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Friday, November 17, 2023 Single-Day Event Presentations

Presentation Title (click for description)	Speaker(s)	Slides
Teaching Methods for SAS Programmers: How to Make Learning Fun	Yutaka Morioka, EPS Corporation	Slides (PDF, 4.1 MB)
Generating Valuable Dummy Data for Analytical Method Development	Tadashi Matsuno, Yoshitake Kitanishi, Shionogi & Co., Ltd.	Slides (PDF, 1.8 MB)
Advancement of Investigation Tasks in Drug Discovery Research Through the Utilization of Generative AI	Yui Yamaguchi, NTT DATA Japan	Slides (PDF, 3.3 MB)
Innovative Approaches for Clinical Trial Data Analysis and RWD Analysis	Toshiaki Habu, SAS	Slides (PDF, 4.4 MB)
Creating Clinical Tables in R with rtables Package	Tomoyuki Namai, Yumi Nishimoto, Chugai Pharmaceutical Co., Ltd.	Slides (PDF, 1.0 MB)
Automatic Generation of Python Programs for Creating SDTM Datasets	Kunihito Ebi, Fujitsu	Slides (PDF, 713 KB)
Development of ADaM Creation Tool Towards Future Automation	Ryo Nakaya, Takeda	Slides (PDF, 722 KB)
Overview of R {admiral}	Junko Urata, Yasutaka Moriguchi, GlaxoSmithKline KK	Slides (PDF, 971 KB)
Lessons Learned from Admiralophtha Development Activities	Yuki Matsunaga, Novartis Pharma K.K.	Slides (PDF, 1.0 MB)

Presentation Descriptions

Teaching Methods for SAS Programmers. How to Make Learning Fun
Yutaka Morioka, EPS Corporation

How to train statistical programming, especially SAS programmers, is an educational challenge for every company. What kind of exercises should we give them to practice data handling and visualization, so that they can learn while having fun? I would like to share my personal opinion on how to approach learning biostatistics and programming together. This presentation will also include a lot of SAS code and techniques!
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Generating Valuable Dummy Data for Analytical Method Development
Tadashi Matsuno, Shionogi & Co., Ltd.
Yoshitake Kitanishi, Shionogi & Co., Ltd.

With the evolution of information technology, companies have gained the capability to collect a large amount and variety of data. This enables the advancement of methodologies and algorithms, fostering improvements in productivity and innovation across various domains. On the other hand, it is also necessary to consider the confidentiality and protection of personal information in the collected data, so there may be instances where data remains underutilized. For example, in the case of pharmaceutical companies, this would apply to clinical trial data. In this session, we will present a report on approaches taken to ensure both confidentiality and anonymity of data, while preserving its characteristics and information content as much as possible.
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Advancement of Investigation Tasks in Drug Discovery Research Through the Utilization of Generative AI
Yui Yamaguchi, NTT DATA Japan

There are various investigation tasks in the drug discovery field. High-quality data is essential for hypothesis building and verification when determining the details of drug discovery targets and experiments. Support from AI is necessary to extract useful information from a large amount of data in a short time. LITRON, NTT Data's document comprehension AI, supports the advancement of investigation tasks. By combining it with large language models such as ChatGPT, it is possible to search for document sets such as internal research results and obtain evidence-based responses in a chat format. It can efficiently learn with a small amount of data and extract information from a large number of input documents, taking into account the context, in a tabular format. This enables various analyses such as prediction and classification. LITRON is a versatile solution that can be applied to processes other than target search.
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Innovative Approaches for Clinical Trial Data Analysis and RWD Analysis
Toshiaki Habu, SAS

Ensuring the quality of clinical data analysis and improving the efficiency of clinical data management are critical topics to life science organizations. In addition to clinical trial data, using real-world data (RWD) has the potential to save the cost of research and development, and shorten the time to patient by leveraging the rich information RWD can provide outside the context of randomized clinical trial. Life science organizations can leverage SAS Life Science Analytics Framework (SAS LSAF) and SAS Health Clinical Acceleration to modernize the clinical trial data analysis workflow, as well as leverage SAS Health Cohort Builder to streamline the process of real-world evidence generation. With trusted solutions provided by SAS, life science organizations can deliver innovative products to patients faster to improve patient outcome and population health. In this presentation, we would like to showcase how SAS latest solutions can help life science organizations improve the efficiency of analyzing clinical trial data and RWD while having the governance on the quality and reliability of analyses results.
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Creating Clinical Tables in R with rtables Package
Tomoyuki Namai, Chugai Pharmaceutical Co., Ltd.
Yumi Nishimoto, Chugai Pharmaceutical Co., Ltd.

Pharmaverse is a connected network of companies and individuals working to promote collaborative development of curated open source R packages for clinical reporting usage in pharma. (https://pharmaverse.org/) It curates many R packages for end-to-end clinical reporting, and several packages for TFLs creation are also listed there. We selected the rtables and tern packages, which enable us to create complex clinical tables easily, and tried to build various tables required in CSR or CTD. We will focus mainly on the rtables package, presenting its usage and reporting on the benefits and difficulties we have noticed with this package.
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Automatic Generation of Python Programs for Creating SDTM Datasets
Kunihito Ebi, Fujitsu

While an approach to generate SAS or R programs automatically for creating SDTM/ADaM datasets is often heard in the pharma industry, this presentation explains automatic generation of Python programs with new technologies. There are some key technologies driving next level of automations behind the scenes. For example, TransCelerate's Digital Data Flow is an enabler of automatic creation of a SDTM specification from a study protocol. A technology that enables creating SDTM programs from SDTM specification is already on the market. Emerging generative AIs such as ChatGPT help less experienced programmers create complete Python programs effectively. This presentation summarizes these technologies from the clinical trial data science perspective and introduces an example of automatic generation of python programs for creating SDTM datasets in this context.
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Development of ADaM Creation Tool Towards Future Automation
Ryo Nakaya, Takeda Pharmaceutical Company Limited

Automation in the creation of statistical deliverables including SDTM, ADaM, and TFLs is one of hot topics in drug development area in pharmaceutical industry. Various attempts have been made to achieve this, such as developing automated generation tools and introducing low-code and hyper-automation solutions. In this presentation, we introduce one of our attempts to develop a tool that generates ADaM datasets with a standardized and streamlined process towards future automation as part of our business process internalization efforts, which has resulted in a reduced workload, time and cost while keeping high quality.
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Overview of R {admiral}
Junko Urata, GlaxoSmithKline K.K.
Yasutaka Moriguchi, GlaxoSmithKline K.K.

The pharmaverse provides a collection of R packages designed to enable clinical reporting in R. {admiral} is "ADaM in R Asset Library", providing an open source, modularized toolbox that enables the pharmaceutical programming community to develop ADaM datasets in R. The package is available from CRAN and developed by >30 authors and >20 contributors. In this session, we will give a quick overview of {admiral}. In addition, examples of coding of {admiral} will be presented.
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Lessons Learned from Admiralophtha Development Activities
Yuki Matsunaga, Novartis Pharma K.K.

Admiral - ADaM in R Asset Library is a toolbox for programming Clinical Data Interchange Standards Consortium (CDISC) compliant Analysis Data Model (ADaM) datasets in open-source R. It is developed by pharmaverse - a connected network of companies and individuals working to promote collaborative development of curated open-source R packages for clinical reporting usage in pharma. Admiralophtha (https://pharmaverse.github.io/admiralophtha/main/) is an extension package for ophthalmology-specific datasets. This extension package was developed by a combined Roche and Novartis team, and v0.1.0 was released in March 2023. I will share lessons learned based on my experience in admiralophtha development activities.
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Presenter Biographies

Kunihito Ebi

Kunihito Ebi has been IT Consultant/Product Manager at Fujitsu for the past 20 years, offering software products and services to the pharmaceutical industry. Ebi is a product manager of CDISC-based metadata management system with SDTM automation capability and a product owner of business applications that enable Digital Data Flow. Ebi is also an authorized instructor of CDISC Define-XML and a developer of open-source Define-XML tool sets.


Toshiaki Habu

Toshiaki Habu is the manager of the Life Sciences section in the consulting department at SAS institute Japan. He has worked at a pharmaceutical company, and he has a lot of experience in IT in the life sciences industry.


Yoshitake Kitanishi

Dr. Yoshitake Kitanishi, Vice President, Head of Data Science Department, Shionogi & Co. Ltd. has been using SAS/R/Spotfire for 15+ years and Matlab / Python for 5+ years.


Yuki Matsunaga

Yuki Matsunaga has worked as a Statistical Programmer, a Medical Scientific Expert, and a Medical Science Liaison for Novartis Pharma K.K. since April 2017. He works on new drug development and retrospective studies using medical real-world data such as electronic healthcare record and health claims data.


Tadashi Matsuno

Tadashi Matsuno has been working for SHIONOGI for 19 years. He had worked at Sales and Human Resources Recruitment departments. He is currently a member of the Data Science Department, handling tasks related to personal data protection and data science talent education.


Yasutaka Moriguchi

Yasutaka Moriguchi is a clinical programmer at GSK and also has over 20 years of experience in this field. He has been an active member of CDISC Japan User Group since 2012 and PhUSE Japan Open Source Technology WG since 2021.


Yutaka Morioka

Yutaka Morioka is a SAS programmer based in Japan. Besides my work as a clinical data scientist, actively engaging in disseminating various SAS programming techniques from introductory level to advanced. Having posted SAS programming blog entries since 2013 and experienced numerous times of presentations in seminars and SAS users meetings. Total 13 years of working experience in Japanese CRO industry analyzing the data of clinical studies. Additional 2 years experience as an analyst predicting election results and summarizing mass social surveys.


Ryo Nakaya

Ryo Nakaya has over 10 years of experience working at Takeda Pharmaceutical Company. He is responsible for statistical analysis in clinical trials, development of analysis data creation tools, global harmonization and standardization of statistical deliverables, and e-Data submission to PMDA as a statistician and statistical programmer.


Tomoyuki Namai

Tomoyuki Namai has over 15 years' experience as a statistical programmer. He is engaged in clinical reporting projects using not only SAS but also R.


Yumi Nishimoto

Yumi Nishimoto is a Statistical Programmer for clinical trials, drug application approvals, etc. at Chugai Pharmaceutical Co., Ltd, and has been using SAS/15+ years and Python for 2+ years.


Junko Urata

Junko Urata is a clinical programmer in medicine development at GSK and has 20 years' experience in this field. She has also been working on a global communication in implementing analysis systems/tools/processes in GSK.


Yui Yamaguchi

Yui Yamaguchi works as a system engineer at NTT DATA Japan. Utilizing the life science knowledge she learned at university, she is currently involved in a DX project in the field of drug discovery, aiming to introduce the document comprehension AI "LITRON".