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Home > Standards & Guidances > Methodological Guide

ENCePP Guide on Methodological Standards in Pharmacoepidemiology


7. Quality management

Biomedical research is characterised by a tremendous expansion of knowledge in recent years and quality in biomedical research ultimately impacts on either regulatory practice or medicines development or public health.


Quality is a measure of excellence or a state of being free from defects, deficiencies and significant variations and the quality management includes all the activities that organisations use to direct, control and coordinate quality (International Standards Organization, ISO 9000). The seven quality management principles (QMPs), i.e., customer focus, leadership, engagement of people, process approach, improvement, evidence-based decision-making and relationship management have been introduced in ISO Quality management principles and are applicable to pharmacoepidemiological research as well.


A summary of the key principles and methods for quality improvement and measurement and patient safety are presented in Medical Quality Management: Theory and Practice (American College of Medical Quality, Prathibha Varkey. Jones and Bartlett Publishers International, 2010). Functioning, well documented, and transparent quality management systems will benefit those involved in data collection, management and output production, but also has an impact of public health.


The terms ‘quality management’, ‘quality improvement’, and ‘performance improvement’ are used interchangeably in the healthcare literature. Quality management implies and consists in the following 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 policies will be applied and achieved, and how these requirements will be met. Quality assurance (QA) defines the standards to be followed in order to meet the quality requirements for a product or service, whereas quality control (QC) ensures that these defined standards are followed at every step.  Quantitative Data Analysis for Quality Control in Strategic Management (Encyclopedia of Strategic Leadership and Management, V.C.X. Wang, Editor, 2017:1461-1470), describes the relevant quantitative data analytics to inform quality control: identifying the study’s objective, determining the relevant data to collect, choosing appropriate instruments to collect those data, analysing that data, recommending appropriate actions, implementing them, and evaluating the implementation to be used effectively in order to act strategically.


Quality improvement refers to anything that enhances an organisation's ability to meet quality requirements.

Managing the quality would not be possible without risk management strategies aimed at identifying, addressing, prioritising, and eliminating potential sources of failure to achieve objectives. ISPE GPP assists researchers in adhering to good pharmacoepidemiologic research principles, including the use of pharmacoepidemiologic studies for risk management activities and comparative effectiveness research (CER).


Rules, procedures, roles and responsibilities of QA and QC 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), 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);477-93).


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 following articles are practical examples of quality aspects implementation in pharmacovigilance and pharmacoepidemiological as well as other biomedical studies:

  • Interviewer Variability – Quality Aspects in a Case–Control Study (Eur J Epidemiol 2006;21(4);267-77) describes the procedures used to reduce interviewer variability, including procedures of quality assurance (i.e. education and training of interviewers and data validity checks) and quality control (i.e. a classification test, annual test interviews, expert case validation and database validation).





Data quality management in pharmacovigilance (Drug Saf 2004;27(12):857-70) focusses on three first 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.



Individual Chapters:


1. Introduction

2. Formulating the research question

3. Development of the study protocol

4. Approaches to data collection

4.1. Primary data collection

4.1.1. Surveys

4.1.2. Randomised clinical trials

4.2. Secondary data collection

4.3. Patient registries

4.3.1. Definition

4.3.2. Conceptual differences between a registry and a study

4.3.3. Methodological guidance

4.3.4. Registries which capture special populations

4.3.5. Disease registries in regulatory practice and health technology assessment

4.4. Spontaneous report database

4.5. Social media and electronic devices

4.6. Research networks

4.6.1. General considerations

4.6.2. Models of studies using multiple data sources

4.6.3. Challenges of different models

5. Study design and methods

5.1. Definition and validation of drug exposure, outcomes and covariates

5.1.1. Assessment of exposure

5.1.2. Assessment of outcomes

5.1.3. Assessment of covariates

5.1.4. Validation

5.2. Bias and confounding

5.2.1. Selection bias

5.2.2. Information bias

5.2.3. Confounding

5.3. Methods to handle bias and confounding

5.3.1. New-user designs

5.3.2. Case-only designs

5.3.3. Disease risk scores

5.3.4. Propensity scores

5.3.5. Instrumental variables

5.3.6. Prior event rate ratios

5.3.7. Handling time-dependent confounding in the analysis

5.4. Effect measure modification and interaction

5.5. Ecological analyses and case-population studies

5.6. Pragmatic trials and large simple trials

5.6.1. Pragmatic trials

5.6.2. Large simple trials

5.6.3. Randomised database studies

5.7. Systematic reviews and meta-analysis

5.8. Signal detection methodology and application

6. The statistical analysis plan

6.1. General considerations

6.2. Statistical analysis plan structure

6.3. Handling of missing data

7. Quality management

8. Dissemination and reporting

8.1. Principles of communication

8.2. Communication of study results

9. Data protection and ethical aspects

9.1. Patient and data protection

9.2. Scientific integrity and ethical conduct

10. Specific topics

10.1. Comparative effectiveness research

10.1.1. Introduction

10.1.2. General aspects

10.1.3. Prominent issues in CER

10.2. Vaccine safety and effectiveness

10.2.1. Vaccine safety

10.2.2. Vaccine effectiveness

10.3. Design and analysis of pharmacogenetic studies

10.3.1. Introduction

10.3.2. Identification of generic variants

10.3.3. Study designs

10.3.4. Data collection

10.3.5. Data analysis

10.3.6. Reporting

10.3.7. Clinical practice guidelines

10.3.8. Resources

Annex 1. Guidance on conducting systematic revies and meta-analyses of completed comparative pharmacoepidemiological studies of safety outcomes