SAS® Health and Life Sciences Learning Subscription

Kód kurzu: SASHLSLS

Learn how to apply the SAS Life Sciences Analytics solutions to organize, standardize, and manage clinical research data, reduce costs, and improve quality of care.

SAS delivers innovative life sciences technologies that help get better, safer therapies to patients faster and improve health care analytics across the health care and pharmaceutical value chain. Learn how Life Sciences Analytics solutions from SAS with predictive analytics and embedded AI capabilities can help your system administrators, data analysts, data scientists, and clinical trial programmers enable the process of health care analytics.

certifikovaní lektori

uznávané certifikácie

Široká ponuka technických
a soft skills kurzov

Skvelý zákaznicky

Prispôsobenie kurzov
presne na mieru

Termíny kurzov

Počiatočný dátum: Na vyžiadanie

Forma: Self-Paced

Dĺžka kurzu: 365 dní

Jazyk: en

Cena bez DPH: 625 EUR


Forma Dĺžka
Jazyk Cena bez DPH
Na vyžiadanie Self-Paced 365 dní en 625 EUR Registrovať
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Popis kurzu

Learn how to:

  • Gain fundamental understanding of the analytical and data structures for operationalizing health and life sciences.
  • Learn to administer the analytical and data structures through authentication, authorization, and auditability mechanisms.
  • Use the SAS and R Program Editors to write and debug code, set up programs, and check job status.
  • Identify cohorts of interest, augment patient cohort data, and apply templated analytics to gain insights quickly.
  • Use the Episode Builder to analyze episodes of care to balance reducing costs and improving quality of care.
  • Establish causal inferences to determine whether the changes in outcomes are causally related to the program or medicine that you are evaluating.

SAS Products Covered

  • SAS Health Analytics Framework
  • SAS Viya
  • SAS Health: Cohort Builder
  • SAS Drug Development
  • SAS Life Science Analytics Framework
  • Base SAS

Štruktúra kurzu

Includes the following on-demand courses:

Episode Builder Features for SAS Health Analytics Framework 4.5

This course explores the features and functionality specific to Episode Builder, within SAS Health Analytics Framework 4.5, to balance the goals of cost reduction and quality improvement through analysis of episodes of care. By attending this course, students gain competence with the Health Analytics Framework building block products.

SAS Health Cohort Builder

This course explores the features and functionality specific to SAS Health: Cohort Builder in SAS Viya 4. Students gain competence in identifying patient cohorts of interest using simple or compound expressions, augmenting the patient cohort data with analysis variables, and leveraging templated analytics to gain knowledge and insights.

SAS Life Science Analytics Framework Essentials

This course is an introduction to SAS Life Science Analytics Framework 5.4. It is the prerequisite for the SAS(R) Drug Development for Programmers/Analysts course.

SAS Life Science Analytics Framework Programming

This course introduces the users to the programming environment in SAS Life Science Analytics Framework 5.4.

SAS Life Science Analytics Framework Administration

This course provides training for user administrators, project leads, and application administrators of SAS Life Science Analytics Framework 5.4.

Establishing Causal Inferences: Propensity Score Matching, Heckman’s Two-Stage Model, Interrupted Time Series, and Regression Discontinuity Models

This course introduces some methods commonly used in program evaluation and real-world effectiveness studies, including two-stage modeling, interrupted time-series, regression discontinuity, and propensity score matching. These methods help address questions such as: Which medicine is more effective in the real world? Did an advertising program have an impact on sales? More generally, are the changes in outcomes causally related to the program being run?

SAS Clinical Enrollment Simulator Overview

The SAS® Clinical Enrollment Simulation (CES) applies discrete event simulation (DES) analytical engine to model the process of clinical trial enrollment.  Users input the country, site and study parameters needed to simulate recruitment and financial performance. Intervention scenarios can also be evaluated which, after simulation, users can readily identify the best strategy to take to achieve recruitment milestones.