Uploaded May 2025 | Updated September 2026, 1 week ago
#datascience #dataanalysis #technology #machinelearning #clinicaltrials #placebo
In this episode, Jing Dai, Director of Biostatistics at Jazz Pharmaceuticals, shares insights from the PHUSE US Connect conference and her work on applying machine learning to neuroscience clinical trials. She discusses challenges like high placebo response and attrition, the value of interdisciplinary collaboration, and how AI/ML can shape trial design, improve regulatory readiness, and move the field toward more objective, data-driven outcomes.
In this episode, you will learn:
– How machine learning can help address high placebo response and attrition in neuroscience clinical trials.
– Why traditional statistical models struggle with high-dimensional clinical data.
– Key regulatory frameworks (GxP, GMLP) for ensuring AI/ML models meet compliance standards in drug development.
– Practical tips for fostering interdisciplinary collaboration between biostatisticians, clinicians, and data scientists.
Nat Chrzanowska
linkedin.com/in/nat-chrzanowska
Jing Dai
linkedin.com/in/jingdai1009
__________________________________________
More about Appsilon: ► appsilon.com
Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments. Schedule a free consultation with our expert
► appsilon.com/contact-us
We design scalable and user-friendly Shiny dashboards to help you make data-driven decisions.
► appsilon.com/services/data-dashboards
We design and implement Statistical Computing Environment for R and Python for efficient data analysis:
► appsilon.com/services/sce
We design, implement, and optimise data analysis environments so you can focus focus on insights and innovation.
► appsilon.com/services/platform
Where Technology Meets Science podcast is available on all podcasting platforms:
► Spotify: open.spotify.com/show/6MckhYFZCwGU4Op6P09foR
► Apple Podcasts: apple.co/3CJDdVy
__________________________________________
For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog:
► appsilon.com/blog
LinkedIn: linkedin.com/company/appsilon
__________________________________________
00:00 – Introduction to the episode and our guest
02:00 – Experience at PHUSE US Connect conference
03:45 – Focus on neuroscience clinical trials
05:55 – Leveraging machine learning to address challenges
06:56 – Machine learning vs. traditional statistical modeling
09:30 – Regulatory compliance and GMLP
15:53 – Reproducibility and addressing bias
20:30 – Examples of AI/ML in clinical trials
24:52 – Challenges in interdisciplinary collaboration
29:55 – Unsolved problems in the field
33:17 – Outro and closing remarks
#datascience #dataanalysis #technology #machinelearning #clinicaltrials #placebo
In this episode, Jing Dai, Director of Biostatistics at Jazz Pharmaceuticals, shares insights from the PHUSE US Connect conference and her work on applying machine learning to neuroscience clinical trials. She discusses challenges like high placebo response and attrition, the value of interdisciplinary collaboration, and how AI/ML can shape trial design, improve regulatory readiness, and move the field toward more objective, data-driven outcomes.
In this episode, you will learn:
– How machine learning can help address high placebo response and attrition in neuroscience clinical trials.
– Why traditional statistical models struggle with high-dimensional clinical data.
– Key regulatory frameworks (GxP, GMLP) for ensuring AI/ML models meet compliance standards in drug development.
– Practical tips for fostering interdisciplinary collaboration between biostatisticians, clinicians, and data scientists.
Nat Chrzanowska
linkedin.com/in/nat-chrzanowska
Jing Dai
linkedin.com/in/jingdai1009
__________________________________________
More about Appsilon: ► appsilon.com
Appsilon empowers pharmaceutical and life sciences companies to leverage open-source technology for faster, data-driven decision-making in regulated environments. Schedule a free consultation with our expert
► appsilon.com/contact-us
We design scalable and user-friendly Shiny dashboards to help you make data-driven decisions.
► appsilon.com/services/data-dashboards
We design and implement Statistical Computing Environment for R and Python for efficient data analysis:
► appsilon.com/services/sce
We design, implement, and optimise data analysis environments so you can focus focus on insights and innovation.
► appsilon.com/services/platform
Where Technology Meets Science podcast is available on all podcasting platforms:
► Spotify: open.spotify.com/show/6MckhYFZCwGU4Op6P09foR
► Apple Podcasts: apple.co/3CJDdVy
__________________________________________
For more insights about how technology helps scientists push the boundaries of data analysis and reporting check out our blog:
► appsilon.com/blog
LinkedIn: linkedin.com/company/appsilon
__________________________________________
00:00 – Introduction to the episode and our guest
02:00 – Experience at PHUSE US Connect conference
03:45 – Focus on neuroscience clinical trials
05:55 – Leveraging machine learning to address challenges
06:56 – Machine learning vs. traditional statistical modeling
09:30 – Regulatory compliance and GMLP
15:53 – Reproducibility and addressing bias
20:30 – Examples of AI/ML in clinical trials
24:52 – Challenges in interdisciplinary collaboration
29:55 – Unsolved problems in the field
33:17 – Outro and closing remarks
