Video: Let's Make Oncology Studies Easy for Programmers and Statisticians
The World Health Organization (WHO) predicts that new cancer cases will increase from 14 million in 2012 to 22 million in 2030 and cancer deaths will increase from 8.2 million to 13 million annually. In addition, oncology revenue from the pharmaceutical industry is expected to increase to $150 billion by 2020 from $107 billion in 2015. Almost all pharmaceutical companies are invested in bringing innovative oncology drugs to market, so the demand for oncology-experienced programmers and statisticians has also increased.
Compared to other therapeutic studies however, oncology studies are complex and difficult for programmers and statisticians. In particular, oncology studies require a specific way for data collection and analysis. In this webinar, we will take a look at how oncology studies differ from other therapeutic studies, and how oncology-specific data collections and analysis must be conducted.
Through this webinar, programmers and statisticians will learn the following:
- Three Oncology Sub-types - Solid Tumor, Lymphoma and Leukemia
- Response Criteria Guideline - RECIST for Solid tumor, Cheson for Lymphoma and IWCLL for Chronic Lymphocytic Leukemia
- Data Collections Based on Response Criteria
- CDISC SDTM Tumor Domains - TR, TU, RS
- Tumor Response - Complete Response, Partial Response, Stable Disease and Progression Disease
- CDISC Time to Event ADaM Datasets
- Events and Censoring
- Oncology-specific Analysis - Overall Survival, Progression Free Survival, Overall Response Rate
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