Guidance for Industry: The Reference Section

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Anyone working in the pharmaceutical industry must have good knowledge of the relevant regulations. While attending courses and workshops is very useful in staying up to date, there is really no substitute to reading the source documents themselves. Here is my living currated “Reference Section” of regulatory documents and guidance that I keep going back to all the time. It may be useful to anyone working in clinical sample testing and/or IVD development.

Setting up to run Mistral LLM locally

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Here is a quick troubleshooting walkthrough on how I set up dolphin-2.5-mixtral-8x7b to run locally with cuBLAS back-end for hardware acceleration. I experienced a few annoying hangups in the process, so hopefully this little note can help anyone experiencing the same.

Design of Experiments (DOE): The Overview

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Design of Experiments (DOE) is a systematic approach to planning, conducting, and analyzing efficient scientific experiments. It is an indispensable tool in optimization of complex processes, especially in engineering and manufacturing. Unlike traditional one-variable-at-a-time methods, DOE involves simultaneously varying multiple factors to efficiently assess their individual and interactive effects on the outcome. The most obvious benefit of DOE is that it allows to dramatically reduce the number of experiments needed to characterize a system (well, depending on the design you choose). But more importantly, it allows us to uncover interactions between variables, something that one-variable-at-a-time testing simply cannot do.

This note gives a very top level overview of DOE as a technique, while a detailed look at various methods will come in subsequent posts.

SDTM vs. CDASH: Why do we need two standards?

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In the realm of clinical research and data management, adherence to standardized formats and structures is paramount. Two such standards, CDASH (Clinical Data Acquisition Standards Harmonization) and SDTM (Study Data Tabulation Model), play crucial roles in ensuring consistency and interoperability across clinical trial data. There is a lot of overlap between CDASH and SDTM, which often creates a false impression that they serve the same purpose, or even compete with each other. In reality, the two standards are designed to complement each other, while addressing similar but distinct needs. In thi note, we delve into the key differences between these two standards to better understand their respective roles in the clinical research landscape.

How to Manage conda Environments

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If you are using Anaconda, the popular python distribution for data science, you know that it comes with its own package and environment manager called conda.

There are many tutorials out there, that tell you to update your conda environments using conda update --all. Luckily, you know that this is a terrible idea!

Understanding Clinical Endpoints

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Anyone involved in clinical trials would be familiar with the term “endpoint”, but it is surprising how many clinical professionals cannot clearly define what the term means or what differentiates an endpoint from an objective. This note is intended to bring some clarity on the matter.

XGBoost: Quick Reference

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In the realm of machine learning algorithms, XGBoost stands out as a powerful tool for predictive modeling. Its efficiency, flexibility, and accuracy have made it a favorite among data scientists and machine learning practitioners. This note delves into what XGBoost is, how it works, and demonstrates its implementation using Python with illustrative examples.

Clinical Research Abbreviations: The Cheatsheet

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When you step into the labyrinth of clinical research, you find yourself surrounded by hedge walls made of acronyms and abbreviations. The clinical world is laden with shorthand that can seem like a secret language to the uninitiated. Yet, behind these cryptic codes lie vital information crucial for patient care. This living note is my personal attempt to keep the alphabet soup of healthcare terminology under control and to unlock the secrets hidden within.