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Often times SAS® programmers need to retain the value of a variable in the current observation to the next observation. The LAG function  can be very helpful here. A LAGn (n=1-100) function returns the value of the nth previous execution of the function. It is easy to assume that the LAGn functions return values of the nth previous observation. Using the LAG function to obtain information from previous observation(s) **********************************************************; /* Sample 1: Create a single lag of one variable */ data one; input x; lagonce= lag (x); datalines ; 1 2 3 4 5 ; proc print data=one; title 'Sample1: Single lag of one variable' ; run ; ***************************************************************; /* Sample 2: Create multiple lags of one variable */ data two; input x; lag1 = lag (x); lag2= lag2 (x); datalines ; 1 2 3 4 5 ; proc print data=two; title 'Sample 2: Multiple lags of one variable' ; run ; ...

IMPLEMENTATION OF CDISC STANDARDS Presented By Sandeep Raj Juneja, ASG Inc.... CDISC accomplishments and Strategy CDISC an d Standards for Clinical Research by Rebecca D.Kush, Ph.D, Founder & President,CDISC CDISC SDTM and related initativies CDISC submission standard : CDISC SDTM_Basics Supporting Th e CDISC Standards By Mark Lambrecht,PhD, Principal Consultant,Life Sciences,SAS Case Report Tabulation Data Definition Specification (define.xml) CDISC Study Data Tabulation Model SDTM Implementation Guide V3.1.1 http://www.cdisc.org/models/sdtm/v1.1/index.html Clinical Data Integration: SAS Clinical Data Integration By Dave Smith, SAS UK Industry Standards for the electronic submission of Data to the FDA by Michael A.Walega CDISC SDTM Basics

LEARN SAS within 7 weeks: Part1 LEARN SAS within 7 weeks: Part2 (Introduction to SAS – The Data Step) LEARN SAS within 7 weeks: Part3 (Introduction to SAS – SET, MERGE, and Multiple Operations) LEARN SAS within 7 weeks: Part4 (More on Manipulating Data) LEARN SAS within 7 weeks: Part5 (Procedures to Summarize Data) LEARN SAS within 7 weeks: Part6 (Producing Graphics and Using SAS Analyst)

1) What do you know about CDISC and its standards? CDISC stands for Clinical Data Interchange Standards Consortium and it is developed keeping in mind to bring great deal of efficiency in the entire drug development process. CDISC brings efficiency to the entire drug development process by improving the data quality and speed-up the whole drug development process and to do that CDISC developed a series of standards, which include Operation data Model (ODM), Study data Tabulation Model (SDTM) and the Analysis Data Model ADaM ). 2) Why people these days are more talking about CDSIC and what advantages it brings to the Pharmaceutical Industry? A) Generally speaking, Only about 30% of programming time is used to generate statistical results with SAS®, and the rest of programming time is used to familiarize data structure, check data accuracy, and tabulate/list raw data and statistical results into certain formats. This non-statistical programming time will be significantly reduced...

There are many reasons to integrate and to summarize all the data from a clinical trial program. Each clinical trial in the program is unique in its objective and design. Some are small safety studies among normal volunteers, while others are efficacy trials in a large patient population. The primary reason to create an integrated summary is to compare and to contrast all the various study results and to arrive at one consolidated review of the benefit/risk profile . A second and important reason is to reach a defensible statistical conclusion, through an exploration of the integrated data, that no competing alternative hypothesis that can reasonably account for the observed findings exists. Third, pooling the data from various studies enables the examination of trends in rare subgroups of patients, such as the elderly, those with differing disease states (mild vs. severe), and those with comorbidities at baseline. Last, providing such a summary in the new drug application is ...

Clinical trials: Clinical Trails Clinical Trials Terminology for SAS Programmers A Simple Solution for Managing the Validation of SAS Programs Electronic Clinical Data Capture Pharmaceutical Programming: From CRFs to Tables, Listings and Graphs SAS Programming in the Pharmaceutical Industry SASâ Programming Career Choices In The Health Care Industry Some Statistical Programming Considerations for e-Submission The Changing Nature of SAS Programming in the Pharmaceuticals Industry Managing Clinical Trials Data using SAS® Software Quality Control and Quality Assurance in Clinical Research: SAS CDISC: An Introduction to CDISC: CDISC: Why SAS® Programmers Need to Know CDISC Implementation Step by Step: A Real World Example CDISC standards Supporting the CDISC standards How to test CDISC Operation data Model (ODM) in SAS The Use of CDISC Standards in SAS from Data Capture to Reporting Clinical Data Model and FDA/CDISC Submissions Creating Case Report...

Fundamentals of Using SAS (part I) Introduction to SAS Descriptive information and statistics An overview of statistical tests in SAS Exploring data with graphics Fundamentals of Using SAS (part II) Using where with SAS procedures Missing values in SAS Common SAS options Overview of SAS syntax of SAS procedures Common error messages in SAS Reading Raw Data into SAS Inputting raw data into SAS Reading dates into SAS and using date variables Basic Data Management in SAS Creating and recoding variables Using SAS functions for making/recoding variables Subsetting variables and observations Labeling data, variables, and values Using PROC SORT and the BY statement Making and using permanent SAS data files (version 8) Data Management: How do I make unique anonymous ID variables for my data? How can I create an enumeration variable by groups? How can I see the number of missing values and patterns of missing values in my data file? How can I count the number of missing values for a character v...