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INTRODUCTION TO TRAINER

Syed Nemath Ullah Hussaini

Qualification:

MPHARM, P.G.D. BIOINFORMATICS,DIPLOMA IN CLINICAL RESEARCH AND CDM, CERTIFIED CLINICAL TRIALS PROGRAMMER, SAS PROFESSIONAL CERTIFICATION, STATISTICS WITH SAS,SQL WITH SAS, ADVANCE SAS PROGRAMMER,PYTHON ESSENTIAL TRAINING,BASICS OF ‘R’ PROGRAMMING,FUNDAMENTALS OF VISUALIZATION WITH TABLEAU.

Experience: More Than overall 15 years experience in Pharma,Teaching and Programming.
Occupation:

Presently Working as

Assistant Professor in MRM COLLEGE OF PHARMACY
Clinical SAS Programming Trainer in I.C.S.P

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Research: Generation of new knowledge , development of new and enabling technologies to identify or respond to major gaps in current knowledge.

Continuation of globalization.

Emerging New technologies.

Globalization of knowledge.

Increased mobility of the world's population.

Expansion of knowledge about disease problems.

ADVANCEMENTS IN RESEARCH

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CLINICAL RESEARCH

Studies conducted in people, designed to answer specific questions about the safety and/or effectiveness of New drugs, or New devices, New Surgical and Diagnostic procedures and other therapies.

Huge data is generated which can be difficult to manage and needs a software which can accurately carry on complex analysis on this data to generate significant,scientifically and statistically supported results.

The data generated is important and confidential to a clinical trial industry ,as the fate of the drug depends on it.

Clean and Standardised data submitted to regulatory authorities for product approval(FDA).

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IMPORTANCE OF CLINICAL RESEARCH

Recent Public health crisis such as coronavirus (COVID-19) pandemic which affected millions of people.

Needs research evidence and data to guide public health systems in making effective decisions in health emergencies.

Rise in the number of New diseases and disease causing agents.

Research in the field of health sciences picked up momentum recently in the 21(st) century.

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INTRODUCTION TO SAS

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.

Clinical Trials Studies.

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IMPORTANCE OF SAS

SAS is a software suite that can mine, alter, manage and retrieve data from a variety of sources and perform statistical analysis on it.

SAS provides a graphical point-and-click user interface for non-technical users and more through the SAS language.

SAS will not make you abandon data formats you previously mastered or managed.(such as Oracle, DB2).

SAS is versatile and powerful enough in data analyses.

SAS is flexible, with a variety of input and output formats.

It has numerous procedures for descriptive, inferential, and forecasting types of statistical analyses.

SAS helps healthcare professionals to meet business goals, control costs, generate greater revenue and enhance strategic performance management.

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ROLE OF SAS IN CLINICAL RESEARCH&BENEFITS OVER OTHER SOFTWARES

SAS is widely used in clinical data analysis in pharmaceutical, biotech and clinical research companies.

SAS programmers play an important role in clinical trial data analysis.

In addition to doctors and clinicians as a part of research team,other s include , group conducting data analysis such as statisticians, clinical data managers (CDMs) and SAS programmers.

SAS programmers analyze the collected data to generate summary tables, data listing and graphs for statisticians and or clinicians to write study report.

SAS also plays a role in protocol development, the randomization process, CRF designing, adverse event reporting etc.

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Programming — Data could be read from variety of sources, and report could be generated Variety of file formats such as HTM, rtf, excel, pdf, ppt, csv etc.

Programmer s with combined skills in the SAS and other standard programming language (PL/sql, COBOL, Assembler or FORTRAN) could accomplish the tasks more quickly with SAS .

SAS tools are used to explore clinical outcomes and risk tolerances to improve quality.

Data management system – SAS could be used to create, alter, update, maintain and secure database through SQL.

Other statistical software such as SPSS, can generate their results into SAS datasets for use in the SAS.

Research data is submitted to FDA in SAS Transport File format(.xpt)

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OVERVIEW OF CLINICAL SAS PROGRAMMING

Clinical Trial SAS programming (Clinical SAS):is a Research Oriented programming where

The Raw research Data is Accessed, explored, cleaned, Filtered, ,processed,Analyzed ,Transformed ,finally generating reports and exported in different formats(HTML,PDF, EXCEL,RTF,CSV,PPT,)

The final data is standardized as per CDISC Standards using SDTM, ADAM, DEFINE implementation guides.

The standardized Data submitted to the FDA for Approval of New Drugs.

The Analysis results are also converted in the form Tables as per SAP, Listings, and graphs.

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COURSE DETAILS

Clinical SAS Programming is basically divided into :

BASE AND ADVANCE SAS

CLINICAL RESEARCH & CDM (Clinical Data Management)

CDISC-SDTM ADAM & TLFS(Tables, Listings, Figures)/TLGS

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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.

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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).

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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.

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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).

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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.

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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.

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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

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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.

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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.

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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 FREQ,PROC MEANS,PROC FORMAT, PROC TRANSPOSE, ODS and PROC REPORT.

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SCOPE

There is a lot of scope of SAS programmers in different fields.

SAS programmers jobs such as Data Administrator, Data warehouse architect, Developer, Business Analyst, Financial Analyst, Marketing Analyst, Big data Analyst,Data Scientist, Statistical Programmer, Clinical data Programmer, Clinical Data Managers, Quality Analyst etc, Are available in the market.

SAS programmers plays an important role in clinical trial data analysis.

The opportunities for SAS programmers to address the technical needs in healthcare industry are ever expanding.

Clinical SAS provides a number of analytical tools used to explore drug result and risk tolerances to improve the quality of patient care.

A clinical SAS training will enable you to formulate ideas and methods for data analysis.
SAS play a major role in clinical trials, right from defining the clinical study to till regulatory submission.

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Clinical SAS training can help you bag some impressive jobs in healthcare industry.

The clinical SAS programming industry, has seen a rapid growth in this decade and the trend seems set to continue.

SAS professionals are finding employment opportunities above and beyond crunching numbers.

According to a recent study, SAS analytics skills are the most valuable skills to have in today’s job market.

In Multinational Companies like Novartis, Glaxo Smithkline, Pfizer, Roche there are a lot of scopes for clinical SAS programmers.

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