Patient Stratification Analyses

A stratification analysis aims at identifying sub-populations of patients where the likelihood of observing a given phenotype (e.g. disease evolution, response to treatment etc) is increased. The patients sub-populations are defined using on one or more biomarkers.

Stratification analysis is used to to identify predictive biomarkers. For example, markers able to predict the response to a drug. These markers are fundamental to precision medicine approaches and is the basis of companion diagnostic tests.

In addition the the predictive markers, stratification is also used for the identification of prognostic biomarkers. That is, markers able to predict the evolution of a disease. Prognostic markers provide interesting insights in key disease mechanisms, not necessarily linked to drug treatment.

Both predictive and prognostic markers contribute to increase knowledge on the drug and the underlying disease mechanisms, bringing value to clinical development projects. Ultimately stratification analyses aim at identifying the most adapted drugs to a group of patients.

  • Biomarker study design +

    In every study, the design is key. It is particularly true in exploratory biomarker studies, where statistical power cannot be controlled and where sample logisitics can have an important impact on input data quality and availability. Read More
  • Statistical analysis plan +

    The Statistical Analysis Plan (SAP) is the blueprint of the analysis that will be performed. Read More
  • Biomarker Data Management +

    Biomarker data management is a key step in every clinical bioinformatics study. Approximately 40% of the overall analysis time is dedicated to these tasks, which are crucial for the quality of the overall analysis. Read More
  • 1
  • Patient Stratification Analyses +

    A stratification analysis aims at identifying sub-populations of patients where the likelihood of observing a given phenotype (e.g. disease evolution, response to treatment etc) is increased. The patients sub-populations are defined using on one or more biomarkers. Read More
  • Pharmacodynamic Analyses +

    A pharmacodynamic analysis aims at identifying biomarkers measured over time, whose change in expression levels is correlated with the drug administration and dose.Biomarkers identified by this analysis may give important insights on the mode of action of a drug, and can ultimately become predictive markers if their changes are correlated with clinical efficacy (surrogate biomarkers).
  • Quality Control +

    Biomarker data are heterogeneous. Technology-specific quality controls must be applied in order to identify possible sources of bias that could impact the analysis. The biology behind the assay and the sample logistics in clinical settings are two of the most important factors that can impact the biomarker data quality. 
  • Biological assessment +

    Numbers are nothing without the biology which is behind. All results from our data analysis projects are reviewed from a biological perspective.
  • 1
  • Tailored Visualizations +

    A picture is worth a thousand words. This is particularly true for complex biological data. Drawing constructive conclusions from a biomarker analysis requires adapted visualisation methods, aligned to the objectives of the analysis. We use our biotechnological and computational expertise to provide the best graphical representation for your data.
  • Archiving +

    Exploratory biomarker data analyses generate hypotheses that can be used years later to support a submission. Ensuring proper archiving and retrieval of raw data, derived data, and results, is therefore instrumental in the biomarker development process.
  • Publication support +

    As the data scientists analysing your data, we are in the best position to support the publication of your results (patents, posters, articles). We  generate customised figures, tables, and listings that bring out your data.
  • 1