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ENCePP Guide on Methodological Standards in Pharmacoepidemiology

 

6.4. 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.

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

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

  4. 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.

  5. 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.

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

  7. Blinding evaluators to exposure variables in order to avoid making subjective judgments about the study.

  8. Methods of dealing with confounding, such as:
    1. Which confounders will be considered and how they will be defined
    2. Adjustment for confounders in statistical models
    3. Restriction in analysis
    4. Matching, including propensity-score matching
    5. Self-controlled study designs
    6. Statistical approach for any selection of a subset of confounders
    7. Methods for assessing the level of confounding adjustment achieved
    8. Sensitivity analysis for residual confounding

  9. Handling of missing data, including:
    1. How missing data will be reported;
    2. Methods of imputation;
    3. Sensitivity analyses for handling missing data;
    4. How censored data will be treated and rationale

  10. Fit of the model – if considered for a predictive model, including:
    1. Criteria for assessing fit;
    2. Alternative models in the event of clear lack of it.

  11. Interim analyses – if considered:
    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

  12. How the achieved patient population will be characterised:
    1. Description of target population;
    2. Description of the analysis population if different, e.g. after propensity score matching or in instrumental variable analyses.

  13. Treatment of multiplicity issues not elsewhere covered.

  14. 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.

 

 

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