasets -- column names, data types, and variable ordering.

df_a <- data.frame(
  USUBJID = c("SUBJ01", "SUBJ02"),
  AGE     = c(45, 52),
  SEX     = c("M", "F"),
  stringsAsFactors = FALSE
)

df_b <- data.frame(
  USUBJID = c("SUBJ01", "SUBJ02"),
  AGE     = c(45L, 52L),
  WEIGHT  = c(75.5, 80.2),
  stringsAsFactors = FALSE
)

compare_variables(df_a, df_b)

Comparing Observations

Use compare_observations() for row-by-row value comparison on common columns:

df1 <- data.frame(
  ID    = c(1, 2, 3),
  SCORE = c(80, 90, 70),
  stringsAsFactors = FALSE
)

df2 <- data.frame(
  ID    = c(1, 2, 3),
  SCORE = c(80, 95, 70),
  stringsAsFactors = FALSE
)

compare_observations(df1, df2)

Data Preparation

Cleaning Data

Remove duplicates and standardize text case before comparing:

messy <- data.frame(
  NAME  = c("Alice", "alice", "Bob", "Bob"),
  SCORE = c(100, 100, 85, 85),
  stringsAsFactors = FALSE
)

clean_dataset(messy, remove_duplicates = TRUE, convert_to_case = "upper")

Sorting and Filtering

Prepare two datasets identically before comparison:

df_unsorted1 <- data.frame(
  REGION = c("West", "East", "North"),
  SALES  = c(150, 200, 180)
)

df_unsorted2 <- data.frame(
  REGION = c("East", "North", "West"),
  SALES  = c(210, 185, 160)
)

prepped <- prepare_datasets(df_unsorted1, df_unsorted2, sort_columns = "REGION")
prepped$df1
prepped$df2

Group-Wise Comparison

Compare datasets within specific subgroups. Useful for multi-site or multi-arm studies:

site_data_v1 <- data.frame(
  SITEID = c("SITE01", "SITE01", "SITE02", "SITE02"),
  SUBJID = c("S01", "S02", "S03", "S04"),
  AGE    = c(45, 52, 38, 61)
)

site_data_v2 <- data.frame(
  SITEID = c("SITE01", "SITE01", "SITE02", "SITE02"),
  SUBJID = c("S01", "S02", "S03", "S04"),
  AGE    = c(45, 53, 38, 62)
)

by_site <- compare_by_group(site_data_v1, site_data_v2, group_vars = "SITEID")
names(by_site)

CDISC Comparison

What is CDISC?

CDISC (Clinical Data Interchange Standards Consortium) provides standardized formats for regulatory submissions:

• SDTM (Study Data Tabulation Model): Raw data from clinical trials
• ADaM (Analysis Data Model): Derived datasets used for statistical analysis

CDISC validation ensures that datasets meet industry standards and regulatory requirements. For official CDISC standards documentation, see https://www.cdisc.org/standards.

Auto-Detecting CDISC Domains

clinCompare auto-detects the CDISC domain of a dataset using column matching, ADaM indicator columns, and filename hints:

dm_data <- data.frame(
  STUDYID  = rep("STUDY01", 3),
  USUBJID  = c("SUBJ01", "SUBJ02", "SUBJ03"),
  AGE      = c(45, 62, 51),
  SEX      = c("M", "F", "M"),
  RACE     = c("WHITE", "BLACK", "ASIAN"),
  ARMCD    = c("TRT", "PBO", "TRT"),
  ARM      = c("Treatment", "Placebo", "Treatment"),
  stringsAsFactors = FALSE
)

detect_cdisc_domain(dm_data)

Comparing with CDISC Validation

cdisc_compare() is the flagship function. It compares two datasets, auto-detects the CDISC domain and key variables, performs key-based row matching, and validates against CDISC standards -- all in one call.

dm_v1 <- data.frame(
  STUDYID  = rep("STUDY01", 3),
  USUBJID  = c("SUBJ01", "SUBJ02", "SUBJ03"),
  AGE      = c(45, 62, 51),
  SEX      = c("M", "F", "M"),
  RACE     = c("WHITE", "BLACK", "ASIAN"),
  ARMCD    = c("TRT", "PBO", "TRT"),
  ARM      = c("Treatment", "Placebo", "Treatment"),
  RFSTDTC  = c("2024-01-15", "2024-01-16", "2024-01-17"),
  stringsAsFactors = FALSE
)

dm_v2 <- data.frame(
  STUDYID  = rep("STUDY01", 3),
  USUBJID  = c("SUBJ01", "SUBJ02", "SUBJ03"),
  AGE      = c(45, 62, 52),
  SEX      = c("M", "F", "M"),
  RACE     = c("WHITE", "BLACK", "ASIAN"),
  ARMCD    = c("TRT", "PBO", "TRT"),
  ARM      = c("Treatment", "Placebo", "Treatment"),
  RFSTDTC  = c("2024-01-15", "2024-01-16", "2024-01-17"),
  stringsAsFactors = FALSE
)

cdisc_result <- cdisc_compare(dm_v1, dm_v2, domain = "DM", standard = "SDTM")
cdisc_result

Validating a Single Dataset

Use validate_cdisc() to check a dataset against CDISC standards without comparing it to another dataset:

validation <- validate_cdisc(dm_v1, domain = "DM", standard = "SDTM")

Extracting All Differences

get_all_differences() returns every value-level difference as a single long-format data frame, making it easy to filter, count, or export:

diffs <- get_all_differences(cdisc_result)
diffs

Exporting Reports

export_report() auto-detects the output format from the file extension:

# HTML report
export_report(cdisc_result, file.path(tempdir(), "dm_report.html"))

# Text report
export_report(cdisc_result, file.path(tempdir(), "dm_report.txt"))

Excel export requires the openxlsx package:

# Excel workbook with Summary, Variable Diffs, Value Diffs, and CDISC tabs
export_report(cdisc_result, file.path(tempdir(), "dm_report.xlsx"))

Batch Comparing a Submission

compare_submission() scans two directories, matches files by name, and runs cdisc_compare() on every matched pair. Domain, standard, and key variables are all auto-detected per file.

results <- compare_submission(
  base_dir    = "submission_v1/",
  compare_dir = "submission_v2/",
  output_file = "submission_diff.xlsx"
)

CDISC Coverage

clinCompare ships with hand-curated metadata for 51 SDTM domains (IG 3.4, with 3.3 support) and 14 ADaM datasets (IG 1.3, with 1.2/1.1 provenance tracking).

SDTM domains: AE, AG, BE, BS, CE, CM, CO, CP, DA, DD, DM, DS, DV, EC, EG, EX, FA, GF, HO, IE, IS, LB, MB, MH, MI, ML, MS, PC, PE, PP, PR, QS, RELREC, RS, SC, SE, SM, SS, SU, SUPPQUAL, SV, TA, TD, TE, TI, TM, TR, TS, TU, TV, VS.

ADaM datasets: ADAE, ADCM, ADEG, ADEFF, ADEX, ADLB, ADMH, ADPC, ADPP, ADRS, ADSL, ADTR, ADTTE, ADVS.

Disclaimer: clinCompare is a quality-assurance and exploratory analysis tool. It is not a substitute for official CDISC compliance validation software (e.g., Pinnacle 21). For regulatory submissions, always cross-reference with your organization's validated tools.

Summary

clinCompare provides a complete workflow for dataset comparison in clinical trials: compare any two data frames with compare_datasets(), add CDISC validation with cdisc_compare(), batch process entire submissions with compare_submission(), and export results to HTML, text, or Excel with export_report().

For more information and additional examples, visit the GitHub repository.

clinCompare documentation built on Feb. 19, 2026, 1:07 a.m.