Biometrics

Biometrics or biostatistics are essential for clinical trials. Demonstrating the effectiveness of human interventions within clinical trials, evaluating diagnostic procedures, estimating the prevalence of side effects and proving scientific hypotheses is possible only by applying adequate biometrics. Since clinical examinations are indispensable for medical, pharmaceutical and technological departments as well as required by law for the approval of novel forms of therapy or novel medical devices, it is clear why biometrics play a particularly important role in clinical assessments.

Biometrics are of essential clinical importance for the definition of the main outcomes (what is being investigated?) and the measurement methods (how should the main objective be estimated?). The chosen evaluation methods (for example multivariate or univariate, intention-to-treat or per protocol), the randomization type (for example cluster randomization), the number of assessments or visits and the sample size estimation can decisively contribute to the success and quality of a clinical trial.

An incorrect number (too many or too little) of participants or assessment days in clinical studies is not economical and does not comply with ethical principles either (i.e. not more people than necessary should be exposed to a risk; studies should be stopped if interims analysis shows there is no possibility for a clinical benefit). Furthermore, inappropriate randomization or analysis methods can lead to undesired events (keyword: risk of bias). Subsequently, major impacts on study results can occur (keyword: placebo or nocebo effect), which in turn may lead to non-consideration of the study results by the scientific community or the local authorities.

CCMD would like to support your company in biometrics. We offer you  individually coordinated statistical planning in the initial phase as well as statistical support during the whole study, so you can carry out clinical trials without being bothered by any concerns.  You can fully concentrate on the content of the study without having to be afraid the planned study could subsequently be classified as non-significant due to methodological or statistical errors.