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Home > Standards & Guidances > Methodological Guide

ENCePP Guide on Methodological Standards in Pharmacoepidemiology

 

2. Formulating the research question

Generating evidence involves three steps: asking the right research questions, finding or collecting the fit-for-purpose data, and conducting the appropriate analyses. The first step in any research is to formulate the research question clearly and accurately. The research question should specify the problem or gap in knowledge to be addressed and how it will be investigated in the proposed research. The research question should formulate the ‘why’ (main justification for starting the research), the ‘what’ (exposure and endpoints), the ‘who’ (target population), the ‘how’ (main study design) and the ‘when’ (time period of the study) of the research in a way that it helps understanding how it has guided the choice of study objectives and methods described in the protocol, such as the choice of the study population, data source(s), exposure or endpoints. It should make it clear whether a hypothesis will be tested and in this case whether the hypothesis is pre-specified or data driven. The research question should also state who will be the primary end-users of the study results (e.g. regulators or public health authorities, health technology assessment organisations, payers, pharmaceutical company, research community).

 

Previous findings are useful for the methodological planning of the current study and may support the background, research question, hypotheses and design of the proposed study. They may also serve to determine the expected effect size and, if available in the target population, to characterise risk factors for the event, to identify relevant outcomes and measures and to assess the feasibility of the proposed study. A critical and thorough review of the literature forms the basis for the theoretical framework of the research question and should usually be included in the background section of a protocol. Such a review aims to evaluate current evidence around the question at hand and identify gaps in knowledge that a study is intended to fill. How to formulate research recommendations (BMJ 2006;333:804-6) proposes the EPICOT format with 5 core elements (Evidence, Population, Intervention, Comparison, Outcome and Time stamp) for research recommendations on the effects of treatments following systematic reviews. This format was adopted by the European network of Health Technology Assessment (EUnetHTA) in its Position paper on how to formulate research recommendations. Chapter 5.7. and Appendix 1 present methods for reviewing and synthesising findings from the literature through the means of systematic review and meta-analysis.

 

When the study data source is not well characterized or known for the purpose of a particular study, a feasibility study should be considered. The aim of a feasibility study is not to answer the research question directly but to determine whether the data source could answer it within the expected timelines and with the required statistical power for the proposed study design. Feasibility studies can provide information on the number of people with a specific exposure or outcome, the availability of covariates and the follow up period. A feasibility study can also provide insights into the potential difficulties which may be encountered in the conduct of the study or which may introduce bias. The ISPE Good pharmacoepidemiology practice (GPP) explains how a data collection method or data source can answer a research question with justifications coming from the feasibility study when relevant.

 

 

 

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