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Home > Standards & Guidances > Methodological Guide

ENCePP Guide on Methodological Standards in Pharmacoepidemiology

 

 

9.2.1.4. Hypothesis testing studies

Traditional study designs such as cohort and case-control studies may be difficult to implement for vaccines where studies involve populations with high vaccine coverage rates, an appropriate unvaccinated group is lacking or adequate information on covariates at the individual level is not available. Frequent sources of confounding to be considered are socioeconomic status, underlying health status and other factors influencing the probability of being vaccinated. Control without separate controls: evaluation of vaccine safety using case-only methods (Vaccine 2004; 22(15-16):2064-70) describes and illustrates epidemiological methods that are useful in such situations as they only involve cases:

  • The case-coverage method uses exposure information on cases and population data on vaccination coverage to serve as control. It requires reliable and detailed vaccine coverage data corresponding to the population from which cases are drawn and permitting control of confounding by stratified analysis. During vaccine introduction, it is also particularly important to address selection bias introduced by awareness of possible occurrence of an outcome. An example of a study using a case-coverage method is Risk of narcolepsy in children and young people receiving AS03 adjuvanted pandemic A/H1N1 2009 influenza vaccine: retrospective analysis (BMJ 2013; 346:f794).

  • The case-crossover method requires the strong assumption that the underlying probability of vaccination should be the same in all defined time intervals, but this is unlikely to hold for paediatric vaccines administered according to strict schedules or for seasonally administered vaccines.

  • The self-controlled case series (SCCS) design is described in section 4.2.3 of the Guide.

The study Control without separate controls: evaluation of vaccine safety using case-only methods (Vaccine 2004; 22(15-16):2064-70) concludes that properly designed and analysed epidemiological studies using only cases may provide, especially the SCCS method, stronger evidence than large cohort studies as they control completely for fixed individual-level confounders (such as genetics and social deprivation) and typically have similar, sometimes better, power. Three factors are however critical in making optimal use of such methods: access to good data on cases, computerised vaccination records with the ability to link them to cases and availability of appropriate analysis techniques.

 

Several studies on vaccines have compared traditional and case-only study designs:

  • Epidemiological designs for vaccine safety assessment: methods and pitfalls (Biologicals 2012;40(5):389-92) used three study designs (cohort, case-control and self-controlled case series) to illustrate the issues that may arise when designing an epidemiological study, such as understanding the vaccine safety question, case definition and finding, limitations of data sources, uncontrolled confounding, and pitfalls that apply to the individual designs.

  • Comparison of epidemiologic methods for active surveillance of vaccine safety (Vaccine 2008; 26(26):3341-3345) performed a simulation study to compare four designs (matched-cohort, vaccinated-only (risk interval) cohort, case-control and self-controlled case series) in the context of vaccine safety surveillance. The cohort study design allowed for the most rapid signal detection, the least false-positive error and highest statistical power in performing sequential analysis. The authors highlight, however, that the chief limitation of this simulation is the exclusion of confounding effects and the lack of chart review, which is a time and resource intensive requirement.

  • Another simulation study (Four different study designs to evaluate vaccine safety were equally validated with contrasting limitations. J Clin Epidemiol 2006; 59(8):808-818) compared four study designs (cohort, case-control, risk-interval and SCCS) with the conclusion that all the methods were valid designs, with contrasting strengths and weaknesses. The SCCS method, in particular, proved to be an efficient and valid alternative to the cohort method.

  • Hepatitis B vaccination and first central nervous system demyelinating events: Reanalysis of a case-control study using the self-controlled case series method. Vaccine 2007;25(31):5938-43) describes how the SCCS found similar results as the case-control study but with greater precision as it used cases without matched controls excluded from the case-control analysis. This is at the cost of the assumption that exposures are independent of earlier events. The authors recommended that, if case-control studies of vaccination and adverse events are undertaken, parallel case-series analyses should also be conducted, where appropriate.

In situations where primary data collection is needed (e.g. a pandemic), the SCCS may not be timely since follow-up time needs to be accrued. In such instances, the Self-controlled Risk Interval (SCRI) method can be used to shorten the observation time (see The risk of Guillain-Barre Syndrome associated with influenza A (H1N1) 2009 monovalent vaccine and 2009-2010 seasonal influenza vaccines: Results from self-controlled analyses. Pharmacoepidemiol Drug Safety 2012;21(5):546-52), historical background rates can be used for an O/E analysis (see Near real-time surveillance for influenza vaccine safety: proof-of-concept in the Vaccine Safety Datalink Project. Am J Epidemiol 2010;171(2):177-88) or a case-control study can be performed, as used in Guillain-Barré syndrome and adjuvanted pandemic influenza A (H1N1) 2009 vaccine: multinational case-control study in Europe. BMJ 2011;343:d3908).

Ecological analyses should not be considered hypothesis testing studies. See section 4.4. of this Guide.

 

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

4.2.1.1. Assessment of exposure

4.2.1.2. Assessment of outcomes

4.2.1.3. Assessment of covariates

4.2.1.4. Validation

4.2.2. Bias and confounding

4.2.2.1. Choice of exposure risk windows

4.2.2.2. Time-related bias

4.2.2.2.1. Immortal time bias

4.2.2.2.2. Other forms of time-related bias

4.2.2.3. Confounding by indication

4.2.2.4. Protopathic bias

4.2.2.5. Surveillance bias

4.2.2.6. Unmeasured confounding

4.2.3. Methods to handle bias and confounding

4.2.3.1. New-user designs

4.2.3.2. Case-only designs

4.2.3.3. Disease risk scores

4.2.3.4. Propensity scores

4.2.3.5. Instrumental variables

4.2.3.6. Prior event rate ratios

4.2.3.7. 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

9.1.3.1. Randomised clinical trials vs. observational studies

9.1.3.2. Use of electronic healthcare databases

9.1.3.3. Bias and confounding in observational CER

9.2. Vaccine safety and effectiveness

9.2.1. Vaccine safety

9.2.1.1. General aspects

9.2.1.2. Signal detection

9.2.1.3. Signal refinement

9.2.1.4. Hypothesis testing studies

9.2.1.5. Meta-analyses

9.2.1.6. Studies on vaccine safety in special populations

9.2.2. Vaccine effectiveness

9.2.2.1. Definitions

9.2.2.2. Traditional cohort and case-control studies

9.2.2.3. Screening method

9.2.2.4. Indirect cohort (Broome) method

9.2.2.5. Density case-control design

9.2.2.6. Test negative design

9.2.2.7. Case coverage design

9.2.2.8. Impact assessment

9.2.2.9. 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