SAS Training BASE SAS CONCEPTS BASE SAS:

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1 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

2 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

3 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

4 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

5 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 < P-values smaller than 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.

6 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. reporting. Implementation of standard clinical data solution best practices from CRF design through data analysis and 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 & Working with Charts Data Analysis & 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