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SAS CLINICAL

SAS Clinical Online Training | SAS Clinical Training Course Details

The key difference between SAS and clinical SAS lies in their focus and features. Clinical SAS provides specific modules and tools for clinical trial data management, such as data validation, randomization, adverse event reporting, and data integration from various sources.

Pharmaceutical, biotech, and clinical research businesses frequently use clinical SAS programming services to analyze clinical trial data. Incredibly impressive outcomes have been obtained using clinical data management service providers in clinical research. SAS assists healthcare professionals in achieving their financial objectives, producing excellent income, enhancing strategic performance management, and controlling expenses. Regarding the welfare of the nation’s health, the healthcare sector ranks among India’s most significant industries.

Currently, the healthcare sector’s global data volume is 30%; by 2025, this percentage will be 36%. Even if you disagree, that is a 10% quicker rate of growth than financial services, 6% faster than manufacturing, and 11% faster than media & entertainment. The market is anticipated to reach 189 billion USD in 2025 as the number of individuals utilizing their mobile phones and personal gadgets for healthcare services increases dramatically. The prevalence of the internet, the accessibility of mobile devices and tablets, and the broad concern about health and wellbeing caused by the epidemic are the main factors for this market explosion.

How is Clinical SAS Programming Services Used?

Consider AstraZeneca overseeing each pill ‘s manufacture, provenance, location, destination, or pharmacy client. Imagine that Lufthansa manages the flight information and tickets for millions of clients daily. In the occurrence of a product recall, does Honda keep track of the inventories of all the vehicles it produces and sells globally, as well as the parts used in which models where? These are some instances of what SAS can be used for. SAS is a tool that many businesses employ, including AstraZeneca, Lufthansa, and Honda. In the twenty-first century, clinical SAS programming services are a crucial tool for business.

The Function of SAS Data Analytics in the Health Sector: SAS’s primary function in pharmaceutical analytics is to produce TLFs or TLDs. Since the FDA only accepts SAS reports, only Clinical SAS can generate reports using CDISC standards like SDTM and ADAM to standardize and evaluate clinical trial data. To write the SDTM and ADAM requirements and to create SAP, the department of BIO- Statistics uses clinical SAS (Statistical Analysis Plan). The CDM team uses SAS to analyze data before it is loaded into particular databases. It will be easier for the FDA to have a good picture of the clinical reports if the analysis is done with clean data and clinical trial data is validated. Therefore, most CROs choose clinical SAS as the ideal solution to utilize because of its effectiveness and safety compared to other software tools.

One of the most effective and well-liked tools for statistical modeling and data analysis is the clinical SAS programming services. The program is among the fastest and most reliable for advanced analytics, multivariate analyses, business intelligence, data mining, data management, report writing, statistical analysis, applications development, business modeling, and data warehousing. Furthermore, due to its dominant position in the market for jobs in advanced analytics, SAS is a benefit in various job marketplaces.

SAS Clinical Software Suite’s Main Advantages

SAS Clinical software offers real-time decision-making access to all pertinent clinical and nonclinical data. It provides a comprehensive view of the patient data and readmission patterns. A deeper comprehension of your clinical performance. Big data solutions & Analytics software for the healthcare sector that focus on your top priorities aid research efforts to improve patient outcomes. The major priorities in clinical trials that the SAS Clinical suite can extract include the following:

Chronic conditions

With statistical innovations, the clinical trial expert can identify many clinical and nonclinical elements that impact readmissions and prevent preventable readmissions optimally and affordably.

Analytics in Visual

The trial expert will be able to quickly and easily visualise the results and statistics thanks to comprehensive video analytics produced by the SAS Clinical software suite.

In Terms of Health

SAS Clinical also assists in analyzing sizable amounts of big data (structured and unstructured), including clinical and operational data. This aids in revealing various hidden insights on indications, patient status/concerns, and other problems that may impact patient treatment. Then convert that insight into knowledge based on fact so you can forecast and enhance outcomes.

Why not simply use spreadsheets like those found in Microsoft Excel or Google Sheets?

The distinction is twofold: First off, tools like SAS allow you to use much more data than Excel does. Imagine trying to retain all those records in an Excel file, using any of the usage scenarios above as examples. That is unrealistic, if not outright impossible! On the other hand, SAS offers the infrastructure necessary to maintain such massive amounts of data in an accessible and managed manner.

SAS’s function in the medical field:

In pharmaceutical and clinical research businesses, clinical SAS programming services are widely used to analyze clinical trial data. The analysis of clinical trial data requires the expertise of SAS programmers.

A SAS programmer responds to the technical requirements of the healthcare sector.

The use of information technology in healthcare delivery enables the extraction of useful knowledge from data, the guidance of decision-making, the enhancement of patient care quality, and the practice of medicine.

In the healthcare industry, SAS analytic solutions support revenue generation, cost management, and strategic performance monitoring business objectives.

SAS methods are utilised to examine clinical outcomes and risk tolerances to enhance the general standard of patient care.

Clinical research is approached personally at Element Technologies, a full-service, international contract research firm (CRO). We provide a level of collaboration and adaptability unattainable in a conventional CRO environment, focusing on individualized services and solutions, regulatory expertise, and therapeutic leadership. Our goal is to enhance clinical research in key therapeutic areas, such as oncology, rare diseases, gastroenterology, nephrology, and women’s health. Element Technologies are one of the top clinical data management service providers. They aim to impact the future of medication development and healthcare by combining the most cutting-edge technology with a CHALLENGE ACCEPTED strategy.

SAS Clinical Online Training Course Content

  • SAS BASICS
  • SAS software installation
  • Getting familiarity with SAS Import and Export Clinical Trials Data
  • Combine SAS data sets.
  • Efficiently import and subset SAS data sets.
  • Access data in an Excel workbook (LIBNAME and PROC IMPORT/EXPORT).
  • Create temporary and permanent SAS data sets.
  • Apply regulatory requirements to exported SAS data sets (SAS V5 requirements). Manage Clinical Trials Data
  • Investigate SAS data libraries using base SAS utility procedures (PRINT, CONTENTS, FREQ).
  • Access DICTIONARY Tables using the SQL procedure.
  • Sort observations in a SAS data set.
  • Create and modify variable attributes using options and statements in the DATA step. Transform Clinical Trials Data
  • Process data using DO LOOPS
  • Retain variables across observations.
  • Use assignment statements in the DATA step.
  • Use SAS functions to convert character data to numeric and vice versa.
  • Use SAS functions to manipulate character data, numeric data, and SAS date values.
  • Transpose SAS data sets. Macro Programming for Clinical Trials
  • Create and use user-defined and automatic macro variables.
  • Automate programs by defining and calling macros.
  • Use system options to debug macros and display values of macro variables in the SAS log (MPRINT, SYMBOLGEN, MLOGIC, MACROGEN).
  • Use PROC REPORT to produce tables and listings for clinical trials reports.
  • Use ODS and global statements to produce and augment clinical trials reports. Validate Clinical Trial Data Reporting
  • Explain the principles of programming validation in the clinical trial industry.
  • Utilize the log file to validate clinical trial data reporting.
  • Use programming techniques to validate clinical trial data reporting (PROC COMPARE).
  • Identify and Resolve data, syntax and logic errors. Apply Statistical Procedures for Clinical Trials
  • Use SAS procedures to obtain descriptive statistics for clinical trials data (FREQ, UNIVARIATE, MEANS, SUMMARY).
  • Use PROC FREQ to obtain p-values for categorical data (2x2 and NxP test for association).
  • Use PROC TTEST to obtain p-values for continuous data (one-sample, paired and two- sample t-tests).
  • Create output data sets from statistical procedures.
  • Apply 'observation carry forward' techniques to clinical trials data (LOCF, BOCF, WOCF).
  • Describe the clinical research process (phases, key roles, key organizations).
  • Interpret a Statistical Analysis Plan.
  • Derive programming requirements from an SAP and an annotated Case Report Form.
  • Describe regulatory requirements (principles of 21 CFR Part 11, International Conference on Harmonization, Good Clinical Practices). Clinical Trials Data Structures
  • Identify the classes of clinical trials data (demographic, lab, baseline, concomitant medication, etc.).
  • Identify key CDISC principals and terms.
  • Describe the structure and purpose of the CDISC SDTM data model.
  • Describe the structure and purpose of the CDISC ADaM data model.
  • Describe the structure and purpose of the CDISC ADaM data model.
  • Describe the contents and purpose of define.xml.
  • CDISC - SDTM
  • Introduction of CDISC
  • Why CDISC and DATA standards
  • What are the versions of CDISC
  • Impact of CDISC Standards on Clinical Activities
  • CDISC Models
  • Study Data Tabulation Model (SDTM)
  • Analysis Dataset Models (ADaM)
  • Fundamentals of SDTM
  • What is SDTM?
  • Observations and Variables in SDTM
  • Special Purpose Datasets
  • General Observation Classes in SDTM
  • SDTM Standard Domain Models
  • Creating New Domain
  • Submitting Data in Standard Format
  • Assumptions for Domain Models
  • General Assumptions for all Domains
  • Models for Special Purpose Domains
  • DM, CO, SE and SV
  • Domain Models Based on the General Observation Classes
  • 1. Interventions
  • CM, EX
  • 2. Events
  • AE, DS
  • 3. Findings
  • LB, EG, VS
  • 4. Trial Design Domains
  • TA, TE, TS, TI and TV
  • 5. REL REC
  • 6. Supplemental Qualifies
  • SDTM-supplementary domains Mapping Programming Using SAS
  • SDTM Annotation on CRF
  • SDTM Mapping Specifications
  • Real time Project on SDTM
  • Define.xml
  • Part 3: CDISC - ADaM:
  • Introduction to ADaM
  • Why ADaM
  • Key Concepts
  • ADaM naming conventions
  • ADaM Implementation
  • Fundamentals of the ADaM Standards
  • Variables in General
  • ADSL variables
  • BDS Variables
  • Real time Project on ADAM
  • ADSL
  • ADAE
  • ADLB
  • ADEX
  • Part 4: TLFs
  • Summary Reports (Tables Listings and Fig)
  • Introduction about the ICH E6,E9 and E3
  • Mock shells
  • Introduction about the statistical reports
  • Introduction about the clinical study report
  • SAS programs development, and validation (QC)
  • MeDRA Guidelines
  • Generating Summary Reports
  • Generating Listings
  • Generating Graphs
  • Real time Project
  • Interview preparation.
  • Assignments will be given based on ongoing topic.

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