Ryuichi Katsuragi, Shionogi & Co., Ltd.
Shinichi Fukunaga, Shionogi & Co., Ltd.
Yoshitake Kitanishi, Shionogi & Co., Ltd.

In order to increase the sales of our own products, which is the most important issue in the sales and marketing field, it is necessary to make decisions on what kind of information should be provided to which clients and when to do so. Therefore, by using various systems and digital channels in the sales and marketing area to visualize sales activities and customer status, we provided information that would be input for decision making. This BI tool contains the sales results and trends of each customer, alerts implemented using a unique algorithm, the status of implementation of recommended activities, and the status of information disclosure to customers, which can be used to inform the planning of activities for clients. As a result, we saw changes in sales activities, such as an increase in the number of issues identified and an increase in activities recommended as effective. In this session, we will introduce the usefulness of data visualization in the field of Sales and Marketing, the know-how we have acquired, and also describes future perspectives.

Takashi Misawa, Japan Tobacco Inc..

RWD is expected to have a wide range of applications from preclinical to LCM. However, RWD in Japan has limitations in terms of age, traceability, etc. It is necessary to understand these limitations and select an appropriate RWD according to a research question. In addition, the large size of the data makes it difficult to understand the characteristics of RWDs. In this session, I would like to explain the characteristics of RWD in Japan and help you select an appropriate RWD. Furthermore, examples of visualization to understand the characteristics of RWD will be presented.

William Kuan, SAS Institute Japan Ltd.

Drug development life cycle includes the steps from strategy development, research and early development, clinical development, regulatory approval to market access and post-marketing monitoring. In addition to traditional clinical trial analyses, the business needs of advanced analytics capabilities (e.g., machine learning, natural language processing) are required with the growing attention on real-world evidence and decentralized clinical trials in pharmaceutical industry. Data visualization is an effective way to help decision making. However, the role of visualization is not only supportive of presenting the analysis results, but also helpful in the process of analyzing the data, especially for users with different level of analytical skills. We discuss here how visualization can support efficient workflows and data-driven decision across the life cycle of drug development using the end-to-end analytics platform SAS Viya and industry solution SAS Health.

Yutaka Morioka, EPS Holdings, Inc.

Raincloud plot is a recent visualization method for univariate data invented by Prof. Micah Allen at Aarhus University. Raincloud plot is a method that combines following three plots and help us to understand data.

1. Violin plot (Cloud): Distribution of data using kernel density estimation
2. Box-and-whisker plot (Umbrella): Quantile summary and outliers
3. Strip plot (Rain): Individual data values as they are

Raincloud plot has already been implemented in many programming languages such as Python and R, but there are very few examples of implementations in SAS. In this presentation, I propose implementation of various patterns of Raincloud plot and application to clinical trial works.

Masataka Higashijima, Eli Lilly Japan K.K.

Data Visualization is not just a tool for statisticians and programmers. Rather, it is a tool that facilitates access to data by non-statisticians such as clinical, safety, and medical writers, and through interactive communication to gain new insights from the data and make more effective team decisions. In recent years, there are discussions around the world about the various uses of Data Visualization in the pharmaceutical field, and its usefulness is beginning to be recognized. In this session, we will introduce examples of Data Visualization used at Eli Lilly and share how Data Visualization contributes to decision-making within the company.

Takayoshi Sasajima, Novartis Pharma K.K.
Eri Sakai, Novartis Pharma K.K.

Post-marketing surveillance (PMS) data is collected under clinical practice, and data cleaning is not enough compared to clinical trials. These data sometimes cause queries in late stage or even re-analysis after database lock. Therefore, it is important for PMS team members to check PMS data regularly and ensure its quality. We provided Spotfire to visualize PMS data interactively without programming skills. Through data-visualization by Spotfire, team members can detect irregular data which may impact on the analysis. In addition, they can also understand the current clinical practice through PMS data and obtain some insights from the visualized format. In this session, we will share how the data-visualization helps to increase the efficiency and effectiveness in our PMS project.

Shigeyuki Furuta, Chugai Pharmaceutical Co., Ltd.
Musashi Ishiguro, Chugai Pharmaceutical Co., Ltd.

BEAMS (Business Efficiency Acceleration for Medical Data Review with Spotfire) is a project in Chugai Pharmaceutical Co., Ltd. to introduce Medical Data Review tool used in F. Hoffmann-La Roche Ltd. into Chugai. The review using the tool enables medical data review from the same perspective as Roche and supports the PoC review and early license out of new medicines to Roche, which is one of Chugai’s business models.

The tool has approximately 40 sheets and 150 visualizations using Spotfire, contributing to reading trends in the time to onset of drug effect and its degree, detecting new safety signals and shortening the time for preparing materials for cohort transition meetings. Providing rich and useful Spotfire visualizations enables dynamic reviews and has been highly evaluated by users in terms of operability. Also, by embedding Python in the tool, 70% to 80% of the construction work could be automated when creating the visualizations.