Quality in research ultimately impacts on regulatory practice, medicines development and public health. Quality is a measure of excellence and quality management includes all the activities that organisations use to direct, control and coordinate quality (International Standards Organization, ISO 9000). Quality management principles as described in ISO Quality management principles are also applicable to pharmacoepidemiological research. 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 both in practice.
Quality management consists in four main activities: quality planning, quality assurance, quality control and quality improvement. Quality planning is defined as a set of activities whose purpose is to define quality system policies, objectives, and requirements, and to explain how these will be applied and achieved. Quality assurance defines the standards to be followed in order to meet the quality requirements for a product or service, whereas quality control ensures that these defined standards are followed at every step. Quality improvement refers to enhancing an organisation's ability to meet quality requirements.
Quality control should be designed as a study and involve identifying the study’s objective, determining the relevant data to collect, choosing appropriate instruments to collect the data, analysing the data, recommending appropriate actions, implementing them, and evaluating the implementation to be used effectively in order to act strategically.
Rules, procedures, roles and responsibilities of quality assurance and quality control for clinical trials and biomedical research are well defined and described in many documents, such as Chapter 11 of the book Principles of Good Clinical Practice (M.J. McGraw, A.N. George, S.P. Shearn, eds., Pharmaceutical Press, London, 2010), the ICH Guideline for Good Clinical Practice E6(R1) and E6(R2), the European Forum for Good Clinical Practice (EFCGP) Guidelines, the Imperial College Academic Health Science Centre (AHSC)’s Quality Control and Quality Assurance SOP, the article Quality by Design in Clinical Trials: A Collaborative Pilot With FDA (Therapeutic Innovation & Regulatory Science 2013; 47;161-6), or the article Guidelines for Quality Assurance in Multicenter Trials: A Position Paper (Control Clin Trials 1998;19(5);477-
For post-authorisation safety studies, the resources are: Commission Implementing Regulation (EU) No 520/2012, FDA’s Best Practices for Conducting and Reporting Pharmacoepidemiologic Safety Studies Using Electronic Health Care Data Sets or ISPE GPP and the Good Practice in Secondary Data Analysis.
The article Quality Assurance and Quality Control in Longitudinal Studies (Epidemiol Rev 1998, 20(1);71-80) provides a comprehensive overview of components of QA and QC in multi-centre cohort studies with primary data collection. Such studies typically involve collection of an extensive amount of data for processing over an extended period of time and at several centres, with quality depending on a variety of factors relating to study personnel and equipment. Consequently, the QC process in such studies should be considered an integral part of the design of the study and a condition for the validity of its results. Quality assurance in non-interventional studies (Ger Med Sci 2009;7:Doc 29: 1-14) proposes measures of quality assurance that can be applied at different stages of non-interventional studies without compromising the character of non-intervention. Chapter 11 ‘Data Collection and Quality Assurance’ of the AHRQ Registries for Evaluating Patient Outcomes: A User's Guide, 3rd Edition, reviews key areas of data collection, cleaning, storing, and quality assurance for registries, with practical examples.
The article The hope, hype and reality of Big Data for pharmacovigilance (Ther Adv Drug Saf 2018:9(1):5-11) deals with advancements in pharmacovigilance in last decades and the relevance of data collection and analysis regarding patient safety.
The following articles are practical examples of quality aspects implementation in pharmacovigilance and pharmacoepidemiological as well as other biomedical studies:
Data quality management in pharmacovigilance (Drug Saf 2004;27(12):857-70) focusses on the intial three steps of data processing cycle (collection and data entry; storage and maintenance; selection, retrieval and manipulation), the different quality dimensions associated with these steps together with examples relevant to pharmacovigilance data.
Quality assessment of structure and language elements of written responses given by seven Scandinavian drug information centres (Eur J Clin Pharmacol 2017: 73(5):623-631) deals with the identification of structure and language elements affecting the quality of responses from Scandinavian drug information centres that have been evaluated by internal and external, medical and language experts.