Abbreviations and Definitions

Abbreviations

Definitions

Bias-Minimisation Techniques

Methods employed to control for potential confounders and biases, reducing systematic errors that could distort study results.

Conceptual Definitions 

High-level, theoretical descriptions of variables, describing what they represent or signify within the context of the research.

Data Characterisation

The evaluation and documentation of the properties, quality, and limitations of curated data. Data characterisation, or metadata, aims to identify the strengths and weaknesses of original data sources, covering aspects such as the purpose of data collection, site types, data types (e.g. administrative claims, clinical notes), and patient demographics. This information is critical for evaluating data suitability for study questions, supporting study protocol development, and contributing to findings' relevance and reliability assessments.

Data Completeness

The extent to which all necessary data points are collected and available for analysis. This can include various considerations for key study variables, including the study population (inclusion/exclusion criteria), exposures, outcomes, and covariates. 

Data Curation

The process of cleaning, standardising, and organising data to meet quality standards necessary for analysis. This includes data cleaning, transformation, and harmonisation to ensure it meets quality standards for analysis. Curation may also involve the annotation and integration of data from diverse sources to make them usable for specific research or decision-making processes. These components play a central part in establishing the provenance of the RWD and ensuring that it has not been mismanaged.

Data Extraction

The process of retrieving relevant information from collected data sources and preparing it for analysis. 

Data Linkage Methodology

Predefined procedures for connecting datasets, including matching criteria and privacy protections, to ensure valid integration of multiple data sources.

Data Provenance

Documentation of the origin, processing history, and transformations applied to data, establishing its traceability and reliability throughout the research lifecycle.

Data Privacy and Confidentiality

Protection of individuals’ personal and sensitive information within datasets, ensuring compliance with legal regulations such as GDPR and maintaining participant trust.

Data Quality Assurance

Systematic measures to ensure accuracy, completeness, consistency, and reliability of data throughout the research process, including validation, auditing, and quality control procedures.

Data Quality Checks

Procedures used to identify and address errors, inconsistencies, missing data, or implausible values within datasets, ensuring data integrity and validity.

Data Sharing & Transparency

Public or controlled access to datasets and protocols, allowing others to verify and reuse findings, thus enhancing credibility and reproducibility.

Data Security

Protocols and technological measures in place to safeguard data against unauthorised access, loss, or breaches.

General Data Protection Regulation (GDPR)

A European Union data protection law that took effect in 2018. Its purpose is to give individuals more control over their personal data by setting strict standards for how organisations collect, process, and protect it. It applies to any organisation that handles the data of EU residents, regardless of where the company is based.  

Good Clinical Practice (GCP) 

GCP is an international ethical and scientific quality standard for clinical trials involving human subjects. It ensures that the rights, safety, and well-being of trial participants are protected and that the data from clinical trials is credible and reliable. GCP provides a framework for all stages of a clinical trial, including its design, conduct, recording, and reporting. 

Informed Consent

A process through which study participants are adequately informed of the research purpose, procedures, risks, and rights before agreeing to participate, ensuring ethical standards.

Operational Definitions                                                                                         

Precise, measurable descriptions (of variables) to ensure consistency and reproducibility across different researchers and sites

Reproducibility

The ability for independent researchers to replicate study results using the same data, methods, and procedures, confirming the reliability of findings.

Research Transparency

The open and complete disclosure of all aspects of the research process, from initial conception and protocol design to data collection, analysis, and dissemination, enabling verification and critique.

Research Lifecycle

The entire sequence of stages in a research project where transparency practices should be applied to ensure trust, reproducibility, and credibility, including planning, conducting, analysing, documenting, and dissemination.

RWD (Real World Data)

Information pertaining to health status and health care delivery that is collected routinely from sources other than traditional clinical trials. RWD includes data generated in real-world settings, such as electronic health records, claims databases, patient registries, and wearable device data.

RWE (Real World Evidence)

Evidence derived from the analysis of RWD regarding the usage, effectiveness, and potential benefits or risks of an intervention. RWE provides insights to support regulatory decisions, health policy, and clinical practice.

Statistical Analysis Plan (SAP)

A comprehensive plan authored prior to data analysis that specifies the statistical methods, models, and procedures, ensuring transparency and reproducibility of the analytic process.

Target Population

The specific group of individuals or entities the research is designed to characterise or generalise its findings to, based on shared characteristics and criteria.