Below is a collection of SAS® papers to help prepare for the SAS® Clinical Trials Programmer Certification Exam. See also New Clinical Programmer and SAS Certification Exam. Mind map. See CDISC.org scope and practice test and sample test CDISC interview questions. See SAS Base reference sheet. See SAS Syntax reference sheet.
|
Time
(24 hours)
|
Certification Exam Topic |
SAS® Savvy Reference |
|---|---|---|
| 0.5 hour | I. Clinical Trials Process |
New Clinical SAS® Programmer Clinical Data Management Regularory and Project Management |
| 6 hours | II. Clinical Trials Data Structures |
CDISC Strategies |
| 1 hours |
III. Import and Export Clinical Trials Data |
Proc Import, Proc Export |
| 2 hours |
IV. Manage Clinical Trials Data |
|
| 0.5 hour | V. Transform Clinical Trials Data |
SAS Functions/Formats |
| 2 hours |
VI. Apply Statistical Procedures for Clinical Trials |
Proc Means/Summary Continuous - ANCOVA and Survival analysis Categorical - Chi Square, McNemar's Test and Rank Test |
| 2 hours | VII. Macro Programming for Clinical Trials |
SAS Macro Programming |
| 4 hours |
VIII. Report Clinical Trials Results |
|
| 4 hours |
IX. Validate Clinical Trial Data Reporting |
|
| 4 hours | X. Other Topics |
I. Clinical Trials Process | |
| Describe the clinical research process (phases, key roles, key organizations) | |
| Interpret a Statistical Analysis Plan | Training Statistical Programmers on SAP Review Skills, Sascha Ahrweiler |
| Derive programming requirements from an SAP and an annotated Case Report Form | |
| Describe regulatory requirements (principles of 21 CFR Part 11, International Conference on Harmonization, Good Clinical Practices) | |
II. Clinical Trials Data Structures | |
| Identify the classes of clinical trials data (demographic, lab, baseline, concomitant medication, etc.) | |
| Identify key CDISC principals and terms | |
| Describe the structure and purpose of the CDISC SDTM data model | Implementation of the CDISC SDTM at the Duke Clinical Research Institute, Jack Shostak |
| Describe the structure and purpose of the CDISC ADaM data model | From SAP to ADaM: The Nuts and Bolts, Nancy Brucken, Paul Slagle |
| Describe the contents and purpose of define.xml | Creating a define.xml file for ADaM and SDTM, John H. Adams |
III. Import and Export Clinical Trials Data | |
| Combine SAS data sets | |
| Efficiently import and subset SAS data sets | |
| Access data in an Excel workbook (LIBNAME and PROC IMPORT/EXPORT) | |
| Create temporary and permanent SAS data sets | |
| Apply regulatory requirements to exported SAS data sets (SAS V5 requirements) | |
IV. Manage Clinical Trials Data | |
| Investigate SAS data libraries using base SAS utility procedures (PRINT, CONTENTS, FREQ) | |
| Access DICTIONARY Tables using the SQL procedure | |
| Sort observations in a SAS data set | |
| Create and modify variable attributes using options and statements in the DATA step | |
| Examine and explore clinical trials input data (find outliers, missing vs. zero values, etc) | |
V. Transform Clinical Trials Data | |
| Process data using DO LOOPS | How to Use ARRAYs and DO Loops: Do I DO OVER or Do I DO i?, Jennifer L Waller |
| Process data using SAS arrays | Arrays by example, Diana Suhr |
| Retain variables across observations | |
| Use assignment statements in the DATA step | |
| Apply categorization and windowing techniques to clinical trials data | |
| Use SAS functions to convert character data to numeric and vice versa | |
| Use SAS functions to manipulate character data, numeric data, and SAS date values | |
| Transpose SAS data sets | |
| Apply 'observation carry forward' techniques to clinical trials data (LOCF, BOCF, WOCF) | Statisical Analysis (impute) |
| Calculate 'change from baseline' results | |
| Obtain counts of events in clinical trials | |
VI. Apply Statistical Procedures for Clinical Trials | |
| Use SAS procedures to obtain descriptive statistics for clinical trials data (FREQ, UNIVARIATE, MEANS, SUMMARY) | |
| Use PROC FREQ to obtain p-values for categorical data (2x2 and NxP test for association) | |
| Use PROC TTEST to obtain p-values for continuous data (one-sample, paired and two-sample t-tests) | |
| Create output data sets from statistical procedures | |
VII. Macro Programming for Clinical Trials | |
| Create and use user-defined and automatic macro variables | |
| Automate programs by defining and calling macros | |
| Use system options to debug macros and display values of macro variables in the SAS log (MPRINT, SYMBOLGEN, MLOGIC, MACROGEN) | The Macro Debugging Primer, Frank DiIorio |
VIII. Report Clinical Trials Results | |
| Use PROC REPORT to produce tables and listings for clinical trials reports | Seven Steps to Regulatory Publication Style With Proc Report. Dennis Gianneschi |
| Use ODS and global statements to produce and augment clinical trials reports | |
IX. Validate Clinical Trial Data Reporting | |
| Explain the principles of programming validation in the clinical trial industry | The 5 Most Important Clinical SAS Programming Validation Steps, Brian C. Shilling |
| Utilize the log file to validate clinical trial data reporting | Errors, Warnings, and Notes (Oh My) A Practical Guide to Debugging SAS Programs, Susan J. Slaughter, Lora D. Delwiche |
| Use programming techniques to validate clinical trial data reporting (PROC COMPARE, MSGLEVEL) | “How Does Your Data Compare?”, SAS’s COMPARE PROCEDURE, Jenna Heyen |
| Identify and Resolve data, syntax and logic errors |