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


5.1.3. Assessment of covariates


In pharmacoepidemiology studies, covariates are often used for selecting and matching study subjects, comparing characteristics of the cohorts, developing propensity scores, creating stratification variables, evaluating effect modifiers and adjusting for confounders. Reliable assessment of covariates is therefore essential for the validity of results. Patient characteristics and other key covariates that could be confounding variables need to be evaluated using all available data. A given database may or may not be suitable for studying a research question depending on the availability of these covariates.

Some patient characteristics and covariates vary with time and accurate assessment is time dependent. The timing of assessment of the covariates is an important factor for the correct classification of the subjects and should be clearly specified in the protocol. Assessment of covariates can be done using different periods of time (look-back periods or run-in periods).


Fixed look-back periods (for example 6 months or 1 year) are sometimes used when there are changes in coding methods or in practices or when is not feasible to use the entire medical history of a patient. Estimation using all available covariates information versus a fixed look-back window for dichotomous covariates (Pharmacoepidemiol Drug Saf. 2013; 22(5):542-50) establishes that defining covariates based on all available historical data, rather than on data observed over a commonly shared fixed historical window will result in estimates with less bias. However, this approach may not be applicable when data from paediatric and adult periods are combined because covariates may significantly differ between paediatric and adult populations (e.g., height and weight).




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