Workshop on Statistical Software – SAS®

Hands-on SAS Base, Advanced, Graph & Statistics
10th, 11th & 12th Oct 2018 (Wednesday, Thursday & Friday) 9.00am - 5.30pm
708, NTU@One-North Executive Centre, 11 Slim Barracks Rise, Singapore 138664

About the workshop

This workshop will introduce participants to manage and analyse the data using SAS University Edition (Free Statistical Software). SAS is extensively used in clinical research and healthcare for data handling, data transformation for analysis ready datasets, statistical analysis and reporting. It is the current leading analytics software across academia and biomedical industry. Day 1 SAS Base will cover importing data files, defining variables and values, recoding variables, conditional data processing, combining datasets, working with SAS dates, and functions. Day 2 SAS advanced will be focused on writing SAS programs to enhance efficiency, automation of SAS programs for future use to perform similar tasks repeatedly and generating commonly used graphs in clinical research. We will cover arrays, do-loops, macros, SQL and graphs. Basic SAS Base knowledge is preferred but not mandatory. Day 3 SAS STAT will cover descriptive statistics, hypothesis testing, parametric and non-parametric tests, regression models, and survival analysis. Basic SAS Base and statistics knowledge is preferred but not mandatory.

Target Audience

Anyone who handles data (Data Managers, Research Associates, Biostatisticians, Analysts, Data Scientists etc.) and interested to use SAS software. Participants without prior programming experience are welcome.

Registration Details

Registration Fee Attending All 3 Days Attending Any 2 Days Attending Any 1 Day  Excluding GST $500 $300 $200 Limited seats available! Click here to register by Monday, 1st Oct 2018. Lunch and tea will be served. Please bring your own laptop. Guidance will be provided on installing SAS University Edition on first day of the workshop. Do visit our website at www.scri.edu.sg for more details or drop us an email (scriacademy@scri.edu.sg) if you have any queries.

Trainers

Rajesh Moorakonda Principal Statistical Analyst Wei Yuan Senior Biostatistician Ganesh Lekurwale Senior Statistical Analyst Joanna Ling Biostatistician

Workshop on Statistical Software - SAS®
Hands-on SAS Base, Advanced, Graph & Statistics
Workshop Outline
Day 1: SAS Base

• Introduction to the SAS University Edition and SAS Studio interface

• Importing your own data (such as CSV, Excel, SPSS, and STATA files) into SAS and exporting SAS data into other file formats

• Creating Labels for variables and Formats for data values

• Performing conditional processing (filtering) using IF and WHERE statements

• Handling data with dates in different formats

• Combining SAS datasets (merging, appending, restructuring)

• SAS Functions (TODAY, DATE, TIME, SUM, MEAN, COUNT, SUBSTRING, SCAN, FIND, TRIM etc.)

Day 2: SAS Advanced

• Conditional processing on multiple variables/columns across rows/observations for restructuring data - RETAIN, DO LOOP, and ARRAYS.

• PROC SQL: Use of Structured Query Language (SQL) to retrieve, update and summarize data.

• SQL clauses, subqueries, summary functions, table joins, Cartesian product, and advantages over DATA step

• MACRO language: Customized SAS program to perform common and similar repetitive tasks without having to re-write a new code. Create macro variables, macro components (compilation, execution, and symbol table), macro statements - conditional processing, debugging, macro functions (%SCAN, %SYSFUNC etc.)

• GRAPH: Scatter plot, Line plot, Bar chart, Pie chart, Histogram, and Boxplot

• ODS statements: Export statistical analysis output in to SAS dataset, word and PDF file.

Day 3: SAS STAT

• Descriptive statistics (mean, median, SD, skewness, percentile, frequency distribution, cross-tabulation, etc.)

• Correlation analysis (Pearson’s correlation, Spearman’s correlation)

• Continuous data analysis (one and two-sample t-test, paired t-test, one-way and two-way ANOVA, simple and multiple linear regression)

• Categorical data analysis (binomial test, Chi-square test, Fisher’s exact test, logistic regression)

• Survival data analysis (Kaplan Meier plot, Log rank test, Cox regression)

• Regression modelling (stepwise variable selection)