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Many programming tasks require merging time series of varying frequency. For instance you might have three datasets (YEAR, QTR, and MONTH) of data, each with eponymous frequency and sorted by common id and date variables. Producing a monthly file with the most recent quarterly and yearly data is a hierarchical last-observation-carried-forward (LOCF) task. Or you may have three irregular times series (ADMISSIONS, SERVICES, TESTRESULTS), in which you want to capture the latest data from each source at every date encountered (event-based LOCF). These are tasks often left poorly optimized by most SQL-based languages, in which row order is ignored in the interests of optimizing table manipulation.   This presentation shows how to use conditional SET statements in the SAS® DATA step to update specific portions of the program data vector (i.e. the YEAR variables or the QTR variables) to carry forward low frequency data to multiple subsequent high frequency records. A similar approach works just as well for carrying forward data from irregular time series. We’ll also show how to use “sentinel variables” as a means of controlling the maximum time-span data is carried forward, i.e. how to remove historical data that has become “stale.” Finally, we will demonstrate how to modify these techniques to carry future observations backward, without re-sorting data.   Presented by Mark Keintz Mark Keintz has been using SAS® since it was documented in one book. His interests are largely in development of applications for financial research and education, addressed in several presentations at SAS Global Forums and various regional SAS user groups. Mark's primary SAS expertise is in DATA step programming, hash programming techniques, efficient use of large data sets, and macro programming.   REGISTER for this virtual event today.
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Compared to other therapeutic studies, oncology studies are generally complex and difficult for programmers and statisticians. There is more to understand and to know such as different clinical study types, specific data collection points and analysis. In this seminar, programmers and statisticians will learn oncology specific knowledge in clinical studies and will understand a holistic view of oncology studies from data collection, CDISC datasets, and analysis. Programmers and statisticians will also find out what makes oncology studies unique and learn how to lead oncology study projects effectively. The seminar will cover four different sub types and their response criteria guidelines. The first sub type, Solid Tumor study, usually follows RECIST (Response Evaluation Criteria in Solid Tumor). The second sub type, Immunotherapy study, usually follows irRC (immune-related Response Criteria). The third sub type, Lymphoma study, usually follows Cheson. Lastly, Leukemia studies follow study specific guidelines (e.g., IWCLL for Chronic Lymphocytic Leukemia). The seminar will show how to use response criteria guidelines for data collections and response evaluation.   Programmers and statisticians will learn how to create SDTM tumor specific datasets (RS, TU, TR), what SDTM domains are used for certain data collection, and what Controlled Terminology (e.g., CR, PR, SD, PD, NE) will be applied. They will also learn how to create Time-to-Event ADaM datasets from SDTM domains and how to use ADaM datasets to derive efficacy analysis (e.g., OS, PFS, TTP, ORR, DFS) and Kaplan Meier Curves using SAS Procedures such as PROC LIFETEST and PHREG.   Finally, programmers and statistician will understand how to build end-to-end standards driven oncology studies from protocol, study sub-types, response criteria, data collection, SDTM, ADaM to analysis.   Presented by Kevin Lee.    REGISTER today.
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