Data Cleaning and Formatting Services

Enhancing Data Accuracy and Consistency for Reliable Research Outcomes

High-quality clinical and real-world evidence (RWE) research relies on clean, well-structured, and standardized datasets. Inconsistent, incomplete, or duplicate data can introduce bias, reduce statistical power, and compromise decision-making in clinical trials, epidemiological studies, and health economics and outcomes research (HEOR).

At Clievi, we specialize in data cleaning and formatting services to ensure that your datasets are accurate, structured, and compliant with regulatory and statistical standards. Our advanced data preprocessing techniques optimize data integrity, improving the reliability of subsequent analyses in systematic reviews, meta-analyses, real-world data (RWD) studies, and predictive modeling.

Our Data Cleaning and Formatting Services

1. Data Cleaning for High-Quality Research Outputs

• Handling Missing Data – Addressing missing values through imputation techniques (mean, median, mode imputation, regression-based imputation, multiple imputation).
• Duplicate Detection and Removal – Identifying and eliminating redundant records, duplicate patient entries, or duplicate study data in research datasets.
• Outlier Identification and Correction – Detecting statistical anomalies, erroneous data points, and biologically implausible values using descriptive statistics, Z-scores, and IQR methods.
• Standardization of Data Variables – Ensuring consistent naming conventions, coding formats, and unit conversions across datasets.
• Data Integrity Checks – Implementing automated validation rules to detect inconsistencies, missing fields, and incorrect data formats.

2. Data Formatting for Standardized and Structured Datasets

• Normalization and Structuring – Converting unstructured data into standardized, well-organized datasets for machine learning models, meta-analyses, and statistical evaluation.
• Dataset Merging and Harmonization – Integrating multiple datasets from diverse sources (clinical trials, EMRs, claims data, and registries) while maintaining data coherence.
• Data Transformation and Recoding – Formatting data into required structures for statistical software (SPSS, STATA, SAS, R, Python).
• CDISC Compliance (SDTM & ADaM Standards) – Structuring clinical trial data according to Clinical Data Interchange Standards Consortium (CDISC) guidelines, including Study Data Tabulation Model (SDTM) and Analysis Data Model (ADaM).
• MeSH Term and Ontology Mapping – Standardizing medical terminologies using Medical Subject Headings (MeSH), SNOMED CT, ICD-10, and RxNorm for interoperability in clinical research.

3. Quality Control and Regulatory Compliance in Data Processing

• Regulatory Compliance Alignment – Ensuring that data cleaning and formatting adhere to:
  • Good Clinical Data Management Practices (GCDMP)
  • FDA 21 CFR Part 11 for electronic records compliance
  • EMA & ICH-GCP guidelines for clinical research datasets
  • Real-World Evidence (RWE) & Health Technology Assessment (HTA) standards
• Automated Data Validation Checks – Implementing error detection algorithms to enhance data integrity and auditability.
• Metadata Documentation – Generating detailed data dictionaries, variable descriptions, and provenance tracking for transparent, reproducible research.

4. Data Cleaning for Real-World Evidence (RWE) and Health Economics and Outcomes Research (HEOR)

• Longitudinal Data Cleaning – Standardizing cohort datasets, patient registries, and electronic health records (EHRs) for epidemiological and HEOR studies.
• Claims Data and Administrative Database Cleaning – Refining payer claims, insurance datasets, and healthcare utilization data for accurate cost-effectiveness analysis.
• Patient-Reported Outcomes (PROs) Data Processing – Harmonizing PRO survey responses for statistical validation and regulatory submissions.
• Multimodal Data Integration – Structuring data from wearable devices, genomics studies, imaging databases, and social determinants of health (SDoH) datasets.

Why Choose Clievi for Data Cleaning and Formatting?

• Domain-Specific Expertise – We specialize in clinical, epidemiological, and healthcare data processing, ensuring compliance with regulatory and statistical standards.
• Automated and Manual Data Validation – Leveraging advanced machine learning algorithms and manual quality checks for error-free, structured datasets.
• Customizable Solutions – Tailored data cleaning and formatting workflows for systematic reviews, meta-analyses, clinical trials, and RWE studies.
• Comprehensive Documentation – Transparent, audit-ready data records with detailed metadata tracking.
• Secure and Confidential Processing – Compliance with HIPAA, GDPR, and other data privacy regulations to maintain data security and confidentiality.

Optimize Your Research with High-Quality Data Processing

At Clievi, we ensure that your datasets are clean, structured, and analysis-ready, enabling accurate, reproducible, and impactful research outcomes. Whether for clinical trials, epidemiological studies, or HEOR analyses, our data cleaning and formatting expertise guarantees data integrity and compliance with global standards.

Partner with Clievi for robust data management solutions and elevate the quality of your research today!