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Home > Standards & Guidances > Methodological Guide

ENCePP Guide on Methodological Standards in Pharmacoepidemiology


6.1. General considerations

There is a considerable body of literature explaining statistical methods for observational studies but very little addressing the statistical analysis plan. A clear guide to general principles and the need for a plan is given in Design of Observational Studies (P.R. Rosenbaum, Springer Series in Statistics, 2010. Chapter18), which also gives useful advice on how to test complex hypotheses in a way that controls the chances of drawing incorrect conclusions.


Planning analyses for randomised clinical trials is covered in a number of publications. These often give checklists of the component parts of an analysis plan and much of this applies equally to non-randomised design. A good reference in this respect is the International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH). ICH E9 ‘Statistical Principles for Clinical Trials’. While specific guidance on the statistical analysis plan for epidemiological studies is sparse, the following principles will apply to most of the studies.

  • A study is generally designed with the objective of addressing a set of research questions. However, the initial product of a study is a set of numerical and categorical observations that do not usually provide a direct answer to the questions that the study is designed to address. The statistical analysis plan details the mathematical transformations that will be performed on the observed data in the study and the patterns of results that will be interpreted as supporting alternative answers to the questions. It will also explain the rationale behind this decision making process and the way that this rationale has influenced the study design. An important part of the statistical analysis plan will explain how problems in the data will be handled in such calculations, for example missing or partial data.
  • The statistical analysis plan should be sufficiently detailed so that it can be followed in the same way by any competent analyst. Thus it should provide clear and complete templates for each analysis.


  • Pre-specification of statistical and epidemiological analyses can be challenging for data that are not collected specifically to answer the study questions. This is often the case in observational studies. However, thoughtful specification of the way missing values will be handled or the use of a small part of the data as a pilot set to guide analysis can be useful techniques to overcome such problems. A feature common to most studies is that some not pre-specified analyses will be performed in response to observations in the data to help interpretation of results. It is important to distinguish between such data-driven analyses and the pre-specified findings. Post-hoc modifications to the analysis strategy should be noted and explained. The statistical analysis plan provides a confirmation of this process.


A particular concern in retrospective studies is that decisions about the analysis should be made blinded to any knowledge of the results. This should be a consideration in the study design, particularly when feasibility studies are to be performed to inform the design phase. Feasibility studies should be independent of the main study results.



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