To promote structured targeted data analysis.


An analysis plan should be created and finalized prior to the data analyses.


The analysis plan (Guidelines per study type are provided below)

  • Executing researcher: To create the analysis plan prior to the data analyses, containing a description of the research question and what the various steps in the analysis are going to be. This should also be signed and dated by the PI.
  • Project leaders: To inform the executing researcher about setting up the analysis plan before analyses are undertaken.
  • Research assistant: N.a.
How To

An analysis plan should be created and finalized (signed and dated by PI) prior to the data analyses. The analysis plan contains a description of the research question and what the various steps in the analysis are going to be. It also contains an exploration of literature (what is already know? What will this study add?) to make sure your research question is relevant (see Glasziou et al. Lancet 2014 on avoiding research waste).The analysis plan is intended as a starting point for the analysis. It ensures that the analysis can be undertaken in a targeted manner, and promotes research integrity.

If you will perform an exploratory study you can adjust your analysis based on the data you find; this may be useful if not much is known about the research subject, but it is considered as relatively low level evidence and it should be clearly mentioned in your report that the presented study is exploratory. If you want to perform an hypothesis-testing study (be it interventional or using observational data) you need to pre-specify the analyses you intend to do prior to performing the analysis, including the population, subgroups, stratifications and statistical tests. If deviations from the analysis plan are made during the study this should be documented in the analysis plan and stated in the report (i.e. post-hoc tests). If you intend to do hypothesis-free research with multiple testing you should pre-specify your threshold for statistical significance according to the number of analyses you will perform. Lastly, if you intend to perform an RCT, the analysis plan is practically set in stone. (Also see ICH E9 - statistical principles for clinical trials)

If needed, an exploratory analysis may be part of the analysis plan, to inform the setting up of the final analysis (see initial data analysis). For instance, you may want to know distributions of values in order to create meaningful categories, or determine whether data are normally distributed. The findings and decisions made during these preliminary exploratory analyses should be clearly documented, preferably in a version two of the analysis plan, and made reproducible by providing the data analysis syntax (in SPSS, SAS, STATA, R) (see guideline Documentation of data analysis).

The concrete research question needs to be formulated firstly within the analysis plan following the literature review; this is the question intended to be answered by the analyses. Concrete research questions may be defined using the acronym PICO: Population, Intervention, Comparison, Outcomes. An example of a concrete question could be: “Does frequent bending at work lead to an elevated risk of lower back pain occurring in employees?” (Population = Employees; Intervention = Frequent bending; Comparison = Infrequent bending; Outcome = Occurrence of back pain). Concrete research questions are essential for determining the analyses required.

The analysis plan should then describe the primary and secondary outcomes, the determinants and data needed, and which statistical techniques are to be used to analyse the data. The following issues need to be considered in this process and described where applicable:

  • In case of a trial: is the trial a superiority, non-inferiority or equivalence trial.
  1. Superiority: treatment A is better than the control.
  2. Non-inferiority: treatment A is not worse than treatment B.
  3. Equivalence: testing similarity using a tolerance range.

In other studies: what is the study design (case control, longitudinal cohort etc).

  • Which (subgroup of the) population is to be included in the analyses? Which groups will you compare?;
  • What are the primary and secondary endpoints? Which data from which endpoint (T1, T2, etc.) will be used?;
  • Which (dependent and independent) variables are to be used in the analyses and how are the variables to be analysed (e.g. continuous or in categories)?;
  • Which variables are to be investigated as potential confounders or effect modifiers (and why) and how are these variables to be analysed? There are different ways of dealing with confounders. We distinguish the following:
    1) correct for all potential confounders (and do not concern about the question whether or not a variable is a ‘real’ confounder). Mostly, confounders are split up in little groups (demographic factors, clinical parameters, etc.). As a result you get corrected model 1, corrected model 2, etc. However, pay attention to collinearity and overcorrection if confounders coincide too much with primary determinants.
    2) if the sample size is not big enough relative to the number of potential confounders,  you may consider to only correct for those confounders that are relevant for the association between determinant and outcome. To select the relevant confounders, mostly a forward selection procedure is performed. In this case the confounders are added to the model one by one (the confounder that is associated strongest first). Subsequently, consider to what extent the effect of the variable of interest is changed. Then first choose the strongest confounder in the model. Subsequently, repeat this procedure untill no confounder has a relevant effect (<10% change in regression coefficient). Alternatively, you can select the confounders that univariately change the point estimate of the association with >10%.
    3) Another option is to set up a Directed Acyclic Graph (DAG), to determine which confounders should be added to the model. Please see for more information.
  • How to deal with missing values? (see chapter on handeling missing data);
  • Which analyses are to be carried out in which order (e.g. univariable analyses, multivariable analyses, analysis of confounders, analysis of interaction effects, analysis of sub-populations, etc.)?; Which sensitivity analyses will be performed?
  • Do the data meet the criteria for the specific statistical technique?

A statistician may need to be consulted regarding the choice of statistical techniques (also see this intanetpage on statistical analysis plan).

It is recommended to already design  the empty tables to be included in the article prior to the start of data analysis. This is often very helpful in deciding which analyses are exactly required in order to analyse the data in a targeted manner.

You may consider to make your study protocol including the (statistical) analysis plan public, either by placing in on a publicly accessible website (Concept Paper/Design paper) or by uploading it in an appropriate studies register (for human trials: NTR/EUDRACT/, for non-/preclinicaltrials:

Check the reporting guidelines when writing an analysis plan. These will help increase the quality of your research and guide you.

Randomised trials



Observational studies



Systematic reviews



Study protocols



Diagnostic/prognostic studies



Case reports



Clinical practice guidelines



Qualitative research



Animal pre-clinical studies



Quality improvement studies



Economic evaluations