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Home > Standards & Guidances > Methodological Guide

ENCePP Guide on Methodological Standards in Pharmacoepidemiology


6.2. Statistical analysis plan structure

The statistical and epidemiological analysis plan is usually structured to reflect the protocol and will address, where relevant, the following points:


  1. A description of the study data sources, linkage methods, and study design including intended study population, inclusion and exclusion criteria and study period with discussion of strengths and weaknesses.


  1. Formal definitions of exposure including transformations to determine duration and quantity of exposure.


  1. Definition of follow-up and censoring if applicable.


  1. Formal definitions of any outcomes, for example ‘fatal myocardial infarction’ that might be defined as ‘death within 30 days of a myocardial infarction’. Outcome variables based on historical data may involve complex transformations to approximate clinical variables not explicitly measured in the dataset used. These transformations should be discriminated from those made to improve the fit of a statistical model. In either case the rationale should be given. In the latter case this will include which tests of fit will be used and under what conditions a transformation will be used.


  1. Formal definitions for other variables – e.g. thresholds for abnormal levels of blood parameters. When values of variables for a subject vary with time, care should be given to explaining how the values will be determined at each time point and recorded in the dataset for use in a statistical model.


  1. The effect measures and statistical methods used to address each primary and secondary objective.


  1. Blinding to exposure variables of evaluators making subjective judgments about the study.


  1. Methods of dealing with confounding, such as:

8.1. Which confounders will be considered and how they will be defined

8.2. Adjustment for confounders in statistical models

8.3. Restriction in analysis

8.4. Matching, including PS matching

8.5. Self-controlled study designs

8.6. Statistical approach for any selection of a subset of confounders

8.7. Methods for assessing the level of confounding adjustment achieved

8.8. Sensitivity analyses for residual confounding


  1. Handling of missing data, including:

9.1. How missing data will be reported;

9.2. Methods of imputation;

9.3. Sensitivity analyses for handling missing data;

9.4. How censored data will be treated, with rationale.


  1. Fit of the model – if considered for a predictive model, including:

10.1. Criteria for assessing fit;

10.2. Alternative models in the event of clear lack of fit.


  1. Interim analyses – if considered:

11.1. Criteria, circumstances and possible drawbacks for performing an interim analysis and possible actions (including stopping rules) that can be taken on the basis of such an analysis


  1. How the achieved patient population will be characterised:

12.1. Description of target population;

12.2. Description of the analysis population if different, e.g. after PS matching or in IV analyses.


  1.  Treatment of multiplicity issues not elsewhere covered.


  1. Sample size considerations should be presented, making explicit the data source from which the expected variation of relevant quantities and the clinically relevant differences are derived. It should be noted that in observational studies on data that already exist and where no additional data can be collected, sample size is not preclusive and the ethical injunction against 'underpowered' studies has no obvious force provided the results, in particular the 'absence of effect' and 'insufficient evidence', are properly presented and interpreted.



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