Quality in research is a measure of excellence that impacts medicines development and public health. What is quality management system (QMS)? (American Society for Quality, 2022) defines a QSM as a formalised system that documents processes, procedures, and responsibilities for achieving quality policies and objectives. Quality management principles as described in ISO Quality management principles are applicable to pharmacoepidemiological research. ISO 9000:2015 describes the fundamental concepts and principles of quality management which are universally applicable to organisations and specifies the terms and definitions that apply to quality management and quality management system standards. The book Total Quality Management-Key Concepts and Case Studies (D.R. Kiran, BSP Books, Elsevier, 2016) deals with the management principles and practices that govern the quality function and presents all the aspects of quality control and management in practice.
The Commission Implementing Regulation (EU) No 520/2012 and the Good pharmacovigilance practices (GVP) Module I provide a framework for the quality management of pharmacovigilance and safety studies of authorised medicinal products.
Measurable quality requirements can be achieved by:
Quality planning: establishing structures (including validated computerised systems) and planning integrated and consistent processes;
Quality assurance and control: monitoring and evaluating how effectively the structures and processes have been established and how effectively the processes are being carried out;
Quality improvement: correcting and improving the structures and processes where necessary.
Pharmacoepidemiological research may be based on primary data collection or secondary use of data collected for other purposes (see Chapter 7). Primary data collection is a controlled process to which all steps of quality management should apply. Secondary use of data requires quality control addressing a posteriori data quality irrespective of its use (also part of the concept of reliability mentioned in the next section, e.g., detection of missing information, errors made during a transfer or conversion, outliers, duplicates, implausible values), as well as data quality in the context of its use for a specific study (also named relevance). The data quality frameworks presented in the next section provide dimensions and methods for quality control of the data re-used for regulatory decision-making or other purposes.
Pharmacoepidemiological research is also becoming more complex and may use a very large amount of data. In such situation, managing quality implies a risk-based approach. Risk-based quality management is incorporated as Good Clinical Practice expectation in ICH E8 (R1) and addressed in the European Commission’s Risk proportionate approaches in clinical trials, EMA’s Reflection paper on risk-based quality management in clinical trials and the GVP Module III on Pharmacovigilance inspections.
The considerations and recommendations of Chapter 4.3 on the definition and validation of drug exposure, outcomes and covariates are essential aspects that need to be addressed for quality management in pharmacoepidemiology.