BEGIN:VCALENDAR VERSION:2.0 METHOD:PUBLISH PRODID:-//Telerik Inc.//Sitefinity CMS 15.4//EN BEGIN:VTIMEZONE TZID:UTC BEGIN:STANDARD DTSTART;VALUE=DATE:20250101 TZNAME:UTC TZOFFSETFROM:+0000 TZOFFSETTO:+0000 END:STANDARD END:VTIMEZONE BEGIN:VEVENT DESCRIPTION:Date: Thursday 1 October 2026Time: \;10:00 - 11:30 BST | 11 :00 - 12:30 CETLocation: \;Online via ZoomWho is this event intended f or? \;Anyone with an interest in the statistical analysis of ctDNA dat a within oncology.What is the benefit of attending? \;Learn about ctDN A data and gain insights into different methods and metrics that are curre ntly used to analyse ctDNA data.OverviewAs cancers grow\, they can release genetic material such as DNA into the blood stream known as circulating t umour DNA (ctDNA) which can be detected by blood tests. ctDNA has emerged as a promising biomarker over the last few years across several cancer typ es including non-small cell lung cancer\, breast and colorectal cancer. In different settings\, ctDNA has been shown to predict time-to-event outcom es\, disease recurrence and treatment response\, detect minimal residual d isease\, and enable risk stratification. However\, clear guidance is lacki ng on which ctDNA metrics are most informative and which statistical model ling frameworks are appropriate to effectively predict long-term clinical responses\, support early patient stratification\, adaptive decision-makin g as well as early efficacy assessment.To address this gap\, we\, on behal f of the Biomarker Special Interest Group of the PSI\, conducted a targete d literature review on statistical modelling approaches for ctDNA data to gain insights into different methods and metrics that are mainly used nowa days. In this webinar we will discuss the emerging overall themes and summ arise the reviewed methods that were published between 2023 and 2025. We w ill also consider what data are typically required for modelling\, and the ir potential application in answering patient-centric questions with regar d to precision medicine. We aim to provide practical recommendations on wh ich ctDNA metrics and statistical models might be best used for which clin ical endpoints to further support the utility of ctDNA in clinical trials. RegistrationThis webinar is free to attend for PSI Members &\; Non-Memb ers.To register for this event\, please \;click here.Speaker details\n .table img {\n width: 150px\;\n height: 225px\;\n object-fit: cov er\;\n }\n\n\nSpeakerBiographyHolly Tovey\, Principal Statistician at The Institute of Cancer Research \;Holly Tovey is a principal statisticia n in the Clinical Trials and Statistics Unit at the Institute of Cancer Re search. She completed her PhD in 2024 on the identification of biomarkers to predict response in triple negative breast cancer. Her research interes ts focus on the incorporation of biomarkers into clinical trials as endpoi nts for patient selection/stratification.\n Saskia Stä\;ndler\, Head of Statistical Programming &ndash\; Translational Science at Evidenz e Germany GmbHSaskia holds a PhD in Biology and started her professional c areer as a Statistical Programmer in April 2022 at the CRO &bdquo\;Evidenz e Germany&ldquo\; in Essen (Germany). She mainly works as a SAS programmer with a focus on biomarker analysis studies and Companion Diagnostics. Bes ides that\, she establishes her own programming team at Evidenze Germany s ince early 2026 and is actively involved in working groups of PSI and Phus e.\n Sara Bellinvia\, Principal Biomarker Statistician &\; Data Scientist at Evidenze Germany GmbHSara works at the Biomarker Statistics & amp\; Data Science Department of the CRO Evidenze Germany where she gained several years of experience in biomarker research. Her daily work revolve s around the analysis of biomarker data as part of clinical trials\, inclu ding exploratory analysis and generalised analysis pipelines. Sara holds a PhD in Biology and has a long-standing passion and interest for statistic s and data science.Lidia Sacchetto\, Senior Biomarker Statistician at Baye r AG \;Lidia is a Senior Biomarker Statistician in Clinical Statistics &\; Analytics at Bayer Pharmaceuticals in Berlin (Germany). She holds a PhD in Mathematics and applies innovative quantitative methods to suppor t biomarker research and drug development. Her interests span statistical methodology\, data science\, biomarker strategy\, and cross-functional col laboration in precision medicine.\n Rebecca Freudling\, Principal D ata Scientist at Staburo GmbH \;Rebecca is Associate Director of Biost atistics at Staburo with focus on biomarkers in clinical development. She has several years of experience in biomarker data analysis for clinical tr ials across multiple therapeutic areas\, particularly oncology. Rebecca re ceived her Master's degree in Biostatistics from Ludwig Maximilian Univers ity of Munich in 2017.\n DTEND:20261001T123000Z DTSTAMP:20260810T130007Z DTSTART:20261001T110000Z LOCATION: SEQUENCE:0 SUMMARY:PSI/EFSPI Biomarker SIG Webinar: Statistical Methods for ctDNA Data UID:RFCALITEM639219636073373200 X-ALT-DESC;FMTTYPE=text/html:

Date: Thursday 1 October 2 026
Time: \;10:00 - 11:30 BST | 11:00 - 12:30 CE T
Location: \;Online via Zoom

Who is this event intended for? \;Anyone w ith an interest in the statistical analysis of ctDNA data within oncology.
What is the benefit of attending? \;Learn about ctDNA data and gain insights into different methods and metrics that are currently used to analyse ctDNA data.

Overview

As cancers grow\, they can release genetic material such as DNA into t he blood stream known as circulating tumour DNA (ctDNA) which can be detec ted by blood tests. ctDNA has emerged as a promising biomarker over the la st few years across several cancer types including non-small cell lung can cer\, breast and colorectal cancer. In different settings\, ctDNA has been shown to predict time-to-event outcomes\, disease recurrence and treatmen t response\, detect minimal residual disease\, and enable risk stratificat ion. However\, clear guidance is lacking on which ctDNA metrics are most i nformative and which statistical modelling frameworks are appropriate to e ffectively predict long-term clinical responses\, support early patient st ratification\, adaptive decision-making as well as early efficacy assessme nt.

To address this gap\, we\, on behalf of the Biomarker Special In terest Group of the PSI\, conducted a targeted literature review on statis tical modelling approaches for ctDNA data to gain insights into different methods and metrics that are mainly used nowadays. In this webinar we will discuss the emerging overall themes and summarise the reviewed methods th at were published between 2023 and 2025. We will also consider what data a re typically required for modelling\, and their potential application in a nswering patient-centric questions with regard to precision medicine. We a im to provide practical recommendations on which ctDNA metrics and statist ical models might be best used for which clinical endpoints to further sup port the utility of ctDNA in clinical trials.

Registration

This webinar is free to attend for PSI Members &\; Non-Members.
To register fo r this event\, please \;cl ick here.

Speaker details

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Spea kerBiography

Holly Tovey\, Principal Statistician a t The Institute of Cancer Research

 \;

Holly Tovey is a principal statistician in the Clinical Trials and Statistics U nit at the Institute of Cancer Research. She completed her PhD in 2024 on the identification of biomarkers to predict response in triple negative br east cancer. Her research interests focus on the incorporation of biomarke rs into clinical trials as endpoints for patient selection/stratification. \n

Saskia Stä\;ndler\, Head of Statistical Programming &ndash\; Translat ional Science at Evidenze Germany GmbH

Saskia ho lds a PhD in Biology and started her professional career as a Statistical Programmer in April 2022 at the CRO &bdquo\;Evidenze Germany&ldquo\; in Es sen (Germany). She mainly works as a SAS programmer with a focus on biomar ker analysis studies and Companion Diagnostics. Besides that\, she establi shes her own programming team at Evidenze Germany since early 2026 and is actively involved in working groups of PSI and Phuse.\n

Sara Bellinvia\, Principal Biomarker Statistician &\; Data Scientist at Evidenze Germany GmbH

Sara works at the Biomarker Statistics &\; Data Science Department of the CRO Eviden ze Germany where she gained several years of experience in biomarker resea rch. Her daily work revolves around the analysis of biomarker data as part of clinical trials\, including exploratory analysis and generalised analy sis pipelines. Sara holds a PhD in Biology and has a long-standing passion and interest for statistics and data science.

Lidia Sacchetto\, Senior Biomarker Statistician at Bayer AG

 \;

Lidia is a Senior Biomarker Sta tistician in Clinical Statistics &\; Analytics at Bayer Pharmaceuticals in Berlin (Germany). She holds a PhD in Mathematics and applies innovativ e quantitative methods to support biomarker research and drug development. Her interests span statistical methodology\, data science\, biomarker str ategy\, and cross-functional collaboration in precision medicine.\n

Rebecca Freudling\, Principa l Data Scientist at Staburo GmbH

 \;

Rebecca is Associate Director of Biostatistics at Staburo with focus on biomarker s in clinical development. She has several years of experience in biomarke r data analysis for clinical trials across multiple therapeutic areas\, pa rticularly oncology. Rebecca received her Master's degree in Biostatistics from Ludwig Maximilian University of Munich in 2017.\n

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