Development↦Data Management↦Database Development↦Variables
What is it? Why is it important?
Variables are medical values or information of interest to a researcher. During study conduct, only variables defined in the study protocol can be collected and used for analysis.
Variables are either:
- Quantitative based on the process of counting or measuring something (e.g. age, weight, lab values, health scores, percentage)
- Qualitative which are non-numerical characteristics but can be fitted according to categories (e.g. eye colour, gender, education)
In studies one differentiates between:
- An outcome or dependent variable: is a variable that researchers want to investigate (i.e. understand, explain, or predict)
- A predictor or independent variable: is a variable that potentially has an effect on the outcome
- A confounding variable: is a variable that can lead to the misinterpretation of study results
When addressing a research question, researchers study the association between a study outcome of interest and one (or several) predictor(s). It is important to identify confounding variables in order to control their potential impact on study outcome.
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Reecommended guidelines to follow:
- Collected variables should provide all necessary information needed by a researcher to answer the study question
- Variables entered in the study database (eCRF) should be collected as completely as possible. Incomplete or missing variables should be avoided
- Some variables are collected at different visits, and can therefore have different values based on the collection time point
- Variable names should provide an idea of its content. A nomenclature integrating related events and the type of variable collected should be used (e.g. preop_temp_val)
- In order to facilitate data analysis, qualitative variables can be coded (e.g. 1 for Yes, 0 for No)
What do I need to do?
As a SP-INV:
Define variables needed to answer your study question including:
- The source (SD) from where the data is retrieved (e.g. laboratory reports, physical examination, medical history, patient file, participant diary)
- Means used to collect data (e.g. iPad/tablet, pCRF, questionnaires, physician interviews)
Consult a statistician to ensure variables are suitable for statistical analysis, such as:
- The selection of appropriate and measurable study variables. This is especially important in order to ensure variables are useful in the interpretation of study results (e.g. study endpoint)
- The implementation of harmonised data formats (e.g. implemented codes, classifications)
Document selected variables and the rational for their study inclusion in the study protocol, and as applicable the DMP
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During study conduct the collection of variables might depend on study progress, such as:
- Some study endpoint variables are only collected towards the end of a study (e.g. cancer status 6 months after treatment stop, % blood pressure improvement after 6 weeks treatment compared to baseline)
- Descriptive variables might only be collected at the beginning of the study, such as during a screening- or baseline visit (e.g. age, gender, disease characteristics at study start, level of education, socioeconomic factors)
The visit-plan provides a good overview to study staff and ensures that no variables are forgotten during study conduct. Thus, the collection of study variables should be well structured:
- List study visits in chronological order based on study progress (e.g. screening visit, baseline visit, followed by interim- until last study visit)
- Group variables according to topic (e.g. lab data), collection time point, and entry operator
Where can I get help?
Your local CTU↧ can support you with experienced staff regarding this topic
Basel, Departement Klinische Forschung, CTU, dkf.unibas.ch
Lugano, Clinical Trials Unit, CTU-EOC, www.ctueoc.ch
Bern, Clinical Trials Unit, CTU, www.ctu.unibe.ch
Geneva, Clinical Research Center, CRC, crc.hug.ch
Lausanne, Clinical Research Center, CRC, www.chuv.ch
St. Gallen, Clinical Trials Unit, CTU, www.kssg.ch
Zürich, Clinical Trials Center, CTC, www.usz.ch
References
ICH GCP E6(R2) – see in particular guideline
- 5.5. Trial Management, data handling, and record-keeping
Swiss Law
ClinO – see in particular article and annex
- Art. 5 Rules of Good Clinical Practice
- Annex 3 Application documents to be submitted to EC