Staburo supports OncologyInformationService in Data Analytics 

Staburo recently started a successful collaboration with OncologyInformationService – O.I.s)! Staburo supports O.I.s) in the analysis of Real World Data in oncology.

O.I.s) has been providing an innovative, proprietary and tailor-made methodology and expertise in the field of Real World Data for oncology and hematology for over 20 years. O.I.s) supports the majority of specialized pharmaceutical companies in market access and market development across Europe. In addition, the company and its founder Lenka Kellermann, are members of several medical associations, such as the “Deutsche Krebsgesellschaft” (DKG), DGHO and ASCO, bringing together patients, physicians and researchers into an environment designed to improve the outcomes. We are looking forward to a great sustainable cooperation!

Training@Staburo: clustering in the context of precision medicine

This presentation about clustering, differentiated between supervised and unsupervised methods, and introduced clustering as an important tool for subgroup identification, in the context of precision medicine and clinical trials.

Precision medicine means tailoring the treatment to specific patient subgroups, i.e. patients characterized by specific genetic alterations, or similar biomarker profiles. Clustering, as an unsupervised technique for subgroup identification, groups patients e.g. based on similarities in biomarkers, rather than on a given knowledge, like tumor type.

The importance of clustering for precision medicine and clinical trials was illustrated, based on two well-known examples of high clinical importance in oncology.

Hierarchical clustering was used to identify several patient subgroups in gene expression data of breast cancer patients. Those subgroups are characterized by different gene expression patterns, as well as different prognosis, regarding clinical endpoints, e.g. overall survival. Especially the HER2 gene is now a well-established prognostic marker, associated with poor survival in breast cancer.

The Consensus Molecular Subgroups in colorectal cancer are cluster-based subgroups with high clinical relevance, as well. Those subgroups were characterized by several expert groups, based on different supervised and unsupervised methods, e.g. hierarchical clustering, or partitional clustering with k-means, as unsupervised techniques. The experts identified 4 subgroups of which one is characterized by poor prognosis in patients with BRAF mutations and MSI.

During this talk, the concepts of both hierarchical and partitional clustering with k-means, were introduced and illustrated based on mentioned examples.

Staburo at the 12th IGSEE Forum

Staburo participated at the career fair that was organised by the International Graduate School of Science and Engineering (IGSSE), at the TUM Science and Study Center in Raitenhaslach. The IGSSE, which has won the German Excellence Initiative twice, stands for innovative research and has very close contacts to international research partners.

Dr. Michael Matiu introduced Staburo to the plenum and presented our biostatistics focus topics. Michael was afterwards available, as discussion partner at the fair. The exposition was organised, to provide network opportunities for highly qualified young researchers within academia and with industry partners, like biostatistics expert company Staburo.

Training@Staburo: How to use cross-validation to obtain reliable subgroup effects

Nicole Krämer, project leader Translational Medicine and Biomarker, explained how we can use cross-validation to obtain reliable subgroup effects.

The identification of patient subgroups, who benefit from a new treatment, is of crucial importance in precision medicine. Many data-driven subgroup identification algorithms are out there and in general, they differ in the underlying estimand and in the way the subgroup is identified. However, due to their data-driven nature, the relative treatment effect (e.g. the hazard ratio or risk difference) within the identified subgroup, is almost always too good to be true and cannot be reproduced in a new trial.

Nicole explained how to apply cross-validation, to obtain more reproducible subgroup effects. In each cross-validation split, the subgroup identification algorithm is applied to the training set. The obtained rule is used, to assign patients in the test set, to the subgroup or its complement. This cross-validated assignment can then be used, to estimate the subgroup effect.

Staburo @ EMA scientific advice meeting in London 

Staburo Managing Director Josef Höfler visited the European Medicines Agency (EMA) in London to have a scientific advice meeting, for a client’s clinical development project. 

Statistical concepts for adaptive designs with sample size re-assessment were commented by EMA’s experts during the meeting.

Furthermore, the pivotal study design with respect to the statistical analyses was discussed. The input from EMA experts was very valuable to increase the chance for less questions from the authorities during the market authorization process – provided that the trial results will be positive.