Empowering Biotech Innovation Through Biostatistical Mentoring
Context
For over 25 years, Creascience’s biostatisticians have been collaborating with both young startups and established pharmaceutical and biotechnology companies to train their scientific staff and assist them in setting up, analyzing and interpreting and reporting scientific studies. Building on this experience, we are proud to present a new mentoring program aimed at increasing the autonomy of research teams within biotechnology startups with regard to good statistical and scientific practices.
Scientific research is an essential and critical activity for any startup in the field of life sciences, regardless of its stage of development. What is more, the sustainability of startups in this field depends directly on the quality of this research, whether directly to ensure the validity of the results obtained or to convince potential investors. Finally, the outcome and ultimate success inevitably involve confronting regulatory authorities at one point or another.
All these aspects make the use of good scientific practices and the ability to convince external stakeholders of them essential. In this context, statistical methods – which go well beyond the analysis of experimental data – constitute essential tools, as demonstrated in particular by their omnipresence in the standards of institutions in the health field.
For example…
- USA : The FDA requires that nonclinical trials follow a formal, detailed experimental design that includes steps taken to reduce bias.
- UK : The MHRA produces a specific guide for medical devices in particular to supervise the statistical aspects of R&D.
- Europe: EMA produces specific guidance to support the management of missing data: from trial planning to data analysis
Why Biostatistics Mentoring Matters
The benefits of using rigorous statistical methods are numerous. These include:
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Common Statistical Challenges in Biotech
Biostatistics plays a critical role in biotech research, but it also faces several challenges. These challenges arise from the unique nature of biotech products, complex data types, and regulatory scrutiny. Here are some common challenges in biostatistics within biotech research.
Small Sample Sizes
Biotech research often involves limited replicates due to cost and ethical considerations.
Our mentors specialize in methods optimized for small samples such as adaptive designs that maximize information from minimal observations while maintaining statistical validity.
Multivariate Data Generation
Modern biotechnology generates vast multivariate datasets through genomics, proteomics, and high-throughput screening.
Mentors may train teams in dimension reduction techniques, multiple testing corrections, and machine learning approaches that extract meaningful signals from complex data.
Handling Missing Data
Biological experiments inevitably generate missing values that can bias results if handled improperly.
Our biostatistical mentors provide expertise in handling missing data so that data integrity and statistical rigor are preserved.
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