The traditional way to combine data from multiple data sources is when data extraction and analysis are performed independently at each centre based on separate protocols. This is usually followed by meta-analysis of the different estimates obtained (see Chapter 5.7).
In this option, data are extracted and analysed locally on the basis of a common protocol. Definitions of exposure, outcomes and covariates, analytical programmes and reporting formats are standardised according to a common protocol and the results of each analysis are shared in an aggregated format and pooled together through meta-analysis. This approach allows assessment of database/population characteristics and their impact on estimates but reduces variability of results determined by differences in design. Examples of research networks that use the common protocol approach are the PROTECT project (as described in Improving Consistency and Understanding of Discrepancies of Findings from Pharmacoepidemiological Studies: the IMI PROTECT Project. (Pharmacoepidemiol Drug Saf 2016;25(S1): 1–165) and the Canadian Network for Observational Drug Effect Studies (CNODES).
This approach requires very detailed common protocols and data specifications that reduce variability in interpretations by researchers.
Multi-centre, multi-database studies with common protocols: lessons learnt from the IMI PROTECT project (Pharmacoepidemiol Drug Saf 2016;25(S1):156-165) states that a priori pooling of data from several databases may disguise heterogeneity that may provide useful information on the safety issue under investigation. On the other hand, parallel analysis of databases allows exploring reasons for heterogeneity through extensive sensitivity analyses. This approach eventually increases consistency in findings from observational drug effect studies or reveal causes of differential drug effects.
For some studies, it has been possible to analyse centrally patient level data extracted based on a common protocol, such as in Selective serotonin reuptake inhibitors during pregnancy and risk of persistent pulmonary hypertension in the newborn: population based cohort study from the five Nordic Countries (BMJ 2012;344:d8012). If databases are very similar in structure and content as is the case for some Nordic registries, a CDM might not be required for data extraction. The central analysis allows removing an additional source of variability linked to the statistical programing and analysis.
Data can also be extracted from local databases using a study-specific, database-tailored extraction into a CDM and pre-processed locally. The resulting data can be transmitted to a central data warehouse as patient-level data or aggregated data for further analysis. Examples of research networks that used this approach by employing a study-specific CDM with transmission of anonymised patient-level data (allowing a detailed characterisation of each database) are EU-ADR (as explained in Combining electronic healthcare databases in Europe to allow for large-scale drug safety monitoring: the EU-ADR Project, Pharmacoepidemiol Drug Saf 2011;20(1):1-11), SOS, ARITMO, SAFEGUARD, GRIP and ADVANCE.
An approach to expedite the analysis of heterogeneity, called the component strategy, was initially developed in the EMIF project and could also be compatible with the generalised common data model (see Identifying Cases of Type 2 Diabetes in Heterogeneous Data Sources: Strategy from the EMIF Project. PLoS ONE. 2016;11(8):e0160648).
Two examples of research networks which use a generalised CDM are the Sentinel Initiative (as described in The U.S. Food and Drug Administration's Mini-Sentinel Program, Pharmacoepidemiol Drug Saf 2012;21(S1):1–303) and OHDSI. The main advantage of a general CDM is that it can be used for virtually any study involving the database. OhDSI is based on the Observational Medical Outcomes Partnership (OMOP) CDM which is used by many organisations and has been tested for its suitability for safety studies (see for example Validation of a common data model for active safety surveillance research. J Am Med Inform Assoc. 2012;19(1):54–60). OMOP also developed an open source repository for the analytical tools created within the project.
In A Comparative Assessment of Observational Medical Outcomes Partnership and Mini-Sentinel Common Data Models and Analytics: Implications for Active Drug Safety Surveillance (Drug Saf. 2015;38(8):749-65), it is suggested that slight conceptual differences between the Sentinel and the OMOP models do not significant impact on identifying known safety associations. Differences in risk estimations can be primarily attributed to the choices and implementation of the analytic approach.
|10. Specific topics|
|Annex 1.||Guidance on conducting systematic revies and meta-analyses of completed comparative pharmacoepidemiological studies of safety outcomes|