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Home > Standards & Guidances > Methodological Guide

ENCePP Guide on Methodological Standards in Pharmacoepidemiology

 

4.4. Spontaneous report database

 

Spontaneous reports of adverse drug effects remain a cornerstone of pharmacovigilance and are collected from a variety of sources, including healthcare providers, national authorities, pharmaceutical companies, medical literature and more recently directly from patients. EudraVigilance is the European Union data processing network and management system for reporting and evaluation of suspected adverse drug reactions (ADRs). The Global Individual Case Safety Reports Database System (VigiBase) pools reports of suspected ADRs from the members of the WHO programme for international drug monitoring. These systems deal with the electronic exchange of Individual Case Safety Reports (ICSRs), the early detection of possible safety signals and the continuous monitoring and evaluation of potential safety issues in relation to reported ADRs. The report Characterization of databases (DB) used for signal detection (SD) of the PROTECT project shows the heterogeneity of spontaneous databases and the lack of comparability of SD methods employed. This heterogeneity is an important consideration when assessing the performance of SD algorithms.

 

The strength of spontaneous reporting systems is that they cover all types of legal drugs used in any setting. In addition to this, the reporting systems are built to obtain information specifically on potential adverse drug reactions and the data collection concentrates on variables relevant to this objective and directs reporters towards careful coding and communication of all aspects of an ADR. The increase in systematic collection of ICSRs in large electronic databases has allowed the application of data mining and statistical techniques for the detection of safety signals. There are known limitations of spontaneous ADR reporting systems, which include limitations embedded in the concept of voluntary reporting, whereby known or unknown external factors may influence the reporting rate and data quality. ICSRs may be limited in their utility by a lack of data for an accurate quantification of the frequency of events or the identification of possible risk factors for their occurrence. For these reasons, the concept is now well accepted that any signal from spontaneous reports needs to be verified clinically before further communication.

 

One challenge in spontaneous report databases is report duplication. Duplicates are separate and unlinked records that refer to one and the same case of a suspected ADR and may mislead clinical assessment or distort statistical screening. They are generally detected by individual case review of all reports or by computerised duplicate detection algorithms. In Performance of probabilistic method to detect duplicate individual case safety reports (Drug Saf 2014;37(4):249-58) a probabilistic method highlighted duplicates that had been missed by a rule-based method and also improved the accuracy of manual review. In the study, however, a demonstration of the performance of de-duplication methods to improve signal detection is lacking.

 

Validation of statistical signal detection procedures in EudraVigilance post-authorisation data: a retrospective evaluation of the potential for earlier signalling (Drug Saf 2010;33: 475 – 87) has shown that the statistical methods applied in EudraVigilance can provide significantly early warning in a large proportion of drug safety problems. Nonetheless, this approach should supplement, rather than replace, other pharmacovigilance methods.

 

Chapters IV and V of the Report of the CIOMS Working Group VIII ‘Practical aspects of Signal detection in Pharmacovigilance’ present sources and limitations of spontaneously-reported drug-safety information and databases that support signal detection. Appendix 3 of the report provides a list of international and national spontaneous reporting system database.

 

 

 

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