Statistics and Methodology

Our Statistics and Methodology Team offers a wide range of comprehensive services to support your research endeavors.

We provide consultancy services in research design and statistical analysis, ensuring that your study is equipped with the appropriate methodologies to effectively address your research questions.

Our services include contributions to the statistical sections of study protocols and grant proposals, development of statistical analysis plans, execution of statistical analyses, preparation of statistical reports, assistance with manuscript writing, and support for Central Data Monitoring activities to ensure data integrity and reliability throughout your study.

Our Services

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Research Question and Study Design

We help you refine your research questions and choose suitable endpoints and study designs.

To gauge your study's feasibility, we pinpoint possible challenges and, address critical methodological concerns, determine suitable statistical methods, and provide sample size estimates.
Grant Proposal / Study Protocol We write or review the statistical analysis section in your grant proposal or study protocol and provide feedback regarding methodological issues.
Sample Size Calculation We develop a rough analysis plan for evaluating the primary endpoint and calculate the sample size based on the assumptions.
Statistical Analysis Plan We develop a detailed statistical analysis plan for all primary and secondary analyses, including descriptions of how the results will be presented.
Interim Analyses and Central Data Monitoring
We provide central data monitoring on accumulating data in collaboration with the Quality Assurance and Monitoring division. 

As your study progresses, we accompany and advise you on all emerging questions. We conduct all planned interim analyses and can provide an independent statistician for the Independent Data Monitoring Committee.
Final Analyses, Results Presentation, Publication After closing the database, we typically perform a series of data consistency checks.

Subsequently, if necessary, we prepare the data for planned analyses in multiple steps and conduct them as specified in the analysis plan.

We prepare a report as defined in the Statistical Analysis Plan and provide the results in tables and graphs, and assist you in interpreting the results and describing the methods used.
Manuscript Writing and Publication We take responsibility as co-authors for the resulting manuscripts by drafting the statistical sections, providing publication-ready tables and figures, write-up/provide feedback on the methods section, results and discussion, and addressing all comments and queries from reviewers regarding statistics.
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We offer our services for various study types, such as:
Our expertise covers a wide range of statistical methods, including:
  • Cohort and case-control studies
  • Randomized-controlled trials, including cluster-randomized trials
  • Diagnostic accuracy and method comparison studies
  • Meta-analyses, including meta-regression and network meta-analysis
  • Multivariable regression-based model-building
  • Derivation and validation of prediction models and prediction tools
  • Machine learning methods, e.g., random forest, regression trees, vector machines
  • Multilevel/mixed-effects models
  • Multivariate analysis (psychometrics)
  • Survival analysis, including competing risk and multi-state models
  • Bayesian statistics

Do you want us to analyze a dataset from your own database ?

If you want us to analyze a dataset from your own database, the following should be read carefully. Not taking this advice into account may create substantial additional preparatory work.

Statistical analysis software (Stata, R, SAS, SPSS, etc.) is based on a uniform, rectangular data structure: the lines represent the cases (e.g., patients) and the columns represent the variables e.g. identification number, sex, age, hemoglobin level. Such a file contains only one line per case (wide format). In this format, multiple measurements of a variable over time (e.g. the developing of laboratory values) must be characterized by several variables (e.g., BLOOD1, BLOOD2, etc.).

In order to process data with the software used by our Statistics division, certain conditions have to be met:

  • Avoid data collection in Excel because there is no audit trail nor access control as required by the human research act (HFG). Rather, use a proper database like REDCap. If nevertheless done, the guidance below should be followed.
  • Stata or R data files can be input directly. A labelled dataset is preferred. If not available, a data dictionary with explanation of the dataset is required.
  • ASCII files e.g. .txt- and .csv-files require special precautionary measures concerning the separator and the coding of missing values. Their use should be limited to cases for which other ways of conversion do not exist.
  • Data-files from other statistical software such as SAS or SPSS may also be possible but need to be checked carefully beforehand.
  • Under no circumstances should data be input in word processing software.

Excel files

Statisticians may spend hours transferring Excel tables to files readable by statistical software. This time can be better used for analysis and interpretation. Hence, please consider:

  • Choose a simple table structure that can be easily exported/imported.
  • One variable per column.

Variables

  • The first line contains the variable names.
  • Name the variables according to the following convention: start with a letter, no special characters or spaces (letters, numbers, underscores "_" are allowed), no longer than 32 characters.
  • The variable name should reflect the content of the variable.
  • For multiple measurements: Use names such as hb1-hb10.
  • Do not allocate a variable name twice.
  • Variables/columns must be uniformly formatted and include uniform entries (i.e., only numbers, only dates (formatted as 03.04.97 or 03.04.1997) or only text).

Data

  • De-identify data by changing names to numbers.
  • Avoid special characters (use also only sparingly in text columns and only if unavoidable). In particular, do not use semicolons (as they can be interpreted as separators)!
  • Leave cells with missing data completely empty.
  • If calculations have been done in Excel: Input the results as numerical values instead of as formulas, which are recalculated each time the table is opened (1. Copy; 2. Paste special; 3. Paste-Values). Delete all columns containing results that were computed from other columns.
  • One value per field only; do not overlay a field with a second value (this is possible in Excel but creates additional observations when transferred to statistical software).