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Home > Standards & Guidances > Methodological Guide

ENCePP Guide on Methodological Standards in Pharmacoepidemiology


4.6.1. General considerations


The need to pool data across different databases in order to gain power and increase generalisability of the results is becoming increasingly necessary. In Europe, collaborations for multi-database studies have been strongly encouraged over the last years by the drug safety research funded by the European Commission (EC) and public-private partnerships such as the Innovative Medicines Initiative (IMI). The funding resulted in the conduct of groundwork necessary to overcome the hurdles of data sharing across countries. A growing number of studies use data from networks of databases, often from different countries.


In the US, the HMO Research Network (HMORN), the OHDSI and the Sentinel initiative are examples of consortia involving health maintenance organisations that have formal, recognised research capabilities. Networking implies collaboration between investigators in sharing expertise and resources. The ENCePP Database of Research Resources may facilitate such networking by providing an inventory of research centres and data sources that can collaborate on specific pharmacoepidemiology and pharmacovigilance studies in Europe. It allows the identification of centres and data sets by country, type of research and other relevant fields.


From a methodological point of view, research networks have many advantages:

  • The potential for pooling data or results maximises the amount of information gathered for a specific issue addressed in different databases.
  • Research networks increase the size of study populations and shorten the time needed for obtaining the desired sample size. Hence, they can facilitate research on rare events and speed-up investigation of drug safety issues.
  • The heterogeneity of treatment options across countries allows studying the effect of individual drugs.
  • Research networks may provide additional knowledge on whether a drug safety issue exists in several countries and thereby reveal causes of differential drug effects, on the generalisability of results, on the consistency of information and on the impact of biases on estimates.
  • Involvement of experts from various countries addressing case definitions, terminologies, coding in databases and research practices provides opportunities to increase consistency of results of observational studies.
  • Sharing of data sources facilitates harmonisation of data elaboration and transparency in analyses and benchmarking of data management.

Different models have been applied for combining data or results from multiple databases. A common characteristic of all models is the fact that data partners maintain physical and operational control over electronic data in their existing environment. Differences however exist on whether a common protocol or a common data model is applied across all databases to extract, analyse and combine the data. A common data model (CDM) approach provides a similar representation of the database that allows standardisation of administrative and clinical information and facilitates a combined analysis across several databases. The CDM can be systematically applied on all data of a database (generalised CDM) or on the subset of data needed for a specific study (study-specific CDM).


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