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:\n\n\n\n\nDates: Tuesday 25th October | Tuesday 8th &\; Tues day 15th November 2022\nTime: \;14:00-15:30 GMT (each session)\nSpeake rs: \;Ondrej Slama \;(Roche)\, Yilong Zhang\, Monika Huhn \;(A straZeneca)\, Fiona Ehrich\, Guiyuan Lei \;(Roche)\, Katrin Roth \ ;(Bayer)\, \;Vincent Shen \;(Roche) \;and Ardalan Mirshani&nbs p\;(Novartis). \;\n\nWho is this event intended for? \;All statist icians and programmers.\nWhat is the benefit of attending? \;Understan ding the power\, flexibility and capability that R can bring.\nCost\nYou c an now register for this event. Fees for each webinar \;are as follows :\nPSI Members = free of charge\nNon-Members = \;£\;20+VAT \nRegi stration\nThis webinar series will occur over the course of 3 separate ses sions - it would be beneficial to attend all three\, though it is not mand atory. Each session has a separate registration\, as below.\n- To register for \;Webinar 1 (25th October)\, please click here.\n- To register fo r \;Webinar 2 \;(8th November)\, please click here.\n- To register for \;Webinar 3 \;(15th November)\, please click here.\nOverview\ nTalks in these webinars will cover general frameworks for app development \, regulatory submissions using R and case studies of using R Shiny in bot h operational and methodological settings. Shiny is an R package that make s it easy to build interactive web apps straight from R.\nSpeaker details\ n\n\n\n \n \n \n Speaker\n \n \n Biography\n \n \n Abstract\n \n \n \n \n Tue sday 25th October\n \n \n \n \n \n Ondrej Slama\n \n \n O ndrej joined Roche in the spring of 2019 as he started in SPA-DA team\, co ntributing to the NEST core and other molecule-related projects. He was pa rt of MS Floodlight\, Admiral\, and now works on Ophthalmology related pro jects\, part of which is VIStA\, which he is going to present. Ondrej come s from the Czech Republic where he studied the Faculty of Nuclear Sciences and Physical Engineering at \;CTU\, mostly focusing on mathematics\, which eventually brought him to machine learning and data science.\n \n \n VIStA - Visualizing Images with Statisti cal Analyses.\n In ophthalmology\, but also in other areas\, th ere is a growing need to promptly analyze non-standard clinical tabular da ta. This often includes imaging data with different imaging modalities. Vi sualizing Images with Statistical Analyses\, or VIStA\, is a framework tha t includes guidelines and template code to create interactive exploratory tools and analysis figures that relate clinical and/or imaging features wi th the underlying images. Our current use cases consist of analytical apps based mostly in R shiny\, but special use cases also include JS\, CSS\, a nd Python. Our vision is to create a unified approach to use statistical a nalyses and analytical tools for data scientists or TA teams requiring int eractive image visualization functionality.\n \n \n \n \n \n Yilong Zhang\n \n \n Dr. Yilong Zhang is a biostatistician at Meta and enjoys clinical development. Before joining Meta\, he worked as a statist ician at Merck and earned a PhD degree in biostatistics from New York Univ ersity. He co-authored a book in using R to fill process\, technical and t raining gaps for clinical trial analysis\, reporting\, and submission. His interests in statistical methods include study design\, missing data\, an d survival analysis. \;\n \n \n R-bas ed test submission to FDA.\n On 22 November 2021\, the R Consor tium R Submissions Working Group successfully submitted an R-based test su bmission package through the FDA eCTD gateway. FDA staff were able to repr oduce the numerical results.\n This submission\, an example pac kage following eCTD specifications\, included a proprietary R package\, R scripts for analysis\, R-based analysis data reviewer guide\, and other re quired eCTD components.\n To our knowledge\, this is the first publicly available R-based or open-source-language-based FDA submission pa ckage. We hope that our materials and what we learned can serve as a good reference for future R-based regulatory submissions from different sponsor s.\n To bring an experimental clinical product to market\, elec tronic submission of data\, computer programs\, and relevant documentation is required by health authority agencies from different countries. In the past\, submissions have been mainly based on the SAS language. In recent years\, the use of open-source languages\, especially the R language\, has become very popular in the pharmaceutical industry and research instituti ons. Although the health authorities accept submissions based on open-sour ce programming languages\, sponsors may be hesitant to conduct submissions using open-source languages due to a lack of working examples. Therefore\ , the R Consortium R Submissions Working Group aims to provide such exampl es as part of its focus on improving practices for R-based clinical trial regulatory submissions.\n https://github.com/RConsortium/submis sions-pilot1-to-fda\n \n \n \n \n \n Monika Huhn\n \n \n Monika Huhn has a background in mathematical statistics and has been with AstraZeneca for nearly \n 10 years. She has worked in a number of different roles\, starting as a study statistician in asthma clinical t rials and most recently working as a data scientist within the AZ Data Sci ence &\; AI department. Her work has mainly focused on designing clinic al trials and learning from clinical data. She has focused some of her tim e on writing software packages and creating web applications in order to e nable others to easily connect with data.\n Monika is very enth usiastic about utilizing data and finding new methods to answer scientific questions. She is also very interested in data visualization and differen t means of making data and analysis results accessible and understandable for all. \;\n \n \n OneView &ndash\; A Shiny app to unlock the full potential drug repositioning investigations . \;\n Monika Huhn &\; Shameer Khader\, Center for Artif icial Intelligence\, Data Science &\; Artificial Intelligence\, R&\; D\, AstraZeneca\n In many clinical and pre-clinical projects\, there is a need to connect biologists and clinicians with the data in a me aningful way. Data is often stored in repositories that are hard to access without programming knowledge or buried in a collection of unstructured a nd unconnected spreadsheets. R Shiny apps can often help us to bridge this gap and empower scientists to work with their data directly.\n One of the Shiny apps we have developed in recent years is OneView\, whic h brings together different data sources to accelerate drug repositioning investigations.\n Drug repositioning is an area of growing inte rest in drug development that can accelerate the discovery of new treatmen t options to benefit patients worldwide. Briefly\, drug repositioning refe rs to the systematic investigation of a novel disease indication for a dru g molecule. Drug repositioning can be accelerated using various tools and technologies\, including intelligent dashboards\, data integration and hum an-in-the-loop machine learning. \n The core data behind the On eView Shiny app are from an analysis comparing transcriptomic signatures o f drug molecules with hundreds of disease transcriptomic signatures\, crea ting connections between a compound and diseases based on an inverse corre lation between the transcriptomic signatures. To fully understand the sign ificance of the relationships\, and find further evidence to substantiate them\, OneView provides a dynamic dashboard enabling scientists to filter/ search within the data\, follow connections through multiple datasets\, an d provide meaningful interactive visualizations.\n \n \n \n \n Tuesday 8th November\n \n \n \n \n \n Fiona Ehrich\n \n \n Fiona Ehrich graduated from Columbia University in May with an MS in Biostatistics and currently works as an A ssistant Research Biostatistician at Memorial Sloan Kettering Cancer Cente r. During graduate school\, she interned at Cytel\, where she developed R Shiny applications enabling users to conduct customized clinical trial sim ulations. Prior to graduate school\, she worked for four years at Alkermes \, a biopharmaceutical company developing medicines in neuroscience and on cology. Fiona is interested in clinical trial design and multi-omics bioma rker development in oncology.\n  \;\n \n \n Adaptive Group Sequential Enrichment Designs App.\n Enrichment strategies in group sequential clinical trials may be co nsidered when there is evidence that a treatment may provide greater benef it to a particular subgroup of the patient population (eg\, biomarker-posi tive). Enrichment designs begin by recruiting the full patient population but build in the option to selectively recruit the prospectively-defined s ubgroup of interest for the remainder of the study based on the results of an interim analysis. An R Shiny app was developed to enable users to cond uct customized simulations of adaptive enrichment clinical trials\, using a promising zone approach with sample size re-estimation and early stoppin g for efficacy\, facilitating the selection of an optimized study design.\ n  \;\n \n \n \n \n \n Guiyuan Lei\n \n \n Guiyuan is a Senior Principal Statistical Scientist with Roche (UK). Curr ently she is a Project Lead Statistician and Data Sciences Team Lead for o ne oncology molecule. She had worked at Universities as a post-doc and res earch associate (statistician) in Sweden and UK for five years before join ed Roche in 2008. At Roche\, Guiyuan has developed extensive experience in drug development within multiple therapeutic areas including I2O\, Hemato logy and Solid Tumors. Outside of the molecule work\, Guiyuan dedicates he r time to many initiatives\, most prominently\, Advancing Inclusive Resear ch. She has long experience in programming and developed several R Shiny a pps. Guiyuan is passionate to help patients both as a professional and as a breast cancer survivor.\n \n \n R Shiny App for Trial Diversity Dashboard.\n Diversity in clinical tri als means enrolling trial participants to reflect the intended patient pop ulation. FDA released draft guidance for industry on diversity plan in Apr il 2022 which recommends that sponsors should define enrollment goals for underrepresented racial and ethnic participants as early as practicable in clinical development for a given indication. The R Shiny app for trial di versity dashboard is for benchmarking and real-time monitoring of the perc entage of each race\, ethnicity\, or gender category from clinical trials\ , overall\, by indication\, by study\, by country\, etc. With a live demo using dummy data\, the author will demonstrate key features of the Shiny a pp: real-time data source\, interactive visualization\, and automatic repo rting.\n  \;\n  \;\n \n \n \n \n \n Katrin Roth\n \n \n Katrin joined Bayer as a PhD Scholar in 2006 and continued as a clinical statistician in 2009 after receiving her PhD i n Mathematical Statistics from the University of Magdeburg\, Germany. Katr in has significant experience in designing and supporting clinical studies and projects in Women&rsquo\;s Health\, Pulmonology\, Anti-Infectives\, O ncology\, Radiology\, and Pain therapeutic areas. Katrin is an active memb er of Bayer's Biostatistics Innovation Center (BIC) Dose-Finding group and a newly elected member and the speaker of the BIC Steering Committee. Kat rin is also a member of the Conference Advisory Committee supporting the I nternational Biometric Society.\n \n \n T he dosedesignR &ndash\; an interactive tool for planning dose finding stud ies\n Zhenglei Gao\, Franco Mendolia\, Christoph Neumann\, Katr in Roth\, Thomas Schmelter\, Katrin Walkamp (Bayer AG)\n We are presenting an interactive tool that applies the theory of optimal experim ental design to facilitate the planning of dose finding studies. In drug d evelopment\, phase 2 dose finding studies aim at estimating the dose-respo nse relationship and thereby finding a therapeutic dose for further develo pment in phase 3. The quality of the study design usually depends on the ( unknown) shape of the dose-response curve. The planning of dose finding st udies is therefore typically an effort that requires substantial input fro m non-statistical experts from\, e.g.\, pharmacology or pharmacokinetics. \n Statisticians from Bayer&lsquo\;s Biostatistics Innovation C enter (BIC) group on dose finding have developed an R shiny [1] app called dosedesignR. The tool is based on the dosefinding R package [2] and aims at facilitating the interactive process during the planning of a dose find ing study.\n [1] Chang W\, Cheng J\, Allaire JJ\, Xie Y and McP herson J (2018). shiny: Web Application Framework for R. https://CRAN.R-pr oject.org/package=shiny \n [2] Bornkamp B.\, Pinheiro J. and Br etz F. (2016). DoseFinding: Planning and Analyzing Dose Finding Experiment s. https://CRAN.R-project.org/package=DoseFinding.\n \n \n \n \n Tuesday 15th November\n \ n \n \n \n \n Vincent Shen\ n \n \n Vincent Shen is from Roche PD Dat a Sciences\, and is currently the Chief Product Owner for NEST\, which is a set of open-sourced R packages that generate\, deliver and catalog insig hts for clinical studies\, in both static and interactive formats. During his 8 years at Roche\, Vincent has taken various data science-related role s in areas such as medical affairs\, real-world evidence\, and PD. Prior t o that\, he worked at Princess Margaret Cancer Centre as a biostatistician focusing on supporting research in lung and head &\; neck cancer. Vinc ent completed his Bachelor of Science degree from University of Hong Kong and Master of Mathematics from University of Waterloo. In his spare time\, Vincent enjoys building LEGOs\, playing piano and cheering for Manchester United.\n  \;\n \n \n NES T - productionize a comprehensive open-source toolkit in R. \;\n In this talk\, we will share how we productionize a comprehensive op en-source toolkit like NEST and transform how we generate and deliver insi ghts for clinical trials. By applying product thinking mindset and introdu cing product owners to the various components of the project\, we drive a roadmap that allows us to consistently make improvements on the toolkit an d closely engage with users. The open-source nature of the product also le ads to a multi-layer collaboration model for both internal users and exter nal companies. We will also discuss how NEST delivers unique values to cli nical trial analysis\, how users are engaged in the development of the too lkit\, and our vision on the influence this toolkit brings to clinical tri al insights generation.\n \n \n \n \n \n Ardalan Mirshani\n \n \n Ardalan Mirshani works as a Data Scientist at Novartis. He holds a phd in statistics from Penn State University. He specializes on scientif ic tool creation\, reproducible workflows\, production-grade shiny applica tions\, and automating repetitive operations associated with data explorat ion\, modeling\, reporting\, and productization processes in the Innovatio n and Technology group.\n  \;\n \n \n Democratizing Shiny App Development.\n As clinical data exploration continues to grow\, there is an increasing need for inter active graphical displays created with Shiny apps. To date\, the developme nt and deployment of study apps have required specialized knowledge and co nsiderable effort. However\, the similarity across domains in clinical stu dies motivated us to build a comprehensive framework that scales shiny app creation across the portfolio. The Datapipeline harmonized framework demo cratizes the shiny app creation. It enables non-technical associates to cr eate and deploy professional shiny apps quickly. It also empowers shiny de velopers to build reusable shiny modules that may be easily shared in a pl ug-and-play manner\, ultimately accelerating future application developmen t.\n \n \n \n\n \; DTEND:20221115T153000Z DTSTAMP:20260517T060332Z DTSTART:20221025T140000Z LOCATION: SEQUENCE:0 SUMMARY:PSI Webinar Series: Showcasing R use in Pharma UID:RFCALITEM639145946125766222 X-ALT-DESC;FMTTYPE=text/html:Sponsored by logo
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\nDates: Tuesday 25th October | Tuesday 8t h &\; Tuesday 15th November 2022
\nTime: \;14 :00-15:30 GMT (each session)
\nSpeakers: \;Ondre j Slama \;(Roche)\, Yilong Zhang\, Monika Huhn \;(Ast raZeneca)\, Fiona Ehrich\, Guiyuan Lei \;(Roche)\, Katri n Roth \;(Bayer)\, \;Vincent Shen \;(Roche)& nbsp\;and Ardalan Mirshani \;(Novartis). \;
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\nWho is this event intended for? \;All statisticians and programmers.
\nWhat is the benefit of attending? \;Understanding the power\, flexibility and capability that R can br ing.
\n

Cost

\n

You can now register for this event. Fees fo r each webinar \;are as follows:
\nPSI M embers = free of charge
\nNon-Members = \;£\;20+VAT

\n

Registration

\n

This webinar series will occur over the course of 3 separate sessions - it would be beneficial to attend all three\, though it is not mandatory. Each session has a separate registration\, as below.
\n- To register for \;Webinar 1 (25th October)\, please click here.
\n- To register for \;< strong>Webinar 2 \;(8th November)\, please < strong>click here.
\n- To register for \;Webinar 3&n bsp\;(15th November)\, please click here.

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Overview

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Talks in these webinars will cover general frameworks for app development\ , regulatory submissions using R and case studies of using R Shiny in both operational and methodological settings. Shiny is an R package that makes it easy to build interactive web apps straight from R.

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Speaker d etails

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Dr. Yilong Zhang is a biostatistician at Meta and enjoys clinical development. Before joining Meta\, he worked as a statistician at Merck and earned a PhD degree in bi ostatistics from New York University. He co-authored a book in using R to fill process\, technical and training gaps for clinical trial analysis\, r eporting\, and submission. His interests in statistical methods include st udy design\, missing data\, and survival analysis. \;

\n \n \n < /tr>\n \n \n \n \n \n \n \n \n \n \n < td valign="top" style="width: 274px\;">\n

Fiona Ehrich gradu ated from Columbia University in May with an MS in Biostatistics and curre ntly works as an Assistant Research Biostatistician at Memorial Sloan Kett ering Cancer Center. During graduate school\, she interned at Cytel\, wher e she developed R Shiny applications enabling users to conduct customized clinical trial simulations. Prior to graduate school\, she worked for four years at Alkermes\, a biopharmaceutical company developing medicines in n euroscience and oncology. Fiona is interested in clinical trial design and multi-omics biomarker development in oncology.

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Guiyuanedit
\n Guiyuan Lei

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Speaker

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Biography

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Abstract

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Tuesday 25th October

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Ondrejedit
\n Ondrej Sla ma

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Ondrej joined Roche in the spring of 2019 as he started in SPA-DA team\, contributing to the NEST core and other molecule -related projects. He was part of MS Floodlight\, Admiral\, and now works on Ophthalmology related projects\, part of which is VIStA\, which he is g oing to present. Ondrej comes from the Czech Republic where he studied the Faculty of Nuclear Sciences and Physical Engineering at \;CTU\, mostl y focusing on mathematics\, which eventually brought him to machine learni ng and data science.

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VIStA - Visualizing Images with Statistical Analyses.
\n In ophthalmology\, but also in other areas\, there is a growing need to promptly analyze non -standard clinical tabular data. This often includes imaging data with dif ferent imaging modalities. Visualizing Images with Statistical Analyses\, or VIStA\, is a framework that includes guidelines and template code to cr eate interactive exploratory tools and analysis figures that relate clinic al and/or imaging features with the underlying images. Our current use cas es consist of analytical apps based mostly in R shiny\, but special use ca ses also include JS\, CSS\, and Python. Our vision is to create a unified approach to use statistical analyses and analytical tools for data scienti sts or TA teams requiring interactive image visualization functionality.\n

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Yilongedit\n Yilong Zhang

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R-based test submission to FDA.
\n On 22 November 2021\, the R Consortium R Submissions Working Group successful ly submitted an R-based test submission package through the FDA eCTD gatew ay. FDA staff were able to reproduce the numerical results.

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This submission\, an example package following eCTD specifications\, included a proprietary R package\, R scripts for analysis\, R-based analy sis data reviewer guide\, and other required eCTD components.

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To our knowledge\, this is the first publicly available R-based or open-source-language-based FDA submission package. We hope that our mater ials and what we learned can serve as a good reference for future R-based regulatory submissions from different sponsors.

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To bri ng an experimental clinical product to market\, electronic submission of d ata\, computer programs\, and relevant documentation is required by health authority agencies from different countries. In the past\, submissions ha ve been mainly based on the SAS language. In recent years\, the use of ope n-source languages\, especially the R language\, has become very popular i n the pharmaceutical industry and research institutions. Although the heal th authorities accept submissions based on open-source programming languag es\, sponsors may be hesitant to conduct submissions using open-source lan guages due to a lack of working examples. Therefore\, the R Consortium R S ubmissions Working Group aims to provide such examples as part of its focu s on improving practices for R-based clinical trial regulatory submissions .

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https://github.com/RConsort ium/submissions-pilot1-to-fda

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Monikaedit
\n Monika Huhn

\n 		\n

Monika Huhn has a background in mathematical statist ics and has been with AstraZeneca for nearly

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10 years . She has worked in a number of different roles\, starting as a study stat istician in asthma clinical trials and most recently working as a data sci entist within the AZ Data Science &\; AI department. Her work has mainl y focused on designing clinical trials and learning from clinical data. Sh e has focused some of her time on writing software packages and creating w eb applications in order to enable others to easily connect with data.

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Monika is very enthusiastic about utilizing data and find ing new methods to answer scientific questions. She is also very intereste d in data visualization and different means of making data and analysis re sults accessible and understandable for all. \;

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OneView &ndash\; A Shiny app to unlock the full potential drug reposi tioning investigations. \;
\n Monika Huhn &\; Shameer Khader\, Center for Artifi cial Intelligence\, Data Science &\; Artificial Intelligence\, R&\;D \, AstraZeneca

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In many clinical and pre-cl inical projects\, there is a need to connect biologists and clinicians wit h the data in a meaningful way. Data is often stored in repositories that are hard to access without programming knowledge or buried in a collection of unstructured and unconnected spreadsheets. R Shiny apps can often help us to bridge this gap and empower scientists to work with their data dire ctly.

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One of the Shiny apps we have developed in recen t years is OneView\, which brings together different data sources to accel erate drug repositioning investigations.
\n Drug repositio ning is an area of growing interest in drug development that can accelerat e the discovery of new treatment options to benefit patients worldwide. Br iefly\, drug repositioning refers to the systematic investigation of a nov el disease indication for a drug molecule. Drug repositioning can be accel erated using various tools and technologies\, including intelligent dashbo ards\, data integration and human-in-the-loop machine learning.

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The core data behind the OneView Shiny app are from an analysis comparing transcriptomic signatures of drug molecules with hundreds of di sease transcriptomic signatures\, creating connections between a compound and diseases based on an inverse correlation between the transcriptomic si gnatures. To fully understand the significance of the relationships\, and find further evidence to substantiate them\, OneView provides a dynamic da shboard enabling scientists to filter/search within the data\, follow conn ections through multiple datasets\, and provide meaningful interactive vis ualizations.

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Tuesday 8th November

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Fionaedit
\n Fiona Ehrich

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Adaptive Group Seq uential Enrichment Designs App.
\n Enrichment str ategies in group sequential clinical trials may be considered when there i s evidence that a treatment may provide greater benefit to a particular su bgroup of the patient population (eg\, biomarker-positive). Enrichment des igns begin by recruiting the full patient population but build in the opti on to selectively recruit the prospectively-defined subgroup of interest f or the remainder of the study based on the results of an interim analysis. An R Shiny app was developed to enable users to conduct customized simula tions of adaptive enrichment clinical trials\, using a promising zone appr oach with sample size re-estimation and early stopping for efficacy\, faci litating the selection of an optimized study design.

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& nbsp\;

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Guiyuan i s a Senior Principal Statistical Scientist with Roche (UK). Currently she is a Project Lead Statistician and Data Sciences Team Lead for one oncolog y molecule. She had worked at Universities as a post-doc and research asso ciate (statistician) in Sweden and UK for five years before joined Roche i n 2008. At Roche\, Guiyuan has developed extensive experience in drug deve lopment within multiple therapeutic areas including I2O\, Hematology and S olid Tumors. Outside of the molecule work\, Guiyuan dedicates her time to many initiatives\, most prominently\, Advancing Inclusive Research. She ha s long experience in programming and developed several R Shiny apps. Guiyu an is passionate to help patients both as a professional and as a breast c ancer survivor.

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R Shiny App for Trial Diversity Dashboard.
\n Diversity in clinical trials means enrolling trial participants to reflect the intended patient population. FDA released draft guidance for industry on diversity plan in April 2022 w hich recommends that sponsors should define enrollment goals for underrepr esented racial and ethnic participants as early as practicable in clinical development for a given indication. The R Shiny app for trial diversity d ashboard is for benchmarking and real-time monitoring of the percentage of each race\, ethnicity\, or gender category from clinical trials\, overall \, by indication\, by study\, by country\, etc. With a live demo using dum my data\, the author will demonstrate key features of the Shiny app: real- time data source\, interactive visualization\, and automatic reporting.

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Katrinedit
\n K atrin Roth

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Katrin joined Bayer as a PhD Scholar in 2006 and continued as a clinical statistician in 2009 after receiving her PhD in Mathematical Statistics from the University of Magdeburg\, Germany . Katrin has significant experience in designing and supporting clinical s tudies and projects in Women&rsquo\;s Health\, Pulmonology\, Anti-Infectiv es\, Oncology\, Radiology\, and Pain therapeutic areas. Katrin is an activ e member of Bayer's Biostatistics Innovation Center (BIC) Dose-Finding gro up and a newly elected member and the speaker of the BIC Steering Committe e. Katrin is also a member of the Conference Advisory Committee supporting the International Biometric Society.

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The dosed esignR &ndash\; an interactive tool for planning dose finding studies
\n Zhenglei Gao\, Franco Mendolia\, Christoph Neumann\, Katrin Roth\, Thom as Schmelter\, Katrin Walkamp (Bayer AG)

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W e are presenting an interactive tool that applies the theory of optimal ex perimental design to facilitate the planning of dose finding studies. In d rug development\, phase 2 dose finding studies aim at estimating the dose- response relationship and thereby finding a therapeutic dose for further d evelopment in phase 3. The quality of the study design usually depends on the (unknown) shape of the dose-response curve. The planning of dose findi ng studies is therefore typically an effort that requires substantial inpu t from non-statistical experts from\, e.g.\, pharmacology or pharmacokinet ics.

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Statisticians from Bayer&lsquo\;s Biostatistics Innovation Center (BIC) group on dose finding have developed an R shiny [1 ] app called dosedesignR. The tool is based on the dosefinding R package [ 2] and aims at facilitating the interactive process during the planning of a dose finding study.

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[1] Chang W\, Cheng J\, Allaire JJ\, Xie Y and McPherson J (2018). sh iny: Web Application Framework for R. https://CRAN.R-project.org/pack age=shiny

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[2] Bornkamp B.\, Pinheiro J. and Bretz F. (2016). DoseFinding: Planning a nd Analyzing Dose Finding Experiments. https://CRAN.R-project.org/pac kage=DoseFinding.

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Tuesday 15th No vember

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Vincentedit
\n Vincent Shen

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Vincent Shen is from Roche PD Data Sciences\, and is currently the Chief Product Owner for NEST\, which is a set of open-sourced R packages that ge nerate\, deliver and catalog insights for clinical studies\, in both stati c and interactive formats. During his 8 years at Roche\, Vincent has taken various data science-related roles in areas such as medical affairs\, rea l-world evidence\, and PD. Prior to that\, he worked at Princess Margaret Cancer Centre as a biostatistician focusing on supporting research in lung and head &\; neck cancer. Vincent completed his Bachelor of Science de gree from University of Hong Kong and Master of Mathematics from Universit y of Waterloo. In his spare time\, Vincent enjoys building LEGOs\, playing piano and cheering for Manchester United.

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NEST - productionize a comprehensive open-source t oolkit in R. \;
\n In this talk\, we will share how we productionize a comprehensive open-source toolkit like NEST and transform how we generate and deliver insights for clinical trial s. By applying product thinking mindset and introducing product owners to the various components of the project\, we drive a roadmap that allows us to consistently make improvements on the toolkit and closely engage with u sers. The open-source nature of the product also leads to a multi-layer co llaboration model for both internal users and external companies. We will also discuss how NEST delivers unique values to clinical trial analysis\, how users are engaged in the development of the toolkit\, and our vision o n the influence this toolkit brings to clinical trial insights generation.

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Ardalanedit< br />\n Ardalan Mirshani

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Ardalan Mi rshani works as a Data Scientist at Novartis. He holds a phd in statistics from Penn State University. He specializes on scientific tool creation\, reproducible workflows\, production-grade shiny applications\, and automat ing repetitive operations associated with data exploration\, modeling\, re porting\, and productization processes in the Innovation and Technology gr oup.

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Democratizing Shiny App Development.
\n As clinical data explor ation continues to grow\, there is an increasing need for interactive grap hical displays created with Shiny apps. To date\, the development and depl oyment of study apps have required specialized knowledge and considerable effort. However\, the similarity across domains in clinical studies motiva ted us to build a comprehensive framework that scales shiny app creation a cross the portfolio. The Datapipeline harmonized framework democratizes th e shiny app creation. It enables non-technical associates to create and de ploy professional shiny apps quickly. It also empowers shiny developers to build reusable shiny modules that may be easily shared in a plug-and-play manner\, ultimately accelerating future application development.

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END:VEVENT END:VCALENDAR