We thank our employees and clients, who helped us, to make 2019 very successful for our company!
We wish all partners and future partners, our employees, and their families a wonderful Christmas and a great start in 2020.
Staburo will continue to support you with experienced biostatisticians and statistical programmers next year and in the years to come! Thank you!
It is a very nice custom to have our Staburo statistics training session on the first Monday of the two-week Oktoberfest period. This year, the Staburo team had the chance to learn about our new R shiny App in the first presentation “OTTO – Demonstration and Methodology”. OTTO stands for Optimal Tool for Tracking of Outliers. Afterwards we heard a talk about “Sample Size Calculation in Time-to-Event Trials with Non-Proportional Hazards Using GESTATE”, presented by our new colleague Jasmin.
After lunch, there where two more Talks, one about “Optimal Plate Layout for Relative Potency Estimation – A Simulation Study” and the last one from our new employee Armin about “Subgroup identification by generalized Patient Rule Induction Method (PRIM)”.
As always, this was a great opportunity to share knowledge with the whole Staburo team, get the chance to get to know the team better and to exchange ideas afterwards.
The second important custom is to pay the Oktoberfest (or also called “Wiesn”) a visit, after the workshop. Once again, we had a wonderful time with our growing team and some long-time Staburo clients.
Everyone got home safely – with additional knowledge from the workshop and positive vibes from the Oktoberfest. We are already looking forward to the Staburo “Wiesn” Workshop 2020 and thank all participants for making this a great team event!
Together with Cinfa Biotech GmbH (now Mundipharma Biologics) and the University of Lucerne, Staburo recently published the first part of the results for a biosimilar which is used for preventing neutropenia, induced by chemotherapy (click here to read the whole article DOI: 10.1002/prp2.503). As part of the collaboration, Staburo Principal Statistician and Managing Director Josef Höfler supported the interdisciplinary team with biostatistics expertise, especially in PK/PD.
The team planned, conducted and analysed a single dose, randomized, double-blind, two-way crossover study and demonstrated the comparability for the two PK endpoints examined (area under the concentration curve from time zero to last measurable concentration and the maximum concentration). The PD comparability (area under the effect curve for absolute neutrophil count [ANC]) was demonstrated, as well. The team could show that there are clinically no differences between biosimilar and the originator with regard to immunogenicity and safety. The investigated drug was approved as biosimilar in Europe.
Staburo supported the evaluation of the first multicentric analytical comparability study of PD-L1 expression in urothelial bladder cancer using four clinically developed immunohistochemistry assays. Statistical analyses included the investigation of inter-assay differences using ANOVA and intra-class correlations and the concordance for clinically relevant cut-offs based on Fleiss‘ kappa.
The results suggest that all four currently clinically relevant assays are analytically similar for the evaluation of PD-L1-stained immune cells and three (SP263, 22C3, and 28-8) for PD-L1-stained tumor cells.
The paper, which was recently published in Virchows Archiv, can be downloaded here (Staburo’s Statistics contribution is mentioned in the Acknowledgments): https://rd.springer.com/article/10.1007%2Fs00428-019-02610-z
The availability of reliable and sensitive assays is an important building block in the production and quality control of biological products. In the development of such assays, unusual or erroneous measurement results need to be detected, but adjudication by human operators is often inconsistent or prone to bias.
To help overcome this challenge, a Shiny app for the statistical detection of such outliers is being developed at Staburo: the Optimal Tool for Tracking of Outliers (OTTO). Close collaboration with the end users throughout the development process ensures that the statistical outlier detection results are in good agreement with subject matter expert evaluations. In particular, a mixed method approach was chosen which significantly reduced false outlier detection rates, compared to conventional statistical methods.
Thanks to the capabilities of the R Shiny technology, the user interface is intuitive and well suited for everyday use in the lab. Features of the app include application of outlier tests, automatic curve refitting after removal of outliers, and report generation for documentation purposes.
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