CLINICAL SAS PROGRAMMING course details.pptx

A brief introduction of clinical sas programming

1. CLINICAL TRIAL SAS PROGRAMMING ( CLINICAL SAS)

2. INTRODUCTION Statistical Analysis Software(SAS) :is a Analytical Tool Developed by SAS institute( At North Carolina State University) for: Data Management Advance Analytics Multivariate Analysis Business Intelligence Criminal Investigation and Predictive Analysis.

3. CLINICAL SAS Clinical Trial SAS programming (Clinical SAS ):is a Research Oriented programming where the Raw research Data Collected during the clinical Trials is Accessed, explored, cleaned, Analyzed , Transformed and exported in different formats (PDF, EXCEL,RTF,CSV) the Analysis results are displayed in the form of Tables, Listings, Reports and graphs . the final data is standardized as per CDISC Standards using SDTM, ADAM, DEFINE implementation guides. The standardized Datasets are submitted to the FDA for Approval of New Drugs.

4. COURSE DETAILS Clinical SAS is basically divided into : BASE AND ADVANCE SAS CLINICAL RESEARCH & CDM (Clinical Data Management) CDISC-SDTM ADAM & TLFS (Tables, Listings, Figures) and GRAPHS

5. BASE SAS &ADVANCE SAS It includes the Basic SAS programming Concepts which includes: Creating New Data Sets using Data step. Accessing the data Through Libraries into SAS. Importing data into SAS environment using INFILE and PROC IMPORT. Exploring the Data using PROC PRINT, PROC FREQ, PROC MEANS and PROC UNIVARIATE . Filtering The data using WHERE Expression and Macro Variables.

6. Formatting ,Sorting the Data and Removing duplicate rows using Formats and PROC SORT . Computing New Columns using Numeric, Character and other functions. Conditional processing of Data using IF-THEN,IF-THEN-ELSE, DO , DO-WHILE, DO-UNTIL loops. Analyzing and Reporting the Data using PROC FREQ ,PROC MEANS and PROC REPORT . Exporting the results in differents Formats ( Excel,RTF,PDF,CSV )using ODS (Output Delivery System).

7. CLINICAL RESEARCH & CDM Clinical research  is a branch of healthcare science that determines the safety and efficacy of medications, devices, diagnostic products and treatment regimens intended for human use. Clinical Data Management(CDM):  is a critical process in  clinical  research, which leads to generation of high-quality, reliable, and statistically sound  data  from  clinical  trials. Clinical data management  ensures collection, integration and availability of  data  at appropriate quality and cost.

8. ADVANCE SAS MACROS: Defining Macros, Creating Macro Variables. Arimetic Calculation in Macros using eval and sysevalf . Using SAS procedures in Macros. Functions ,Conditional processing, Iterative loops in Macros. Creating Macros in data step by call symput . Use of macros in Titles and Footnote. Macro debugging options such as Symbolgen,mprint,mlogic .

9. SQL: Creating tables, alter and Update table using PROC SQL. Renaming and Labelling Columns. Filtering data using WHERE expression. Numeric, Aggregate and String functions in SQL. Creating macros in SQL. Grouping and Ordering data in SQL. Joining tables by SET OPERATORS or JOINS.

10. In Clinical Research We are going to study the following: Clinical Research process : Divided basically into Phases Pre-Clinical , Phase I, Phase II, Phase III, Phase IV . IRB/IEC, FDA and ICH . Sponsor Investigator & Investigator Brochure. Clinical Trial Design. CRO(Contract Research Organization). Clinical Trial Protocol Randomization. Case Report Form(CRF).

11. In CDM we are going to study: Electronic Data Capture. Creating Database, Database Validation ,Programming and Standards. Data Entry Process. Data Storage. Medical Coding Dictionaries(MEDDRA). Safety Data Management And Reporting Serious Adverse Events Data Reconciliation Database Closure or Database Lock.

12. TLFS AND GRAPHS Tables,Listings and Figures plays vital role in SAS programming to display the data in a readable format. To display the data in the form of Charts and Graphs which helps in Data Analysis. To display the data as per SAP(statistical Analysis Plan) document which is prepared to assist SAS programmers to  detail on the scope of planned analyses, population definitions, and methodology on how prospective decisions are to be made for presenting study results. TLF’s helps in submission of Final document to FDA And sponsors. Graphs are prepared using procedures such as PROC CHART, PROC PLOT, PROC GCHART, PROC GPLOT,PROC SGPLOT. Tables as per SAP are prepared using PROC SORT, PROC FORMAT, PROC TRANSPOSE , and PROC REPORT .

13. SDTM Study Data Tabulation Model Implementation Guide for Human Clinical Trials ( SDTMIG ) Prepared by the Submissions Data Standards ( SDS ) team of the Clinical Data Interchange Standards Consortium ( CDISC ). To guide the organization, structure, and format of standard clinical trial tabulation datasets submitted to a regulatory authority ( FDA ) . Standardized datasets helps To store all submitted data in a repository and 2. With the use of standard software tools, help to work with the data more effectively with less preparation time and better support viewing and analysis. 3. Facilitate data interchange between partners and providers. SDTM represents an interchange standard.

14. In SDTMIG We will Study: Introduction To SDTM. Fundamentals of the SDTM Submitting Data in Standard Format Assumptions for Domain Models Models for Special-Purpose Domains Domain Models Based on the General Observation Classes(interventions, Events and Findings) Trial Design Domains. Representing Relationships and Data

15. ADAM Analysis Data Model Implementation Guide ( ADaMIG ): prepared by the Analysis Data Model ( ADaM ) Team of CDISC . Both the SDTM and ADaM standards were designed for submission to a regulatory agency ( FDA ). It describes fundamental principles that apply to all analysis datasets. the design of ADaM datasets and associated metadata facilitate explicit communication of the content and source of the datasets supporting the statistical analyses . The Analysis Data Model supports efficient generation, replication, and review of analysis results.

16. The ADaMIG specifies : ADaM standard dataset structures ,variables and naming conventions. It also specifies standard solutions to implementation issues. In ADAMIG we are going to study: Introduction to ADAMIG. Fundamentals of the ADaM Standard. There are two ADaM standard data structures: 1. Subject-Level Analysis Dataset ( ADSL ) and 2. Basic Data Structure ( BDS ). Standard ADaM Variables: 1. ADSL variables and 2. BDS Variables Implementation Issues, Standard Solutions, and Examples.

17. SCOPE SAS programmers play an important role in clinical trial data analysis.  The opportunities for SAS programmers to address the technical needs of the healthcare industry are ever expanding. SAS can help meet healthcare professionals meet business goals, control costs, generate greater revenue and enhance strategic performance management. Clinical SAS provides a number of analytical tools that are used to explore drug result and risk tolerances to improve the quality of patient care. SAS play a major role in clinical trials, right from defining the clinical study to till regulatory submission. Multinational Companies like Novartis, Glaxo Smithkline , Pfizer, Roche Have a separate Division for Research and Development where there is a lot of scope for clinical SAS programmers.