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Home > Standards & Guidances > Methodological Guide

ENCePP Guide on Methodological Standards in Pharmacoepidemiology New-user designs

The practice of including many prevalent users, i.e. patients taking a therapy for some time before study follow-up began, in observational studies can cause two types of bias. Firstly, prevalent users are “survivors” of the early period of pharmacotherapy, which can introduce substantial selection bias if risk varies with time. Secondly, covariates for drug users at study entry are often plausibly affected by the drug itself. New user designs help avoid the mistake of adjusting for factors on the causal pathway which may introduce bias towards the null. Evaluating medication effects outside of clinical trials: new-user designs (Am J Epidemiol 2003;158 (9):915–20) reviews designs which avoid these biases by restricting the analysis to persons under observation at the start of the current course of treatment. In addition to defining new-user designs, the article explains how they can be implemented as case-control studies and describes the logistical and sample size limitations involved.


Individual Chapters:


1. General aspects of study protocol

2. Research question

3. Approaches to data collection

3.1. Primary data collection

3.2. Secondary use of data

3.3. Research networks

3.4. Spontaneous report database

3.5. Using data from social media and electronic devices as a data source

3.5.1. General considerations

4. Study design and methods

4.1. General considerations

4.2. Challenges and lessons learned

4.2.1. Definition and validation of drug exposure, outcomes and covariates Assessment of exposure Assessment of outcomes Assessment of covariates Validation

4.2.2. Bias and confounding Choice of exposure risk windows Time-related bias Immortal time bias Other forms of time-related bias Confounding by indication Protopathic bias Surveillance bias Unmeasured confounding

4.2.3. Methods to handle bias and confounding New-user designs Case-only designs Disease risk scores Propensity scores Instrumental variables Prior event rate ratios Handling time-dependent confounding in the analysis

4.2.4. Effect modification

4.3. Ecological analyses and case-population studies

4.4. Hybrid studies

4.4.1. Pragmatic trials

4.4.2. Large simple trials

4.4.3. Randomised database studies

4.5. Systematic review and meta-analysis

4.6. Signal detection methodology and application

5. The statistical analysis plan

5.1. General considerations

5.2. Statistical plan

5.3. Handling of missing data

6. Quality management

7. Communication

7.1. Principles of communication

7.2. Guidelines on communication of studies

8. Legal context

8.1. Ethical conduct, patient and data protection

8.2. Pharmacovigilance legislation

8.3. Reporting of adverse events/reactions

9. Specific topics

9.1. Comparative effectiveness research

9.1.1. Introduction

9.1.2. General aspects

9.1.3. Prominent issues in CER Randomised clinical trials vs. observational studies Use of electronic healthcare databases Bias and confounding in observational CER

9.2. Vaccine safety and effectiveness

9.2.1. Vaccine safety General aspects Signal detection Signal refinement Hypothesis testing studies Meta-analyses Studies on vaccine safety in special populations

9.2.2. Vaccine effectiveness Definitions Traditional cohort and case-control studies Screening method Indirect cohort (Broome) method Density case-control design Test negative design Case coverage design Impact assessment Methods to study waning immunity

9.3. Design and analysis of pharmacogenetic studies

9.3.1. Introduction

9.3.2. Identification of genetic variants

9.3.3. Study designs

9.3.4. Data collection

9.3.5. Data analysis

9.3.6. Reporting

9.3.7. Clinical practice guidelines

9.3.8. Resources

Annex 1. Guidance on conducting systematic revies and meta-analyses of completed comparative pharmacoepidemiological studies of safety outcomes