• Deterministic computation: Reproducible workflows assume that computational processes are deterministic, meaning that given the same inputs and parameters, they will always produce identical outputs. This requires careful handling of random number generation (using fixed seeds), addressing floating-point precision issues, and managing platform-specific behaviors that could affect results.
• Comprehensive documentation: The approach assumes that all data cleaning decisions, including their rationale and implementation details, can and should be explicitly documented. This includes defining acceptable ranges for variables, specifying rules for handling missing data, outlining outlier detection criteria, and justifying any transformations applied to the raw data.
• Separation of concerns: Reproducible workflows assume a clear separation between data, code, and environment. Raw data is treated as immutable, with all transformations implemented through code that is version-controlled. This separation ensures that the original data remains intact and that all changes are traceable and reversible.
• Accessibility and transferability: The methodology assumes that all components necessary for reproduction—including code, environment specifications, and documentation—can be effectively transferred to and executed by other researchers. This requires attention to licensing, dependencies, and platform compatibility issues.
• Iterative refinement: Reproducible workflows acknowledge that data cleaning is an iterative process requiring multiple cycles of inspection, cleaning, and validation. The approach assumes that these iterations can be tracked and managed systematically, with clear documentation of how decisions evolve based on increasing familiarity with the dataset.

1. Version Control Implementation

Git-based version control for data cleaning scripts:

• R Implementation: # Initialize Git repository with R project
# In terminal or R console with usethis
library(usethis)
create_project("~/projects/clinical_trial_cleaning")
use_git()

# Track changes with meaningful commits
# In terminal
git add data_cleaning_functions.R
git commit -m "Add validation rules for lab values with clinical ranges"
• Python Implementation: # Use DVC for data version control
# In terminal
pip install dvc
dvc init
dvc add raw_patient_data.csv
git add raw_patient_data.csv.dvc .gitignore
git commit -m "Add raw patient data with DVC tracking"

2. Data Validation Framework

• R Implementation with validate package: library(validate)

# Define validation rules
rules <- validator(
age >= 18, # Inclusion criteria
systolic_bp >= 70 & systolic_bp <= 220, # Physiological range
diastolic_bp >= 40 & diastolic_bp <= 120,
if(!is.na(death_date)) death_date >= enrollment_date, # Logical temporal sequence
is_complete(patient_id), # Required fields
is_unique(patient_id) # No duplicates
)

# Apply rules to data
validation_results <- confront(clinical_data, rules)
summary(validation_results)

# Generate validation report
library(validatetools)
validation_report <- validate_report(validation_results, clinical_data)
• Python Implementation with Great Expectations: import great_expectations as ge

# Load data as a Great Expectations DataFrame
df = ge.read_csv("clinical_data.csv")

# Define expectations
df.expect_column_values_to_be_between("age", min_value=18, max_value=100)
df.expect_column_values_to_be_between("systolic_bp", min_value=70, max_value=220)
df.expect_column_values_to_be_between("diastolic_bp", min_value=40, max_value=120)
df.expect_column_values_to_not_be_null("patient_id")
df.expect_column_values_to_be_unique("patient_id")

# Validate and save results
validation_result = df.validate()
print(validation_result)

3. Pipeline Automation

• R Implementation with targets package: # In _targets.R file
library(targets)

source("R/data_cleaning_functions.R")

list(
tar_target(raw_data, read_csv("data/raw_clinical_data.csv")),
tar_target(validated_data, validate_clinical_ranges(raw_data)),
tar_target(imputed_data, impute_missing_values(validated_data)),
tar_target(derived_variables, create_derived_variables(imputed_data)),
tar_target(final_clean_data, remove_outliers(derived_variables)),
tar_target(quality_report, generate_data_quality_report(final_clean_data))
)

# Execute pipeline
# In R console
library(targets)
tar_make()
• Python Implementation with Kedro: # Install and set up Kedro
# In terminal
pip install kedro
kedro new --starter=pandas-iris

# In conf/base/catalog.yml
raw_clinical_data:
type: pandas.CSVDataSet
filepath: data/01_raw/clinical_data.csv

clean_clinical_data:
type: pandas.CSVDataSet
filepath: data/03_primary/clean_clinical_data.csv

# In src/pipeline.py
from kedro.pipeline import Pipeline, node
from .nodes import validate_data, handle_missing, create_features

def create_pipeline():
return Pipeline(
[
node(
func=validate_data,
inputs="raw_clinical_data",
outputs="validated_data",
name="data_validation",
),
node(
func=handle_missing,
inputs="validated_data",
outputs="complete_data",
name="missing_data_handling",
),
node(
func=create_features,
inputs="complete_data",
outputs="clean_clinical_data",
name="feature_engineering",
),
]
)

# Run pipeline
# In terminal
kedro run

4. Environment Standardization

• R Implementation with renv: # Initialize dependency management
library(renv)
renv::init()

# Install required packages
install.packages(c("dplyr", "tidyr", "validate", "mice"))

# Snapshot dependencies
renv::snapshot()

# Share with collaborators
# They run:
# renv::restore()
• Python Implementation with Docker: # Dockerfile
FROM python:3.9-slim

WORKDIR /app

COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

COPY . .

CMD ["python", "run_cleaning_pipeline.py"]

# Build and run
# In terminal
docker build -t clinical-data-cleaning .
docker run -v $(pwd)/data:/app/data clinical-data-cleaning

5. Integrated Documentation

• R Implementation with R Markdown: ---
title: "Clinical Trial Data Cleaning Protocol"
author: "Research Team"
date: "`r Sys.Date()`"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(validate)
```

## Data Cleaning Workflow

### 1. Data Loading and Initial Assessment

```{r load-data}
raw_data <- read_csv("data/raw_clinical_data.csv")
glimpse(raw_data)
summary(raw_data)
```

### 2. Validation Rules

```{r validation}
rules <- validator(
age >= 18,
systolic_bp >= 70 & systolic_bp <= 220
)

validation_results <- confront(raw_data, rules)
summary(validation_results)
```
• Python Implementation with Jupyter Notebooks and nbdev: # Install nbdev
# In terminal
pip install nbdev

# In Jupyter notebook
# # Clinical Data Cleaning Pipeline
# > Documented data cleaning workflow for multicenter trial

# ## Import libraries

import pandas as pd
import numpy as np
import great_expectations as ge

# ## Load and examine raw data

def load_clinical_data(filepath):
"""
Load the raw clinical trial data

Args:
filepath: Path to CSV file

Returns:
DataFrame with raw clinical data
"""
return pd.read_csv(filepath)

raw_data = load_clinical_data("data/raw_clinical_data.csv")
raw_data.info()
raw_data.describe()

When interpreting the outputs of reproducible data cleaning workflows:

• Data Quality Metrics: Evaluate quantitative measures of data quality before and after cleaning. These include completeness (percentage of non-missing values), validity (percentage of values meeting domain constraints), consistency (logical coherence between related variables), and uniqueness (absence of duplicates). A successful workflow should show improvements across these dimensions with clear documentation of which records failed which validation rules.
• Transformation Impact Assessment: Compare descriptive statistics and distributions of key variables before and after cleaning. Significant changes in means, medians, or standard deviations may indicate potential bias introduction during cleaning. Report both raw and transformed statistics, with particular attention to variables critical for primary and secondary outcomes in medical research.
• Reproducibility Verification: Assess whether independent execution of the workflow by different team members or on different computing environments produces identical or acceptably similar results. Minor numerical differences (within \(10^{-10}\)) may occur due to floating-point arithmetic but should not affect scientific conclusions. Document any platform-specific behaviors that impact reproducibility.
• Pipeline Execution Metrics: Evaluate the efficiency and reliability of the workflow through execution time, memory usage, and failure rates. A well-designed pipeline should execute consistently without manual intervention and include appropriate error handling and recovery mechanisms. Report computational requirements to set appropriate expectations for reproduction attempts.
• Documentation Completeness: Assess whether the generated documentation adequately explains all cleaning decisions, their rationale, and their implementation. Complete documentation should enable a knowledgeable reader to understand what transformations were applied, why they were necessary, and how they were implemented, without needing to decipher code directly.
• Data Provenance Clarity: Verify that the workflow maintains clear lineage from raw data to final cleaned dataset. Each transformation should be traceable, with the ability to identify which specific cleaning operations affected which records and variables. This transparency is essential for defending analytical choices during peer review and regulatory submissions.

• Clinical Trial Data Management: Implementing reproducible cleaning workflows for multi-center randomized controlled trials; standardizing laboratory value units across sites; validating case report form data against protocol-specific eligibility criteria; documenting protocol deviation handling; creating CDISC-compliant datasets for regulatory submissions; generating data cleaning audit trails for GCP compliance.
• Epidemiological Cohort Studies: Harmonizing variables across multiple follow-up waves; implementing consistent handling of loss-to-follow-up; standardizing outcome definitions over time; documenting evolving exclusion criteria; creating reproducible derivation of composite risk scores; generating transparent documentation of cohort selection for STROBE-compliant reporting.
• Electronic Health Record Research: Standardizing diagnostic and procedure codes across healthcare systems; implementing reproducible phenotype algorithms; documenting temporal windowing decisions for exposure-outcome relationships; creating consistent approaches to handling fragmented care episodes; generating transparent feature engineering for predictive modeling.
• Systematic Reviews and Meta-analyses: Implementing reproducible data extraction workflows from primary studies; standardizing outcome measures across studies; documenting inclusion/exclusion decisions; creating transparent risk-of-bias assessments; generating reproducible evidence synthesis for PRISMA-compliant reporting.
• Genomic and Biomarker Studies: Implementing reproducible quality control for high-dimensional data; standardizing batch effect correction; documenting outlier handling in expression data; creating transparent pipelines for variant calling and annotation; generating reproducible biomarker discovery workflows with appropriate multiple testing correction.

• Initial development overhead: Establishing reproducible workflows requires significant upfront investment in code structure, documentation, and testing, which may be perceived as disproportionate for smaller studies. Alternative: Adopt graduated reproducibility practices, starting with basic version control and documentation templates, then incrementally adding automation and validation as project complexity increases. For small projects, literate programming approaches like R Markdown or Jupyter notebooks may offer a lighter-weight entry point to reproducibility.
• Technical skill barriers: Implementing comprehensive reproducible workflows requires proficiency with programming, version control systems, and workflow management tools that may exceed the training of many medical researchers. Alternative: Utilize graphical workflow tools like KNIME or Orange that provide visual pipeline construction while maintaining reproducibility; consider collaborative approaches where methodologists develop templates that clinical researchers can adapt with minimal technical overhead.
• Handling of sensitive data: Fully reproducible workflows may conflict with privacy regulations and data sharing restrictions common in medical research, particularly when working with protected health information. Alternative: Implement two-tier reproducibility approaches where sensitive data processing steps are encapsulated with restricted access, while subsequent analysis of de-identified data remains fully open; use synthetic data generators to create privacy-preserving test datasets that mimic the structure and statistical properties of sensitive data.
• Evolving standards and tool obsolescence: Reproducible workflows depend on specific software versions and packages that may become obsolete or incompatible over time, particularly for long-term studies. Alternative: Containerization technologies like Docker or Singularity can preserve entire computational environments; package management systems like Conda, renv, or pip with requirements.txt files can explicitly document dependencies; consider periodic workflow modernization with careful validation against preserved reference outputs.

When reporting reproducible data cleaning workflows in medical research publications:

• Include a dedicated "Data Processing" or "Data Preparation" section in the Methods that explicitly describes the data cleaning workflow, including software tools, packages with version numbers, and references to publicly available code repositories where applicable.
• Report quantitative data quality metrics before and after cleaning, including the number of records affected by each major cleaning operation, the percentage of missing data for key variables, and how these issues were addressed.
• Provide a data flow diagram illustrating the progression from raw data to analysis-ready datasets, highlighting major transformation steps, validation checkpoints, and points where records may have been excluded.
• Document all exclusion criteria applied during data cleaning with corresponding sample sizes at each step, following CONSORT (for clinical trials), STROBE (for observational studies), or RECORD (for routinely collected health data) guidelines.
• Specify the handling approach for key data issues including missing values (e.g., complete case analysis, multiple imputation), outliers (e.g., winsorization, robust methods), and derived variables (providing exact formulas).
• Include a data availability statement that addresses not only the final dataset but also the reproducibility materials (code, configuration files, validation rules) with appropriate access mechanisms that respect privacy constraints.
• For journals supporting enhanced content, provide links to executable notebooks (e.g., Code Ocean, Binder) that demonstrate key data cleaning steps with synthetic or de-identified data examples.
• Report any deviations from pre-registered or protocol-specified data cleaning procedures, with justification for why changes were necessary and assessment of their potential impact on results.

Our Manuscript Statistical Review service frequently identifies these errors in reproducible data cleaning

Our Manuscript Statistical Review service frequently identifies these errors in reproducible data cleaning workflows:

• Undocumented exclusion criteria: Removing observations based on ad hoc or post-hoc criteria without explicit documentation, leading to potential selection bias. This often manifests as inconsistencies between reported sample sizes and actual analysis datasets, particularly when exclusions are implemented across multiple cleaning scripts without centralized tracking.
• Inappropriate imputation methods: Implementing simplistic missing data approaches (e.g., mean imputation, last observation carried forward) that fail to account for the missing data mechanism, potentially biasing associations and underestimating standard errors. Reproducible workflows should document the missing data pattern analysis that informed the imputation approach selection.
• Inconsistent outlier handling: Applying different outlier detection and handling methods across variables without clear justification, or implementing outlier handling that varies between descriptive and inferential analyses. A proper workflow should apply consistent, pre-specified outlier criteria with clear documentation of both the detection method and the handling approach.
• Untracked data transformations: Implementing variable transformations (e.g., log transformations, categorization of continuous variables) without documenting the impact on distributions and associations. Reproducible workflows should include before-and-after comparisons of key summary statistics and visualizations to assess transformation effects.
• Pipeline parameter sensitivity: Failing to assess how changes in data cleaning parameters (e.g., outlier thresholds, imputation model specifications) affect final results. Robust reproducible workflows should include sensitivity analyses that quantify how key findings change under different reasonable data cleaning decisions.
• Temporal drift ignorance: Not accounting for potential changes in data collection procedures, coding practices, or measurement instruments over time in longitudinal studies. Reproducible workflows should include explicit checks for temporal consistency and document any harmonization procedures applied to maintain comparability.