SAS Training

BASE SAS CONCEPTS

BASE SAS:

Dataset concept and creating a dataset from internal data

Capturing data from external files (txt, CSV and tab)

Capturing Non-Standard data (date, time and amounts)

Data cleaning (Stages of data cleaning)

Different types of transformations

Data warehousing

Reporting the data

Creating Libraries

Different GLOBAL Options & DATASET Options

Conditional Statements and Operators

Backend Process of SAS (PDV)

Working with LOGICAL Variables

Working with DATASET Functions

Data Validation process

Use of ARRAYS and Different kinds of arrays

SAS/ACCESS

Importing data from XLS, TXT, CSV and MDB files using IMPORT Procedure

Exporting dataset to required locations using EXPORT Procedure

ETL Process

BASE SAS PROCEDURES:

Data modification using FORMAT procedure

Generating frequency table with Frequency procedure

Generating statistical analysis table with MEANS procedure

Difference between Summary and Means procedure

Reporting statistics with tabulate procedure

Generating reports with report procedure

Data reshaping by TRANSPOSE Procedure

Assigning ranks using RPOC RANK

Creating transport files by PROC Cimport and PROC Cport

Importance of Dbload Procedure

LIBNAME ACCESS METHOD:

Managing access files & excel files by the libname access method

Connect to different databases (Oracle, access etc )using libname access method

What are the difference between Libname access method and PTF Applications

OUTPUT DELIVERY SYSTEM (ODS):

Generating LIST & LOG files

Generating report in external file like hTML, RTF and PDF using ODS

Customization of reports using ODS options

SAS/STAT

Testing the data with Univariate Procedure

Comparison between T-test and non-parametric test

One way analysis of Variance with ANOVA procedure

What is PROC CORR

Regression analysis with PROC REG

SAS/GRAPH

Generating Charts with Chart Procedure

Different kind of charts (Horizontal/Vertical/Pie/Block Charts)

Use of Summarized datasets to develop charts

Working with PLOTS

Customization of graphs using GCHART PROCEDURE

To generate multiple PLOTS

ADVANCE SAS CONCEPTS

SQL Applications:

SQL concepts

Creating tables, Index and Views using SQL procedures

Working with OPERATORS & JOINS

Different kinds of Views

Working with SQL FUNCTIONS

Generating different kinds of reports

Working with duplicate data

Working with CONSTRAINTS

ETL process

What is the use of SUBQUERIES in PROC SQL

PASS THROUGH FACILITY (PTF):

Use of Pass Through Facility

Access data from different databases from the SAS

Communicating with other database like Access, Oracle, and DB2….

Controlling other database from the SAS

Implementation of PTF application

MACRO

How the SAS maro language works

Role of Macro in SAS

Introduction to tokenization, compilaing and execution of MACRO

Working process of macro processor

Macro applications

Macro concepts

Working with macro variables (GLOBAL & LOCAL)

Passing values to MACRO Parameters

Types of parameters

Macro QUOTING functions

Macro applications for

o APPENDING PROCESS

o MERGING PROCESS

o FREQ PROCEDURE

o MEANS PROCEDURE

o TABULATE PROCEDURE

o REPORT PROCEDURE

How to debug the Macros

Nested MACROs & examples

MACRO functions

Loop Process in macro

Interface functions

o Call symput

o Symget

o Symexist

o Symglobal

o Symlocal

o Call symdel

o Call execute

Auto call macros

Statistics Content:

Module 1 : HYPOTHESES, DATA, STRATIFICATION

· General considerations

· Two main hypotheses in drug trials: efficacy and safety

· Different types of data: continuous data

· Different types of data: proportions, percentages and contingency tables

· Different types of data: correlation coefficient

· Stratification issues

· Randomized versus historical controls

Module 2 : THE ANALYSIS OF EFFICACY DATA

· The principle of testing statistical significance

· The t-value = standardized mean result of study

· Unpaired t-test

· Null-hypothesis testing of 3 or more unpaired samples

· Three methods to test statistically a paired sample

· Null-hypothesis testing of 3 or more paired samples

· Paired data with a negative correlation

· Rank testing

· Rank testing for 3 or more samples

Module 3 : THE ANALYSIS OF SAFETY DATA

· Introduction, summary display

· Four methods to analyze two unpaired proportions

· Chi-square to analyze more than two unpaired proportions

· McNemar’s test for paired proportions

· Survival analysis

· Odds ratio method for analyzing two unpaired proportions

· Odds ratios for 1 group, two treatments

Module 4 : THE INTERPRETATION OF THE P-VALUES

· Introduction

· Renewed attention to the interpretation of the probability levels, otherwise called the p-values

· Standard interpretation of p-values

· Common misunderstandings of the p-values

· Renewed interpretations of p-values, little difference between p = 0.06 and p = 0.04

· The real meaning of very large p-values like p > 0.95

· P-values larger than 0.95

· The real meaning of very small p-values like p < 0.0001 1

· P-values smaller than 0.0001

Clinical:

Module 1 : Clinical Trials Introduction

Clinical Trial Phases Introduction

Clinicl Trial Terminology

Introduction to SAS in Clinical Data Management.

Clinical Data Management Process & Life cycle

Importance of CDISC SDTM in Clinical SAS

CDISC SDTM Introduction & Standards.

Clinical SAS Programmer Roles & Responsibilities

Overview of good clinical practice(GCP)

What is a protocol?

What is informed consent?

What is a placebo?

What is a control or control group?

What are the different types of clinical trials?

Different types of reports generated by programmer in clinical trials?

Module - 2

Importance of CDISC SDTM in Clinical SAS

SDTM Introduction & Standards.

Key SDTM concepts & Understanding the SDTM Standard

Application of SDTM Standards.

SDTM Mapping Specification

A detailed review of SDTM concepts.

SDTM domain models and relationship tables.

A discussion of common implementation issues.

Annotate CRFs in accordance with CDISC published or sponsor specific guidelines with appropriate metadata to

reflect case report tabulation (CRT) data.

Create case report tabulation (CRT) data set specifications per CDISC or sponsor specified requirements.

How to represent various types of collected data in the SDTM format.

Implementation of standard clinical data solution best practices from CRF design through data analysis and

reporting.

COMPONENTS OF SAS

Create TLG (Demographic, Safety and Efficacy)

SDTM data-sets

ADaM data-sets

SAS Banking: Complete SAS Basics, SQL, Macros and Statistics

Excel

The Basics

Formatting a Worksheet

Managing your workbooks

Editing a Workbook

Formulas

Working with the Forms Menu

Creating &amp; Working with Charts

Data Analysis &amp; Pivot Tables

Lookup table

Statistics with Excel

Data Ware Housing

Project:

Types of loans: Secured and Unsecured

Credit cards Analysis

Generating report: RCO, FDSF, AFPQR