Print page Resize text Change font-size Change font-size Change font-size High contrast

Home > Standards & Guidances > Methodological Guide

ENCePP Guide on Methodological Standards in Pharmacoepidemiology


5.1.2. Assessment of outcomes


A case definition compatible with the observational database should be developed for each outcome of a study at the design stage. This description should include how events will be identified and classified as cases, whether cases will include prevalent as well as incident cases, exacerbations and second episodes (as differentiated from repeat codes) and all other inclusion or exclusion criteria. The reason for the data collection and the nature of the healthcare system that generated the data should also be described as they can impact on the quality of the available information and the presence of potential biases. Published case definitions of outcomes, such as those developed by the Brighton Collaboration in the context of vaccinations, are not necessarily compatible with the information available in a given observational data set. For example, information on the duration of symptoms may not be available, or additional codes may have been added to the data set following publication of the outcome definition.


Search criteria to identify outcomes should be defined and the list of codes should be provided. Generation of code lists requires expertise in both the coding system and the disease area. Researchers should also consult clinicians who are familiar with the coding practice within the studied field. Suggested methodologies are available for some coding systems (see Creating medical and drug code lists to identify cases in primary care databases. Pharmacoepidemiol Drug Saf 2009;18(8):704-7). Coding systems used in some commonly used databases are updated regularly so sustainability issues in prospective studies should be addressed at the protocol stage. Moreover, great care should be given when re-using a code list from another study as code lists depend on the study objective and methods. Public repository of codes as is available and researchers are also encouraged to make their own set of coding available.


In some circumstances, chart review or text entries in electronic format linked to coded entries can be useful for outcome identification. Such identification may involve an algorithm with use of multiple code lists (for example disease plus therapy codes) or an endpoint committee to adjudicate available information against a case definition. In some cases, initial plausibility checks or subsequent medical chart review will be necessary. When databases have prescription data only, drug exposure may be used as a proxy for an outcome, or linkage to different databases is required.


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